A Bierla

G Fromentin

J-M Martin Ltds

Evaluation of lubricant viscosity and base oil effects by form tapping test

In order to correlate the effect of lubricant viscosity and base oil on the efficiency of a lubricant, we measure the variation of tapping torque according to ASTM D5619 standard. Three fully formulated lubricants containing the same additive package but having different viscosities are tested in the first part of the experiment. The effect of chemically active and chemically non-active paraffinic base oils combined with a sulphur additive is studied in the second part. The nature of the tribofilm created at the bottom of the threads formed during the second part of the study is characterized by X-ray Photoelectron Spectroscopy (XPS). The aim is to associate the surface analysis results with the efficiency obtained during the form tapping tests. The results show that the lubricant viscosity does not have any influence on the tapping torque values whereas physicochemical interactions between different base oils and a sulphur additive can modify importantly the overall efficiency of lubricant.

Introduction

The tests of cutting fluids are used to improve the formulation of lubricants and consist in evaluating the behaviour of additives or new compounds. The technological development in machining has to be followed up by an evolution in the formulation of lubricants. The environmental aspect is not without importance. The ecological regulations for the lubricants become increasingly constraining. The reduction of certain toxic chemical substances is one of the reasons why cutting oil manufacturers are trying to develop new, more efficient but less toxic oils. The efficiency evaluation is realized typically using laboratory tests such as the four-ball test, or with Timken and Reichert machines. These tests are easy to carry out and do not require a lot of time and material. The contact between the surfaces and the parameters used for the tribological tests do not reproduce exactly the tool-chip-workpiece zone, though. These tests are still however the fastest way to study the antiwear or extreme-pressure properties of lubricants. During the seventies, a new approach for testing lubricants under machining conditions was developed. The first attempts of cutting fluids evaluation by machining tests showed that these results can be quite different from those obtained by the tribological tests. Several authors believe that the tests in real conditions of machining are the only one that make it possible to evaluate the lubricants in a relevant way [START_REF] Webb | Experience in field and laboratory evaluation of metal cutting fluids[END_REF][START_REF] De Chiffre | Investigations of cutting fluid performance using different machining operations[END_REF][START_REF] De Chiffre | Surface integrity and part accuracy in reaming and tapping stainless steel with new vegetable based cutting oils[END_REF][START_REF] Blanchard | Mechanical testing of cutting fluids[END_REF][START_REF] Lorenz | Reliable cutting fluid rating[END_REF]. The disadvantages of the machining tests are principally: important quantity of material used, long duration and high cost of tests.

Removed volume

Displaced volume

Formed thread Cut thread

Drilling diameter

These tests require the rigorous control of several machining parameters so that the true effect of the lubricant can be studied (problem of repeatability of the tests). This difficulty is often overcome by the preliminary series of tests with a fluid of reference to take into account, and estimate, the effects of others parameters. The aim of machining test evaluation is not to determine the properties of the lubricants but to establish which type of lubricants is the most appropriate for a given application and improve the quality of machining. The measurements of cutting forces and specific cutting energy become the main method of lubricant evaluation. These tests allow one to assess the lubricants in a fast and more reliable way compared to the tool life tests. This method is very advantageous because less expensive, requiring less material and characterized by a very good repeatability. It is thus understandable that this type of test is frequently used to evaluate lubricants in tapping [START_REF]Nord test method NT MECH 039[END_REF][START_REF]Standard Test Method for Comparing Metal Removal Fluids Using the Tapping Torque Test Machine[END_REF], drilling, reaming or boring. The importance of the fluid during the operation of tapping was proven through several studies [START_REF] Fromentin | Etude mécanique et technologique du taraudage par déformation : application aux aciers prétraités[END_REF]. In order to evaluate the efficiency of the lubricant, we measure the tapping torque variation according to standard ASTM D5619. The performance of the lubricant increases with the decrease of tapping torque values. The use of small quantities of oils makes it possible to test several lubricants. Form tapping is a method of manufacturing an internal thread which is different from cut tapping. The thread is obtained by displacement of the work material. There is no chip formation. Figure 1 presents the difference between the micrographs of cut and formed thread. The consecutive movement of lobes of a tap produces the thread. Each lobe provokes a three-dimensional plastic flow that leads to a strain hardening of the work material [START_REF] Fromentin | Precision and surface integrity of internal threads obtained by form tapping[END_REF]. The consolidation of the matter (increase close to 100% of initial hardness) has a crucial impact on the tool wear. The formed threads are distinguished from those obtained by cut tapping, in addition to the absence of chip, by an increased tool life, an increased reliability and better geometrical and mechanical characteristics of threads [START_REF] Fromentin | Etude mécanique et technologique du taraudage par déformation : application aux aciers prétraités[END_REF]. That's why this process acquires greater importance for several mechanical industries such as the automotive sector.

This study is divided in two separate parts. Firstly, we are using the tapping test to consider the effect of three lubricants, having different viscosities but the same additives, on the variation of tapping torque. Secondly, the base oil effect is examined using the tapping torque test as well. Two different paraffinic base oils (chemically active and chemically non-active) with and without a sulphur additive are evaluated. The cut parts of formed threads are then characterized using X-ray Photoelectron Spectroscopy, in order to correlate the efficiency of the lubricant with the tribochemical reactions that took place on the surface of the threads during the form tapping operation.

Experimental materials

The tested lubricants

Lubricant viscosity effect

The three lubricants used in this part of the study are commercially available metalworking fluids formulated for severe machining operations of ferrous and non ferrous materials. They consist of a paraffinic base oil mixed with the additive package. One lubricant differs from any other only by its viscosity. Table 1 shows the available information about the tested lubricants, their viscosities and their designations used for this study.

Table 1: Tested fluids and their designationslubricant viscosity effect

Base oil effect

Four different fluids are tested in this part of the experiment. We examine the effect of two different mineral base oils: chemically active (containing sulphur components) and chemically non-active. In addition, two other blends are prepared -the tested base oils are combined with a sulphur additive (with the same concentration in both cases) (see Table 2). Sulphur additives are known to play a major role in different severe machining operations; in this particular case, the use of sulphur will moreover allow us to highlight any interactions with sulphur components from the chemically active base oil. Hence the choice of a sulphur additive. Before starting the tests, all the pieces of machining equipment, including the force transducer with its protection case and the concentric installation, were carefully cleaned. After that, they were fixed on the table of the machine centre. The 20 mm-diameter cylinders previously machined on the CNC lathe were mounted in the concentric installation. Figure 2 shows the machining equipment used for tapping tests. The accurate hole diameter and concentricity have a considerable effect on the tapping torque value. A larger diameter means that there will be less matter to push back, therefore less energy for the tap to form thread, and consequently lower tapping torque and less remarkable difference between the tested lubricants.

Surface analysis of tapped specimens

This part of the experiment aims at associating the presence of lubricant elements on the surface to the performance obtained during the tapping test. The surfaces of threads formed with three different fluids (F4, F5 and F7) are observed with X-ray Photoelectron Spectroscopy (XPS). XPS analysis is carried out with an Al K α line excitation source. We are using a PHI Quantera SXM spectrometer. The residues of lubricants are ultrasonically removed from cut parts of formed threads in pure heptane just before their introduction into the analysis vacuum chamber. The analysis is made in to the pitch of the formed thread (thread root) because that is the zone where the most severe contact with the working tap occurs. Figure 3 presents the zone of analysis of the cut part of the formed thread.

Viscosity effect in form tapping

The objective of this test is to compare products of different viscosity but having the same additives. Figure 4a and 4b presents the results of the tapping test with the three lubricants presented in chapter 2.2.1. A new tap is used for every lubricant. The graph 4a shows the tapping torque evolution as a function of time. Form tapping is a process where the cutting section is constant. It means that the tapping torque varies little when all the active lobes of tap are entered into the material to form the thread. The average tapping torque is evaluated when the thread formation takes place. This period of time corresponds to the quasi-stationary state comprised for this test between 1.5 and 3 seconds. The average tapping torque for the three lubricants reaches the value of about 30 N.m. It can be assumed that the F2 lubricant acts differently at the moment of tap entry into the material (0.5 s of time) after which it achieves the same tapping torque value as the two others fluids. The graph 4b sums up the results and gives the average tapping torque values obtained for every fluid from three repetitions. There is little difference between the results obtained for the three lubricants. This experiment confirms that viscosity has no influence on the efficiency of lubrication in form tapping and that it is in fact the additives which will determine the fluid efficiency. The hypothesis that the lubricant viscosity does not have any influence in form tapping was already mentioned elsewhere [START_REF] Fromentin | Etude mécanique et technologique du taraudage par déformation : application aux aciers prétraités[END_REF]. As a result, the rest of the experiment will be concentrated on the physicochemical action of different base oils, combined or not with a sulphur additive, on the performance of a lubricant. An important part of surface analysis will follow the second part of the tapping tests.

Base oil effect in form tapping

The first section of the base oils tapping test aims at checking the variations between the taps used for this part of experiment. The dispersions can be due to the roughness and the microgeometry of the tools. We evaluate these differences in order to establish the correction coefficient of the taps. As a result of this coefficient, the tap effect on the latter results can be ignored.

Four new forming taps are employed (FT1 -FT4); each one is used to form four threads with the reference fluid (F4). All the taps have the same dimensions and belong to the same production batch. The first graph (see Figure 5a) shows the variation of tapping torque value for different taps. The second one (see Figure 5b) gives the average tapping torque value for every tap calculated from four tapped holes with the F4 reference fluid. The existing differences between the taps were evaluated to be of the order of 2%. The correction coefficient is calculated and applied for the second section of the experiment. The taps are carefully cleaned in heptane using an ultrasonic bath before the next use. The second section of this experiment consists in evaluating the performance of the four tested fluids (see 2.1.2. The tested lubricants -Base oil effect): F4, F5, F6 and F7. One tap is used to form five threads with the one and only tested lubricant. The evolution of tapping torque as a function of time (see Figure 6a) shows the differences between the tapping torque values for each fluid. The second graph summarises the results and presents the average tapping values, adjusted with the correction coefficient of the taps, for these lubricants (see Figure 6b). Among the tested fluids, F5 and F7 are the most efficient due to their lower average tapping torque value. The action of sulphur additive enables one to decrease the tapping torque by up to 21% in the case of F7 fluid compared to F6 and up to 11% in the case of F5 fluid compared to F4. The tapping torque value obtained with F7 fluid is comparable with the one obtained with F5, as a result we can deduce that base oil does not have any important effect on the final efficiency of the lubricant. It should be emphasised that the efficiency of the F4 fluid is higher than the one of F6, though. It is suggested from this study that the chemically active elements contained in the F4 fluid somehow compete with the sulphur additive for the surface and consequently prevent the additive present in F5 fluid from being as efficient as in the case of the F7 fluid. The formed threads with F4, F5, F7 fluids are cut for the purpose of surface analysis. XPS Spectroscopy is employed to show the chemical states of lubricant elements present on the surface. The binding energy of O1s at 529,8 eV can be attributed to the existence of different metallic oxides. The iron Fe 2p3/2 main peak at 710,5-711eV confirms the presence of oxides and probably of iron sulphides as well. There is possible interference of energy position between the peaks of these two products. Another O1s components at 531,4 eV and 532,8 eV can belong to O-C organic bonds and O=C, sulphates respectively. The sulphur peak S2p indicates the presence of two different components: sulphides and/or adsorbed sulphur species at 162,7-163,3 eV and sulphates at 168-168,6 eV. The decomposition of the sulphur peak detected on the thread surfaces obtained with the three tested fluids differs, though (see Figure 7). It can be noticed that on the surface of the threads obtained with: -F4 fluid, the sulphur peak is composed by a majority of sulphates (component towards 168,4 eV) before ion etching and of sulphides or/and adsorbed sulphur compounds at 163,24 eV after ion etching -F5 fluid, the sulphur peak contains two components: more intense at 168,35 eVsulphates and less intense at 163,17 eVsulphides or/and adsorbed sulphur compounds before ion etching and the same components of similar intensity at 167,93 eV and 162,67 eV after ion etching respectively -F7 fluid, the sulphur peak contains two components of similar intensity at 167,93 eVsulphates and at 163,17 eV -sulphides or/and adsorbed sulphur compounds before ion etching and the same components of similar intensity at 168,05 eV and 162,67 eV after ion etching respectively Table 3 indicates the different proportion of sulphur components for these three lubricants. The chemical shifts of sulphur and proportions between two sulphur peak components after ion etching for different fluids will be the object of the latter discussion. Table 4 shows the atomic percentage of major chemical elements detected on the surfaces of formed threads with the three tested fluids before and after ion etching.

It can be reported that the surface of the thread formed with F4 fluid was etched sufficiently to remove the carbon layer and to reach the oxide layer (increased iron and oxygen atomic concentration). It suggests also that the tribofilms created with two other fluids (F5 and F7) can contain carbon.

Table 3: Different proportions of sulphur components on the sulphur spectra of formed threads with F4, F5 and F7 tested fluids before and after ion etching Table 4: Atomic percentage of chemical elements detected on the surfaces of formed threads with F4, F5 and F7 tested fluids before and after ion etching

Discussion

The surfaces of formed threads with F5 and F7 fluids are mainly composed of sulphates, sulphides or/and adsorbed sulphur compounds and iron oxides. We can deduce that the efficiency of lubrication brought about by these two lubricants is due to the sulphides probably to the iron sulphides. It is impossible however to specify from our data the exact chemical form of these species.

The protective film of sulphides as a reaction product of sulphur compounds with a metallic surface is well known for very good anti-seizure and anti-welding behaviour in extreme conditions such as machining (high temperature, pressure). The sulphides can also be easily sheared. They may lower the friction coefficient between the surfaces, which could explain the advantageous effect on the forces during the form tapping operation [START_REF] Barros | Friction reduction by metal sulfides in boundary lubrication studied by XPS and XANES analyses[END_REF][START_REF] Ning | Effect of sulphide layers on the tribological behaviour of steels under boundary lubrication conditions[END_REF]. The tribofilm on the surfaces of threads formed with F4 fluid seems to be composed of sulphides or/and adsorbed sulphur compounds on which the sulphates layer is deposited. The position of sulphur peak at 163,24 eV detected on the surfaces of formed threads with F4 fluid after ion etching could suggest the presence of another sulphide compounds instead of those detected on the surfaces of threads formed with F5 and F7 fluids (sulphur peak at 162,67 eV after ion etching) and/or the presence of adsorbed sulphur species. This is the possible explanation of the different efficiency of these three tested fluids. The friction behaviour of iron sulphides, for example of FeS and FeS 2 compounds, can be very different [START_REF] Da-Ming | Microstructure and tribological properties of sulphide coating produced by ion sulphuration[END_REF]. This observation is made referring to the chemical shift of sulphur peak before and after ion etching but it should be taken with caution because the ion etching can modify the oxidation number of sulphur atoms (reduction).

It is difficult to determine the role and the process of sulphate formation for the three tested fluids (sulphur additive decomposition product or/and sulphides oxidation product). It is inferred that sulphur elements coming from chemically active paraffinic base oil can compete with the sulphur additive on the surface. The same sulphur additive is twice more efficient in the chemically non-active paraffinic base oil. The efficiency of the two fluids containing the sulphur additive (F5 and F7 fluid) is comparable.

Conclusions

(1) The major role of fluid in form tapping is to lubricate by the physicochemical way. The viscosity does not have any effect on lubricant efficiency.

(2) The two most efficient lubricants form sulphides or/and adsorbed sulphur compounds, sulphates and iron oxides on the thread surfaces. The presence of sulphides is probably the reason for their good efficiency.

(3) The chemically active and chemically non-active paraffinic base oils combined with a sulphur additive provide similar lubricant efficiency. There is a possible competition between the sulphur elements coming from base oil and the sulphur additive.

(4) This study highlights the fact that the cost of fully formulated oil can be reduced by the use of a less elaborated base oil. The efficiency of a fully formulated oil depends on the activity of its additives.

Sulphides or/and adsorbed sulphur compounds

Sulphates

Figure 1 :

 1 Figure 1: Micrographs of cut and formed thread

Table 2 : 2 . 2

 222 s at 40°C -Paraffinic base oil + additive package F2 23 mm 2 /s at 40°C -Paraffinic base oil + additive package F3 46 mm 2 /s at 40°C -Paraffinic base oil + additive package Tested fluids and their designations -base oil effect The tapping test materials and experimental procedureThe tapping tests are carried out on a DMC65V (Deckel Maho Gildmeister) 3 axes vertical machining centre equipped with a 840D Siemens CNC. The forces generated during the form tapping operation are measured with a 9173 Kistler force transducer. This device records the force Fz and the torque Mz. The direction Z corresponds to the axis of the tapped specimen and the spindle of the machining centre. The use of tapping torque to compare the performance of different fluids is the normalised method ASTM D5619 standard test[START_REF]Standard Test Method for Comparing Metal Removal Fluids Using the Tapping Torque Test Machine[END_REF]. The system of acquisition includes a charge amplifier and a PC provided with a data acquisition card and Dasylab acquisition software. The fabrication of workpieces turned on a CNC lathe in C70 carbon steel is prior to the form tapping tests. We are using the M12x1.5 6HX taps for reasons of sensor capacity and to provide a typical example of industrial practice. They are made of high speed steel (HSS) enriched with cobalt and TiN coated. The taps and material used during the tests belong to the same production batches. The tools are cleaned with heptane in an ultrasonic bath.

  That is why the drilled and bored holes adapted to the dimensions of threads M12*1.5 are produced directly on the machining centre previous to the tapping test. The operations of drilling and boring are made with sulphurized water-based emulsion (concentration 5%). The diameters of bored holes are rigorously checked before each tapping operation. The holes are cleaned and then filled with test fluid. The tapping torque developed during each test is monitored and recorded.

Figure 2 :

 2 Figure 2: The set up for the machining operation

Figure 3 :

 3 Figure 3: Analysed zone of the cut part of the formed thread Afterwards, the thread surfaces are slightly etched with Ar + in order to remove the adventitious carbon (etching thickness 1,6 nm). The ion etching conditions are: 1 kV, 2 x 2 mm², 1.6 nm/min (we refer to SiO 2 etching).

  PHOTO

Figure 4a :

 4a Figure 4a: Tapping torque evolution as a function of time for F1, F2, F3 lubricants having different viscosities

Figure 5a :Figure 5b :

 5a5b Figure 5a: Tapping torque evolution as a function of time for four new taps (FT1-FT4) with the reference fluid (F4)

Figure 6a : 3 . 3

 6a33 Figure 6a: Tapping torque evolution as a function of time for four (F4-F6) tested fluids (base oil effect)

Figure 7 :

 7 Figure 7: Chemical shift of sulphur peaks on the surfaces of formed threads with F4, F5 and F7 tested fluids

Acknowledgements

The authors would like to express their appreciation to the TOTAL France Company for contribution to this study.


Member, IEEE Hyoung Il Son
email: hyoungil.son@tuebingen.mpg.de.

Member, IEEE Antonio Franchi
email: antonio.franchi@tuebingen.mpg.de.

Lewis L Chuang
email: lewis.chuang@tuebingen.mpg.de.

Junsuk Kim

Member, IEEE Heinrich H Bülthoff

Member, IEEE Paolo Robuffo Giordano

P Robuffo

Human-Centered Design and Evaluation of Haptic Cueing for Teleoperation of Multiple Mobile Robots

Keywords: Bilateral Teleoperation, Maneuverability, Multi-Robot Systems, Perception, Psychophysics. I

whether they are published or not. The documents may come

L'archive ouverte

an increasing development in the bilateral teleoperation algorithms for the efficient and robust control of multiple mobile robots (e.g., see [START_REF] Rodriguez-Seda | Bilateral Teleoperation of Multiple Mobile Agents: Coordinated Motion and Collision Avoidance[END_REF], [START_REF] Lee | Passive decomposition and control of nonholonomic mechanical systems[END_REF], [START_REF] Lee | Semi-autonomous haptic teleoperation of multiple unmanned aerial vehicles over the internet[END_REF], [START_REF] Franchi | A passivity-based decentralized approach for the bilateral teleoperation of a group of UAVs with switching topology[END_REF]). Nonetheless, comparatively less attention has been devoted towards understanding how teleoperation performance can be improved with regards to the human operator. Given this level of interest in multiplemobile-robot teleoperation, we believe that it is worthwhile to investigate how human control performance can be assessed within this context.

The overall performance of bilateral teleoperation systems relies on the human operator's command. Therefore, it seems advisable to take human control effort into consideration when defining performance metrics for our teleoperation system. For example, maneuverability performance would be considerably enhanced if the same tracking performance could be achieved with less control effort. This perspective seems especially reasonable in the case of a multi-robot system which can typically require more control effort for realizing an effective maneuvering when compared to the single-robot case, due to their more complex dynamics and interactions.

Similarly, a sensitive perception of the remote environment is often considered to be important for effective bilateral teleoperation systems. In the case of remote mobile robots (e.g., teleoperation of UAVs), visual feedback can often be limited by the on-board camera restrictions (e.g., restricted field-of-view, poor resolution) [START_REF] Mccarley | Human factors implications of UAVs in the national airspace[END_REF], [START_REF] Diolaiti | Teleoperation of a mobile robot through haptic feedback[END_REF]. This could severely limit teleoperational success and result in undesired collisions with remote obstacles. Providing haptic cues could alleviate this limitation. Unlike conventional teleoperation [START_REF] Hashtrudi-Zaad | Analysis of control architectures for teleoperation systems with impedance/admittance master and slave manipulators[END_REF] (e.g., telesurgery), however, hard contact between flying UAVs and their physical environment is especially undesirable because they could cause severe damages to the mechanical structure of the robots. Therefore, there are no actual physical forces to be transmitted to the multi-UAV teleoperator and the conventional objectives of force tracking are not applicable. For this reason, pure force tracking may not be a measurable concept that can be directly generalized to the teleoperation of mobile robots. In this sense, transparency [START_REF] Lawrence | Stability and transparency in bilateral teleoperation[END_REF] is also not a suitable measure for the perceptual ability in the teleoperation of remote UAVs.

To sum up, evaluating human performance in the bilateral teleoperation of multi-UAVs should address the control effort required in maneuvering the mobile robots as well as the perceptual sensitivity of the operator of the remote environment. The contribution of providing haptic cues should be assessed accordingly.

A. Previous Work

Frequency response analysis using transfer functions is a general approach in analyzing performance in the domain of bilateral teleoperation (e.g., transparency in [START_REF] Fite | Loop shaping for transparency and stability robustness in bilateral telemanipulation[END_REF], and position and force tracking in [START_REF] Aliaga | Experimental quantitative comparison of different control architectures for master-slave teleoperation[END_REF]). Likewise, the relationship between the teleoperator's control effort and maneuvering accuracy can be analyzed in terms of a frequency response function for the evaluation of maneuverability performance. Maneuverability has been proposed to be an important measure of the effectiveness of master-slave systems [START_REF] Yokokohji | Bilateral control of master-slave manipulators for ideal kinesthetic coupling-formulation and experiment[END_REF]. However, this work specifically focused on the transmission of position and force errors between the master and slave devices and not on actual human performance per se.

Some researchers have explicitly studied human performance in teleoperation systems. Chen et al. [START_REF] Chen | Human performance issues and user interface design for teleoperated robots[END_REF] provide excellent surveys on teleoperation systems in the context of human performance issues and user interface designs. Notably, maneuverability of a master haptic device has been studied in [START_REF] Ito | Maneuverability of master control devices considering the musculo-skeletal model of an operator[END_REF]. Here, the authors proposed a measure for the maneuverability of the master by considering the musculo-skeletal model of operator. Recently, Nambi et al. also studied the human controllability on the master device to see an effect of velocity, admittance gain and force on the performance using admittance-type devices [START_REF] Nambi | On the Ability of Humans to Apply Controlled Forces to Admittance-Type Devices[END_REF].

Some research studies have addressed how human perceptual sensitivity can be enhanced in the conventional teleoperation [START_REF] Sherman | Design of bilateral teleoperation controllers for haptic exploration and telemanipulation of soft environments[END_REF], [START_REF] De Gersem | Reliable and enhanced stiffness perception in soft-tissue telemanipulation[END_REF], [START_REF] Malysz | Nonlinear and filtered force/position mappings in bilateral teleoperation with application to enhanced stiffness discrimination[END_REF], [START_REF] Botturi | Perception-centric force scaling in bilateral teleoperation[END_REF], [START_REF] Son | Enhancement in operator's perception of soft tissues and its experimental validation for scaled teleoperation systems[END_REF]. Among these, fidelity has been proposed as an objective based on the claim that the information about the relative changes of environment impedance is more important for interactions with soft tissues than the environment impedance alone, see [START_REF] Sherman | Design of bilateral teleoperation controllers for haptic exploration and telemanipulation of soft environments[END_REF]. Taking inspiration from [START_REF] Sherman | Design of bilateral teleoperation controllers for haptic exploration and telemanipulation of soft environments[END_REF], Germsem et al. provided empirical evidence that a relative change in the transmitted stiffness to the operator could be increased by tuning the control parameters of a teleoperation controller [START_REF] De Gersem | Reliable and enhanced stiffness perception in soft-tissue telemanipulation[END_REF]. Modified position/force scaling approaches were developed between the master and the slave, using a nonlinear filtered scaling and a time-varying scaling in [START_REF] Malysz | Nonlinear and filtered force/position mappings in bilateral teleoperation with application to enhanced stiffness discrimination[END_REF] and [START_REF] Botturi | Perception-centric force scaling in bilateral teleoperation[END_REF] respectively. Son et al. proposed in [START_REF] Son | Enhancement in operator's perception of soft tissues and its experimental validation for scaled teleoperation systems[END_REF] a quantitative index for the perception based on psychophysics and then showed that the operator's detection and discrimination could be enhanced by a perception-index-maximizedcontroller via psychophysical experiments.

In a recent study, the effectiveness of various artificial forces for collision avoidance was evaluated in the teleoperation of a single UAV [START_REF] Lam | Artificial force field for haptic feedback in UAV teleoperation[END_REF]. An ecological interface paradigm was presented in [START_REF] Nielsen | Ecological interfaces for improving mobile robot teleoperation[END_REF], which provides multi-visual information into a 3D mixed-reality display in mobile robot teleoperation. A series of principles for presenting the information to improve an operator's situation awareness of a remote robot were presented in [START_REF] Nielsen | Ecological interfaces for improving mobile robot teleoperation[END_REF]. In addition, the effects of various factors (e.g., time delay, force-reflecting method, image-display alternatives, etc.) on obstacle avoidance performance of mobile robot teleoperation were studied through subjective tests using several measures [START_REF] Janabi-Sharifi | Experimental analysis of mobilerobot teleoperation via shared impedance control[END_REF]. A fuzzy force-reflection method was also proposed and compared against a conventional proportionalderivative (PD) type force-reflection method. There were more fundamental studies to understand human's neuromuscular system in a car driving with haptic feedback. Venrooij et al. proposed a method to measure biodynamic feedthrough and admittance of human operator simultaneously in a motion simulator [START_REF] Venrooij | A method to measure the relationship between biodynamic feedthrough and neuromuscular admittance[END_REF]. A similar study was presented in [START_REF] Abbink | Measuring neuromuscular control dynamics during car following with continuous haptic feedback[END_REF] to determine a driver's biomechanical properties of the anklefoot complex during car following task.

Although it is evident that, recently, a number of researchers focus their attention on human perspective studies in mobile robot teleoperation, still, few works have addressed the case of teleoperation of multiple mobile robots.

B. Objective and Outline

The aim of the present paper is to build upon the control framework introduced in [START_REF] Lee | Semi-autonomous haptic teleoperation of multiple unmanned aerial vehicles over the internet[END_REF] by the authors and to discuss how information from the remote UAV group (slave-side) can be appropriately transformed into haptic cues for the human operator via the master device. Subsequently, we will present and discuss a systematic evaluation of the contribution of the proposed cues to teleoperation performance from the human operator perspective via psychophysical studies. To this end, we first introduce a performance index of the teleoperation system of remote UAVs, maneuverability, in terms of how easily the operator can control the movement of the slave system accurately. We then discuss a second performance index, perceptual sensitivity, aimed at measuring how well the operator is able to perceive the current state of the UAVs and their surrounding obstacles.

The structure of this paper is as follows. In Section II the concepts of maneuverability and operator perceptual sensitivity are introduced together with suitable metrics that take human performance into account. Following this, Section III reviews the control architecture for the bilateral teleoperation of multiple UAVs in [START_REF] Lee | Semi-autonomous haptic teleoperation of multiple unmanned aerial vehicles over the internet[END_REF]. Three haptic feedback algorithms are then introduced within the context of this framework. After presenting our experimental method in Section IV, these haptic feedback algorithms are subsequently evaluated, using our proposed metrics, to determine the algorithm that achieves the best level of maneuverability performance and operator sensitivity in Section V and VI, respectively, followed by discussions in Section VII. This paper ends with our conclusions and recommendations for future work.

II. PERFORMANCE MEASURES

A. Maneuverability 1) Position tracking of slave robot:

In conventional teleoperation research, the position tracking ability of the slave is typically evaluated by estimating the discrepancy between the positions of the slave and the master. In the current work, we are concerned with the discrepancy between the position of the slave and the target position on the reference path that the teleoperator intends to maneuver to. Therefore, position tracking error is defined as

e p (t) := x(t) -x ref (t) (1)
for N slave robots, wherein This position tracking error can be quantitatively evaluated across the entire operation time using cross-correlation (CC). This allows to objectively compare the similarity of the actual path (x) and the desired path (x ref ) of the slave robots. In the current study, we define this cross-correlation as in (2) to assess the position tracking error across the overall time of maneuvering (T ).

x(t) = 1 N N i=1 x i (t), x i (t) ∈ R 3 represents the position of i th
CC position = T 0 x(t) • x ref (t)dt T 0 x2 (t)dt T 0 x ref 2 (t)dt (2)
2) Maneuverability performance: In addition to the conventional approach, maneuverability performance can (and should) take into account the control effort of the operator. From the operator's perspective, it is desirable to achieve equivalent maneuvering accuracy with less control effort. However, the position tracking metric (i.e., CC position ) does not consider this fact. From this perspective, maneuverability is defined in terms of the ease of the operator in maneuvering the slave robot for achieving accurate tracking performance.

To assess this effect, we propose to consider the frequency response profile of the human operator, that is, the dynamical relationship between the operator's intended-force (f h ∈ R 3 ) as the input, and the position tracking accuracy (CC position ) as the output

Φ maneuv (s) := CC position (s) f h (s) . (3)
In this regard, maneuverability increases when f h is reduced and when position tracking CC position is increased (i.e., the position tracking error is small). From this, two additional performance metrics can be derived for maneuverability. First, the ±3 dB bandwidth of Φ maneuv , denoted as ω bd . Second, the H 2 norm of Φ maneuv , denoted as Φ maneuv 2 and defined in (4)

Φ maneuv 2 = W low CC position f h 2 (4)
where W low is a low-pass weighting function with a cut-off frequency ω c . The selection of ω c depends on the specific application of bilateral teleoperation systems. As the normal tremor of a human hand occurs at 8 ∼ 12 Hz [START_REF] Burdea | Force and touch feedback for virtual reality[END_REF], ω c can be less than 8 Hz. The bandwidth and H 2 norm are useful properties of the system. Respectively, they denote how well the system will track an input and the degree of sensitivity of system output with respect to its input. Concerning our current evaluation, large values of ω bd and Φ maneuv 2 both indicate high maneuverability performance.

B. Perceptual Sensitivity 1) Force tracking of master device:

The force tracking of the master haptic device is measured in terms of how accurately it can track the forces that are transmitted from slave robot(s). This is, generally, evaluated by estimating the discrepancy between the force of the master and the slave. The force tracking error is defined as

e f (t) := f m (t) -f ref (t) (5)
where f m (t) ∈ R 3 represents the force input of the master device and f ref (t) ∈ R 3 is the reference force, at time t.

With conventional teleoperation, f ref would be replaced by the force exerted on the slaves fs = 1 N N i=1 f s,i (t), f s,i (t) ∈ R 3 represents the force on i th slave, by being in contact with its immediate environment.

2) Perceptual sensitivity of human operator: Unfortunately, force tracking accuracy is not a sensible measure with regards to the teleoperation of mobile robots (or vehicles, in particular UAVs). As mentioned earlier, UAVs should ideally avoid any direct contact with their environment.

Given that haptic feedback cues should also serve to make human operators sensitive to the remote environment, which is inhabited by the controlled UAVs, it is reasonable to consider sensitivity in terms of their ability to perceptually discriminate between physical differences in the remote environment. For this, it is useful to consider the smallest change in the magnitude of a physical variable (e.g., position, force, and impedance) that can be effectively perceived. Such a measure is commonly referred to as the just noticeable difference (JND) [START_REF] Gescheider | Psychophysics: the fundamentals[END_REF] In this paper, JND is estimated from a psychometric function, which is the probabilistic distribution of a human observer's perceptual response across a chosen physical variable (i.e., distance to an obstacle). For this, the JND is defined as

JN D := ∆f ref f ref , (6)
which represents the minimum difference in the reference force that can be effectively perceived by the human operator via force feedback. From our perspective, the JND is more suitable than force tracking accuracy (see [START_REF] Lee | Passive decomposition and control of nonholonomic mechanical systems[END_REF]) in understanding how human operators can benefit from haptic force feedback in perceiving the remote environment of the multi-UAVs that they control.

III. BILATERAL TELEOPERATION OF MULTIPLE UAVS

A. Teleoperation Control Architecture

In [START_REF] Lee | Semi-autonomous haptic teleoperation of multiple unmanned aerial vehicles over the internet[END_REF] we developed a novel control framework which has three control layers: i) UAV control layer, where each UAV is controlled to follow the trajectory of an abstract kinematic virtual point (VP); ii) VP control layer, which modulates each VP's motion according to the teleoperation commands and local artificial potentials; and iii) teleoperation layer, through which a remote human user can command the VPs' velocity using a haptic device while haptically perceiving the state of the UAVs over the Internet. Hereafter, we briefly review the control architecture and refer the reader to [START_REF] Lee | Semi-autonomous haptic teleoperation of multiple unmanned aerial vehicles over the internet[END_REF] for further details.

On the master side (see Fig. 1) we consider a 3 degree-offreedoms (DOFs) haptic device modeled by M (q)q + C(q, q) q = τ + f h [START_REF] Franchi | A passivity-based decentralized approach for the bilateral teleoperation of a group of UAVs with switching topology[END_REF] where q ∈ R 3 is a configuration of the device (e.g., the position of its end effector), M (q) ∈ R 3×3 is the positivedefinite/symmetric inertia matrix, C(q, q) ∈ R 3×3 is the Coriolis matrix, and τ , f h ∈ R 3 are the control input and human forces, respectively. The slave side consists of a group of N UAVs, and we denote with

x i ∈ R 3 the position of a representative point of the i-th UAV, i = 1, . . . , N . Define p i : t ∈ R → p i (t) ∈ R 3
as the desired trajectory to be followed by x i . We assume that, by adopting a suitable trajectory tracking controller, any smooth reference trajectory p i ∈ C ∞ (VP) can be tracked by the representative point x i with a small error p ix i . This assumption holds, for example, if x i is a flat output [START_REF] Fliess | Flatness and defect of nonlinear systems: Introductory theory and examples[END_REF] for the considered UAV, i.e, if it algebraically defines, with its derivatives, the state and the control inputs of the i-th UAV. It is well known that both helicopters and quadrotors satisfy this property [START_REF] Nieuwstadt | Real-time trajectory generation for differentially flat systems[END_REF], [START_REF] Mistler | Exact linearization and noninteracting control of a 4 rotors helicopter via dynamic feedback[END_REF] 1 .

We denote with N i the set of UAVs which interact with the i-th UAV (commonly denoted as neighbors in the multi-robot literature). The reference trajectory p i is generated online by the kinematic evolution:

ṗi = u c i + u o i + u t i p i (0) = x i (0). (8)
where the meaning of the three velocity terms

u c i , u o i , u t i ∈ R 3 is here briefly explained.
The contribution of u c i ∈ R 3 is aimed at achieving a certain 3D shape formation specified by the desired distances d c ij , ∀i = 1, . . . , N, ∀j ∈ N i as well as avoiding a collision among UAVs. In particular u c i ∈ R 3 creates an attractive action if p ip j is large, a repulsive action if p ip j is small, and a null action if p ip j = d c ij . Because of the presence of two vertical asymptotes in u c i , corresponding to the minimum and the maximum allowed distances, interrobot collisions are guaranteed to be prevented and inter-robot connectivity to be preserved (see [START_REF] Lee | Semi-autonomous haptic teleoperation of multiple unmanned aerial vehicles over the internet[END_REF] for a formal proof). For any obstacle point p o in the environment, the term u o i ∈ R 3 implements a repulsive action if p ip o is small and a null action for p ip o > D o where D o ∈ R + represents a certain distance threshold. Finally, u t i ∈ R 3 represents a desired velocity term directly controlled by the human operator by means of the position of the master device q

u t i = λq ∀i (9)
where λ ∈ R + is a constant scale factor used to match different scales between q and the UAVs desired velocity u t i . The desired shape is exactly achieved only if the sum u o i + u t i is the same ∀i = 1, . . . , N (e.g., if u o i + u t i = 0 ∀i), otherwise the resulting group shape is a deformed version of the desired one. In this way the group can automatically adapt to the size of environment (e.g., by shrinking when the operator pushes it in narrow hallways).

B. Haptic Feedback Algorithms

We take as a starting point, three classes of haptic cues that are typically considered as force feedback sources in conventional teleoperation systems: i) the mismatch between the command of the master and the execution of the slave in terms of position qx s or velocity qẋs (i.e., the masterslave tracking error); ii) the force measured by a force-sensor mounted on the slave in contact with the environment f s ; and iii) a linear combination of i) and ii) [START_REF] Hashtrudi-Zaad | Analysis of control architectures for teleoperation systems with impedance/admittance master and slave manipulators[END_REF]. In the frequency domain, these three cases can be summarized by the following expression for the control input force:

τ = -C p (q -x s ) -K f f s ( 10
)
where C p = K p + B p s is the PD transfer function of the position controller and K f is the force controller transfer function, being K p , B p , and K f diagonal gain matrices [START_REF] Hashtrudi-Zaad | Analysis of control architectures for teleoperation systems with impedance/admittance master and slave manipulators[END_REF]. Case i) corresponds to the situation where K f = 0 and is commonly referred to as position-position (PP) control or position-error feedback; case ii) is consistent with the situation where K p = B p = 0 and is typically referred to as forceposition (FP) control or direct force feedback; finally, case iii), where all the gain matrices are non-zero, is usually denoted as position/force-position (PFP) control. Because of practical reasons such as space limitation, safety, and cost, we did not consider the classes of haptic cues that are based on the force measured on the master side, e.g., position-force (PF) and a four channel (4C) control [START_REF] Hashtrudi-Zaad | Analysis of control architectures for teleoperation systems with impedance/admittance master and slave manipulators[END_REF], [START_REF] Lawrence | Stability and transparency in bilateral teleoperation[END_REF].

The design of the force feedback control τ in (7) originates from these three classes of haptic cues (i.e., PP, FP, and PFP). However, three main adjustments have to be taken into account when transferring these concepts from conventional teleoperation to our case. First, we have several slaves instead of only one, hence the haptic cue in our case must depend on the average of all the UAV contributions. Second, the masterslave tracking error defined above cannot be directly applied given that the desired velocity for the group is proportional to q rather than q. Therefore, we define the master-slave tracking error in our case as q -1 λN N i=1 ẋi . Third, there is no real contact force acting on the slave side, therefore we replace f s with the (scaled) average of the velocity terms due to obstacle avoidance 1 λN N i=1 u o i , which plays the role of a repulsing force for the desired trajectory. Note that while the tracking error is directly influenced by the real UAV states (thanks to the presence of x i ), the obstacle force is only a function of the desired trajectory, in particular of u o i . With these considerations in mind we define our force feedback control as

τ = -B q -Ky (11)
where B, K ≻ 0 are diagonal gain matrices, -B q represents a damping term whose role is to stabilize the haptic device, and y ∈ R 3 represents a haptic cue informative of the environment surrounding the UAVs and of the UAV motion states. In direct relation with the aforementioned PP, FP, and PFP classes, we consider the following choices for the signal y: 1) Force-cue feedback (Force):

y = y F := 1 λN N i=1 u o i . ( 12
)
This haptic cue is the counterpart of the FP class and plays the role of a repulsive force from the environment w.r.t. the current position of the reference trajectory for the UAV (environmental force). It is only related to the difference between the positions where the UAVs are supposed to go and the location of obstacles. Therefore it is not related to the actual positions of the UAVs or to any real contact of the UAVs with their environment.

2) Velocity-cue feedback (Velocity):

y = y V := q - 1 λN N i=1 ẋi . ( 13
)
This haptic cue corresponds to the PP class and represents the mismatch between the commanded velocity (specified by q in (9)) and the average velocity of real UAVs (tracking-error).

The mismatch can be caused by the following reasons: i) inaccurate model of the UAV, imprecise parameter calibration (e.g., mass of the UAV), or poor quality of the trajectory tracker, ii) external disturbances on the UAV motion (e.g., wind, friction, actual contact with the environment), iii) nonzero velocity terms u o i + u t i , i = 1, . . . , N in the trajectory generator [START_REF] Mccarley | Human factors implications of UAVs in the national airspace[END_REF], due to the presence of obstacles closer than D o to the group. Therefore, this haptic cue provides the operator more information compared to the previous case. Note that the velocity-cue also includes the Force cue, albeit 'filtered' by the dynamical behavior of the real UAV positions/velocities rather than directly taken from the desired trajectory.

The third and last case is the counterpart of the PFP class and linearly combines the previous two cues:

3) Velocity plus force-cue feedback (Velocity+Force):

y = y V + y F . (14)
We end the Section with some final considerations. If the UAVs are maneuvered close to the obstacles, both y V and y F are non-zero. In fact, some of the u o i , i = 1, . . . , N will be non-zero, implying that: i) y F = 0 by definition and ii) the reference trajectory (and consequently the UAVs) are not following exactly the operator commands represented by the term u t i = λq. If the velocity term due to the obstacles and the other two terms in [START_REF] Mccarley | Human factors implications of UAVs in the national airspace[END_REF] are exactly balancing (they sum up to zero) and consequently the UAV are stationary, then y F ≃ y V . Finally, if the UAV are moving far away from the obstacles in the environment then y F = 0 while y V is in general non-zero because of all the aforementioned reasons (external disturbances, inaccurate calibration, poor trajectory tracker, contact with an unperceived object, etc). Experimental setup. Subject with haptic device (Omega 3) and Graphical User Interfaces (GUIs).

IV. GENERAL EXPERIMENTAL METHODS

A. Participants

Thirty-two participants (25 males; age range: 20-33 years) from Korea University, Seoul were paid approximately $20 USD to take part in this study, which consisted of four experiments on maneuverability and perceptual sensitivity for low and large control gains. Five participants took part in all four experiments while the rest took part in at least one experiment. There were eighteen participants for each experiment. All participants possessed normal or corrected-tonormal eyesight and no physical disability. The experiments were conducted in accordance with the requirement of the Helsinki Declaration.

B. Apparatus

The apparatus mainly consisted of a central display and a haptic device. The former presented a virtual environment for a swarm of four UAVs whose flight path could be controlled by the latter (see Fig. 2).

This multi-UAV swarm always assumed a tetrahedron formation in this virtual environment, with an inter-UAV distance of approximately 0.8 m. The UAVs dynamics and control logic were simulated in a custom-made simulation environment that was based on the Ogre3D engine (for 3D rendering and computational geometry computations), with PhysX libraries to simulate the physical interaction between the UAVs and their virtual environment. This simulation was updated at 60 Hz which, in turn, constrained the data exchange rate between the haptic device and virtual UAVs. In the display, the simulated UAVs and environment were rendered from a camera perspective that was 32 m (and 8 m) away from the starting positions of the UAVs for the maneuverability (and the perception experiment respectively), with a FOV of about 21 • . This rendered scene was presented via the center display monitor.

The UAV swarm was controlled by a commercial haptic device (Omega 3, Force Dimension). The Omega 3 is a 3-DOFs haptic device with 3 translational actuated axes and a local control loop running at about 2.5kHz on a dedicated linux machine. In addition, ATI six-axis force/torque sensors, Nano17, were attached to the Omega 3 device to measure the force that the human operator exerted on the device during experimentation, as shown in Fig. 2.

Finally, instructions on manipulating the haptic device and the experimental procedure were presented on the left and right monitors, respectively.

C. Data Analysis

Three measures served as performance metrics for maneuverability in our evaluation study, according to our description in Sec. II-A. To recap, CC position (%) in (2) represents the similarity of the executed path to the desired path. ω bd and Φ maneuv 2 represents the bandwidth and H 2 norm of the maneuverability frequency response (Φ maneuv in (3)). These measures were separately computed for the x-axis and y-axis components of the executed path. In fact, the dynamics of the quad-rotors along y-axis are faster than the x-axis for mechanical reasons. Thus, there were six performance metrics for maneuverability. In our study, Φ maneuv was measured as the experimental frequency response of the haptic teleoperation system of multi-UAVs shown in Fig. 2. This considered as input the human operational force to the haptic device (f h ), calculated as

f h (t) = f m (t) -τ (t) (15)
where f m is the force that was measured from the force sensor on the haptic device and τ is the master control force given by the current haptic feedback algorithm. The output is CC position treated in percentage (see [START_REF] Jones | Principled approaches to the design of multi-robot systems[END_REF]). With both variables, it was possible to calculate Φ maneuv by applying the empirical transfer function estimator (ETFE [START_REF] Ljung | On the estimation of transfer functions[END_REF]), using a second-order low-pass filter (W low ) with cut-off frequency of 8 Hz (see ( 4)). Psychometric functions were fitted, in the perception experiment, to the collected data to assess each participant for their discrimination sensitivity, given a particular haptic cue. They were derived using the psignifit toolbox for Matlab which implements the maximum-likelihood method described in [START_REF] Wichmann | The psychometric function: I. fitting, sampling and goodness-of-fit[END_REF]. Discrimination sensitivity was measured as the difference of stiffness values between the 16th and 84th percentile of the psychometric function. This value was referred to as the JND and denotes the difference in stimulus intensity that participants were able to reliably detect over one standard deviation around the point of subject equality2 (PSE). A smaller value indicates greater sensitivity in discrimination; that is, better environmental awareness.

In the maneuverability and perception experiment, all performance metrics were submitted to a one-way repeatedmeasures analysis of variance (ANOVA) for the factor of haptic feedback algorithms (Velocity, Force, and Velocity+Force cues) 3 . An alpha level of 0.05 was taken to indicate statistical significance. When a main effect was found, post-hoc t-tests 4were conducted among the haptic feedback algorithm conditions to identify those which were significantly different from each other in terms of the relevant performance metrics.

D. Selection of Control Parameters

The magnitude of the force feedback is expected to be strongly influenced by the control parameters (gains) K and B which were defined for our force feedback control in [START_REF] Lawrence | Stability and transparency in bilateral teleoperation[END_REF] with the haptic cueings in (12) (Force cue), (13) (Velocity cue), and (14) (Velocity+Force cue). Here, for a fully fair comparison of the performance of the proposed haptic feedback algorithms the control parameters were tuned to produce a same magnitude of force to the operator.

The multi-UAVs, in a tuning procedure, were maneuvered from a starting position to an ending position located in front of an obstacle as shown in Fig. 3. The UAVs, then, were stopped at the ending position over 5 secs. During this operation f m was measured using the force sensor mounted on the haptic device.

Details of this tuning procedure are reported in the following: first, we fixed the gains as K = 50 N/m and B = 2 Ns/m for the Force cue. Then, keeping this condition as reference, the gains were tuned for the cases of the Velocity and the Velocity+Force cues to produce the same force as in the Force cue case. This tuning procedure was performed in four cases depending on the position of the obstacles relative to the multi-UAVs, i.e., obstacles located on the right side, left side, upper side, and down side (see Fig. 3). The values of the tuned Hereafter, this parameter set will be referred to as low gain. Note that the damping parameter B was not included in the tuning to maintain the same motion agility of the UAVs and the same stability performance based on the passive-set-position-modulation (PSPM) algorithm [START_REF] Lee | Passive-set-position-modulation framework for interactive robotic systems[END_REF].

Experimental results using the tuned control parameters are shown in Fig. 4. The plots are averages of repeated five experiments. The first row of Fig. 4 shows the trajectories of the center position of the multi-UAVs. The UAVs were operated along the same path with all haptic feedback algorithms. The master control force τ is shown in the second row. As expected the magnitude of τ at steady-state keeps similar across the various conditions despite small difference due to transient phases. We can also verify that the operator received very similar forces for all haptic feedback algorithms from the measured force f m shown in the third row of Fig. 4.

Another parameter set was also tested to analyze an effect of control parameters on the system performance. This is defined as K = 70 N/m and B = 2 Ns/m for all Velocity, Force, and Velocity+Force cues and, hereafter, will be referred to as large gain.

V. EXPERIMENT 1: EVALUATION OF MANEUVERABILITY

The purpose of this experiment was to evaluate how maneuverability performance varied in response to the proposed haptic cue algorithms, in accordance to the proposed performance measures.

A. Procedure

In this experiment, participants were required to maneuver a swarm of UAVs, using the haptic control device. The objective was always to follow a moving target. This moving target preceded the UAV swarm and moved along a preassigned path, which was either straight or curved (see Fig. 5 and [START_REF] Son | Measuring an operator's maneuverability performance in the haptic teleopertion of multiple robots[END_REF] for details). Prior to experimentation, the participants were given a detailed tutorial about the experiment and were provided a training session with the Omega for about 10 mins to get them familiarized with the haptic device and the procedure.

The full experiment was separated into three blocks of 25 trials each. Each block of trials was defined by the design of the haptic cue that provided feedback to the participant; namely, Velocity, Force, and Velocity+Force cues.

On each trial, participants were presented with one of three possible scenarios for UAVs maneuvering; 5, 10 and 10 trials for Scenario 1 (a straight path with no bounding obstacles), for Scenario 2 (a straight path that was bounded by four obstacles) and for Scenario 3 (a curved path which was bounded by four obstacles), respectively. These scenarios were featured in all of the blocks and were fully randomized within the block for their presentation order. It should be noted that the obstacles as well as the reference path were invisible during the experiment to minimize any influence of visual feedback. Only the UAVs and moving targets were visible during the experiment. Finally, the presentation order of the blocks was fully counter-balanced across the participants, to minimize the influence of practice and order effects on our findings.

B. Results

Figs. 6-8 are summaries of performance in this maneuvering experiment. Performance on the three different scenarios were independently analyzed, for low and large values of gain. The results are reported in the following sub-sections.

1) Scenario 1-straight path with no obstacles: With low gain values, the haptic cue conditions did not significantly differ on any of the performance measures for maneuverability. On the other hand, with large gain values, the Force cue condition yielded significantly larger Φ maneuv 2 values compared to the Velocity+Force condition [START_REF] Son | Measuring an operator's maneuverability performance in the haptic teleopertion of multiple robots[END_REF].

Direct comparisons between the low and large gain conditions of each haptic cue indicated the following. There was no significant difference on any performance measure for the Velocity cue. For the Force cue, larger gains significantly increased the values of Φ maneuv 2 for both the horizontal and vertical components (respectively, t 34 = 2.55, p < 0.05; t 34 = 2.23, p < 0.05). In the case of the Velocity+Force cue larger gains significantly decreased the value of ω bd (t 34 = 2.42, p < 0.05).

2) Scenario 2-straight path with obstacles: As reported in [START_REF] Son | Measuring an operator's maneuverability performance in the haptic teleopertion of multiple robots[END_REF], with large gain, the Force cue yielded significantly higher performance compared to Velocity and Velocity+Force cues for both the horizontal and vertical components. With low gain values, however, the haptic cue conditions significantly differed on the measure of Φ maneuv 2 , for the horizontal component of flight (F 2,34 = 24.3, p < 0.001), but not on the other measures. Specifically, the Force cue yielded significantly higher Φ maneuv 2 values, relative to both Velocity and Velocity+Force cues.

Direct comparisons between the low and large gain conditions of each haptic cue indicated the following. There was no significant difference on any performance measure for the Velocity cue. Increasing the gain for the Force cue significantly increased the values of Φ maneuv 2 for both the horizontal and vertical components (respectively, t 34 = 2.73, p < 0.05; t 34 = 2.35, p < 0.05). In addition, doing so significantly decreased the ω bd on the vertical component (t 34 = 3.68, p < 0.01). Last, increasing the gain for the Velocity+Force cue significantly decreased the ω bd on the vertical component (t 34 = 2.22, p < 0.05). 3) Scenario 3-curved path with obstacles: With low gain values, the haptic cue conditions significantly differed on the measure of Φ maneuv 2 , for both the horizontal and vertical component of flight (respectively, F 2,34 = 6.38, p < 0.01; F 2,34 = 27.46, p < 0.001), but not on the other measures. In both instances, the Force cue yielded significantly higher values compared to both Velocity and Velocity+Force cues. With large gain values, the haptic cue conditions significantly differed on the measures of CC position and Φ maneuv 2 . With regards to CC position , the Velocity cue yielded significantly higher values than Velocity+Force cue. However, with regards to Φ maneuv 2 , the Force cue yielded higher values than both Velocity and Velocity+Force cues.

Direct comparisons between the low and large gain conditions of each haptic cue indicated the following. For the Velocity cue, larger gains induced significantly larger ω bd on the horizontal component (t 34 = 2.43, p < 0.05) and smaller ω bd on the vertical component (t 34 = 2.23, p < 0.05). Also, it resulted in significantly larger Φ maneuv 2 in the vertical component (t 34 = 2.00, p < 0.05). For the Force cue, larger gains induced significantly larger Φ maneuv 2 in the vertical component (t 34 = 3.73, p < 0.001). Last, increasing the gain for the Velocity+Force cue induced significantly reduced ω bd on the vertical component (t 34 = 2.19, p < 0.05).

C. Discussion

In general, the Force cue algorithm for haptic feedback consistently yields better maneuverability performance than the Velocity algorithm as well as the Velocity+Force algorithm. This is demonstrated for the measure of Φ maneuv 2 across the three tested scenarios. The results show that the operator required less force to achieve the same level of maneuvering accuracy when the Force cue feedback was implemented. Broadly speaking, this advantage of using the Force cue feedback is more pronounced with the larger values of gain and when obstacles were present. Therefore, Force cue feedback afford greater sensitivity in the transfer from the input force to tracking accuracy. In other words, less control effort is required on the part of the operator in achieving the same level of tracking accuracy with Force cues. With regards to tracking accuracy itself, the measure of CC position was less sensitive as a performance measure of maneuvering accuracy. It only revealed distinctions between the haptic feedback algorithms with large gain values and only in the presence of obstacles. When significant differences were revealed, the Velocity cue feedback proved to be significantly better than the Velocity+Force cue feedback, but not the Force cue feedback.

Finally, varying gain values tended to influence the bandwidth (ω bd ) of the frequency response profile. Larger gain values typically corresponded with lower ω bd , especially for the Velocity+Force cue, although it can be apparent on all algorithm types.

VI. EXPERIMENT 2: EVALUATION OF PERCEPTUAL SENSITIVITY

The purpose of this experiment was to evaluate how the operator's perceptual sensitivity of obstacles in the multi-UAVs' (remote) environment could be influenced by the proposed haptic cue algorithms.

A. Procedure

In this experiment, participants were presented with a swarm of four UAVs on each trial, which were located between two invisible obstacles (see Fig. 9). Their task was to move the multi-UAV swarm towards the direction of the obstacles and to determine which of the two obstacles returned a stiffer response on the haptic control device. Participants indicated the stiffer of the two obstacles by using a mouse to click on one of two possible buttons that were located at the left or right position of the screen, which corresponded to the respective positions of the obstacles.

The experiment was divided into three blocks of trials for the three haptic cues. The presentation order of these blocks was counter-balanced across the participants, such as to minimize practice effects.

Throughout the experiment, one obstacle was held at a constant stimulus level (distance, 3.4 m) while the other differed from this reference wall across 7 levels of relative stimuli, from 50% to 150% in equal steps, by the method of constant stimuli [START_REF] Gescheider | Psychophysics: the fundamentals[END_REF]. The positions of the obstacles, relative to each other, were counterbalanced throughout the experiment and there were 10 trials for each level of tested difference, resulting in a total of 70 trials per block. These trials were completely randomized for presentation order. Instructions were provided prior to experimentation, as well as a practice session, to ensure good performance during testing like Experiment 1. Each trial took approximately 20 secs to complete and 10 mins breaks were provided between the blocks of trials. The entire experiment took 90 mins to perform.

It is important to reiterate that only the UAVs were visible to the observer. We rendered the obstacles invisible to minimize any possible influence of visual feedback on our participants' discrimination responses like Experiment 1. This allowed us to specifically address the influence of our haptic cue feedback.

B. Results

Experimental results are summarized in Fig. 10. Smaller JND values indicate greater perceptual sensitivity to the haptic cue condition.

There was no significant main effect of haptic cue condition with low gains (F 2,34 = 0.49, p > 0.05). However, there was a significant main effect of haptic cue condition when larger gains were applied as reported in [START_REF] Son | An evaluation of haptic cues on the tele-operator's perceptual awareness of multiple UAVs' environments[END_REF]. Here, participants were significantly more sensitive to haptic feedback forces that were based on the Velocity information of remote UAVs, cues, compared to Force cue feedback.

Direct comparisons between the low and large gain conditions of each haptic cue revealed significant difference for all the haptic conditions (Velocity cue: t 34 = 13.7, p < 0.001; Force cue: t 34 = 6.4, p < 0.001; Velocity+Force cue: t 34 = 8.7, p < 0.001). Thus, larger gains in haptic feedback result in higher perceptual sensitivity, regardless of haptic cue design.

C. Discussion

Generally, participants are more sensitive to haptic forces with larger gains. This is to be expected. However, it is important to note that gains should not be indiscriminately increased. In our current scenario, the application of the same gain magnitudes resulted in differential improvements across the three haptic cue designs.

With larger gains, Force cue feedback fared comparably worse to Velocity cue feedback. In this regard, at least, our current findings contradicts conventional scenarios of teleoperation control (e.g., [START_REF] Son | Enhancement in operator's perception of soft tissues and its experimental validation for scaled teleoperation systems[END_REF]). With conventional teleoperation, it has been argued that force information tends to bring about better perceptual awareness [START_REF] Son | Enhancement in operator's perception of soft tissues and its experimental validation for scaled teleoperation systems[END_REF]. This, however, may not be the case with UAVs wherein force information is not sensed directly and has to be contrived. The current design of obstacle avoidance force (u o i in ( 8)) as well as the connectivity preservation force (u c i in ( 8)) both affect the operator's perception performance. It is clear that this particular design can be further improved in order to bring about better perceptual sensitivity.

Finally, the utility of Velocity+Force cue is mixed across our participants. Some participants appear to be most sensitive to its generated feedback while other participants are least sensitive to it. It is possible that our participants were unable to intuitively interpret haptic feedback that was contrived from two sources of information and while some preferred the Velocity+Force cue which is presumably richer in information, others construed this as being confounding or inconsistent.

VII. GENERAL DISCUSSION

A. Maneuverability

Force cue results in multi-UAV control that requires less effort. This is because Force cue feedback is absent during free motion. In contrast, the Velocity cue is absent only when UAVs move with a constant velocity vector, which rarely happens. Therefore, the latter case constantly requires the operator to counter-balance the UAVs' felt inertia in order to obtain the desired motion.

The current approach of considering the frequency response profile of the human operator proved to be important in distinguishing between our haptic cue designs for maneuvering performance. Our approach highlighted the specific aspect by which the Force cue differed from its Velocity counterpart. It is important to note that the measure of tracking accuracy, which is the more conventional measure of maneuverability (e.g., [START_REF] Aliaga | Experimental quantitative comparison of different control architectures for master-slave teleoperation[END_REF], [START_REF] Yokokohji | Bilateral control of master-slave manipulators for ideal kinesthetic coupling-formulation and experiment[END_REF]) did not discriminate between the haptic cue designs across our experimental scenarios. With this in mind, future studies should consider adopting the current approach in assessing maneuvering performance.

Evaluating maneuvering performance in terms of the operator's frequency response need not be restricted to the force input of the operator's motor command and the output of tracking accuracy. For example, other behavioral inputs that are relevant to maneuvering efficiency can be modeled in lieu of f h (see [START_REF] Chen | Human performance issues and user interface design for teleoperated robots[END_REF]). This could include measured stress levels or cognitive workload. Besides the human operator, the same approach could provide additional insight into the relationship of the parameters of haptic controllers and desired maneuverability outcome. Similarly, the desired output could be described in terms of other equally desirable objectives besides tracking accuracy (e.g., system stability, velocity and force tracking).

Another point that is worth noting is that our current measurement of f h consists of both the voluntary motor commands of the operator (f active h ) as well as forces that result from the operator's involuntary musculoskeletal reflexes, to movements in the haptic device (f response h ). Although it was not feasible to discriminate between the two in our current study, future work could address this distinction by obtaining direct measurements from the brain (e.g., using the electroencephalography (EEG) [START_REF] Millan | Noninvasive brainactuated control of a mobile robot by human EEG[END_REF]) which like f active h could represent the voluntary input of the operator. Alternatively, f response h could be estimated from an appropriate dynamic model (e.g., quasi-linear second-order model [START_REF] Dolan | Dynamic and loaded impedance components in the maintenance of human arm posture[END_REF]) of the wrist or elbow joint as well as from direct measurements of the skeletal muscles (e.g., using the electromyography (EMG) [START_REF] Clancy | Estimation and application of EMG amplitude during dynamic contractions[END_REF]). With this, f active h could be calculated by discounting the estimated f response h from the measured f h . Finally, a moving target was used in our current study to explicitly indicate the desired control path. Nonetheless, operators might vary in terms of what they might individually consider to be desirable control paths, especially for increasingly complex paths. Eye-tracking measurements of where the operators are fixating during UAV control could indicate the intended target destination. We intend to address this in a subsequent study and, in doing so, estimate free UAV control in the absence of explicit instruction.

B. Perceptual Sensitivity

Perceptual awareness of the remote environment via haptic feedback is important, especially for obstacle avoidance. In our current framework, the presence of proximal obstacles is rendered more detectable by haptic feedback that is based on the UAVs' velocity information, relative to force information. This is important to note, especially for instances where visual feedback is degraded or simply unavailable.

Full visual information was provided to our participants throughout the current experiments. In the real world, however, such information are unlikely to be readily available due to the limitations of on-board camera (e.g., restricted FOV, poor camera resolution) [START_REF] Mccarley | Human factors implications of UAVs in the national airspace[END_REF], [START_REF] Diolaiti | Teleoperation of a mobile robot through haptic feedback[END_REF] or the external disturbances (e.g., dust, smoke, or steam on the camera) [START_REF] Borenstein | Teleautonomous guidance for mobile robots[END_REF]. Even if visual information can be effectively captured, the data volume that has to be transmitted will be considerably more than that which is sufficient for fabricated force cues.

With this in mind, the extra effort that is required when Velocity cues are employed for multi-UAVs control might be justifiable in situations that require participants to be highly aware of nearby objects. Such a situation could be when multi-UAVs are used to probe the spatial layout of an environment; for example, search-and-rescue scenarios of enclosed spaces.

C. Selection of Control Parameters

The current study also investigated how performance benefits of haptic feedback cue related with the control parameter of gain (i.e., K). The results of the experiments were unequivocal. Large gain values are necessary in order for haptic feedback cues to have a noticeable effect on performance. The positive contribution of the Force cue to maneuvering performance particularly benefited from larger gain values (see Figs. 6(c) -8(c)). Nonetheless, it would be wrong to assume that larger feedback forces will automatically deliver improved human performance.

In fact, the largest force feedback was quantitatively returned by the Velocity+Force cue with large gains. However, Velocity+Force cue consistently returned poor performance, for the evaluations of both maneuvering and perceptual sensitivity. In addition, increasing gain values can result in a decrease in the bandwidth of the frequency response profile of maneuvering performance. In other words, larger gain values can decrease the effectiveness of the system in tracking the operator's control input.

Subsequent research would be necessary in investigating the functional relationship between gain values of haptic force feedback and human performance. This will be useful in determining optimal gain values for the various haptic cue designs.

VIII. CONCLUSION

In this paper, we presented a framework that supports the transmission of haptic cues from the controlled UAVs to the teleoperator's controller. Haptic cues can be designed to increase the teleoperator's maneuvering performance of the UAVs and perception of the remote environment. For this purpose, we proposed three possible types of haptic cues and three complementary teleoperation controllers based on wellknown conventional teleoperation control. These three designs were based on the UAVs' i) obstacle avoidance force, ii) velocity information, and iii) a combination of the two.

In addition, we proposed metrics for assessing maneuverability performance, which takes into account the human operator's control effort during bilateral teleoperation. With respect to this, we found H 2 norm to be a more reliable measure than the more conventional measure of position tracking accuracy (e.g., [START_REF] Aliaga | Experimental quantitative comparison of different control architectures for master-slave teleoperation[END_REF], [START_REF] Yokokohji | Bilateral control of master-slave manipulators for ideal kinesthetic coupling-formulation and experiment[END_REF]). More importantly, this allowed for a comprehensive assessment of maneuvering performance. We were able to determine that the Force cue feedback algorithm resulted in better maneuvering performance, in the sense that teleoperators were able to achieve equivalent steering of the remote UAVs with significantly less control effort. On the other hand, we proposed JND for assessing the operator's perception ability on the environment instead of conventional force tracking accuracy (e.g., [START_REF] Hashtrudi-Zaad | Analysis of control architectures for teleoperation systems with impedance/admittance master and slave manipulators[END_REF], [START_REF] Lawrence | Stability and transparency in bilateral teleoperation[END_REF]). With the JND, the operator's perception is evaluated better than in the conventional way from the human perspective: the perception performance with the three haptic feedback algorithms could be compared by using discrimination tasks and measuring the JND. Contrary to the maneuverability case, the results showed that the Velocity cue based haptic feedback algorithm was the best (statistically significant).

Maneuverability performance as well as perceptual awareness are equally important qualities during bilateral teleoperation. In light of this, the appropriate haptic feedback algorithm ought to be selected according to the application and the objective of the operator. The current evaluation of fabricated haptic cues for their influence on performance should allow us to design better algorithms for controlling multiple UAVs. Given that our control framework allows for the joint control of multiple robots with a single input, we can expect our findings to generalize to the teleoperation of single robots (e.g., mobile robot [START_REF] Diolaiti | Teleoperation of a mobile robot through haptic feedback[END_REF], UAV [START_REF] Stramigioli | A novel approach to haptic tele-operation of aerial robot vehicles[END_REF]).

Ultimately, the appeal of this study lies in the fact that it allows performance of the human operator to be quantified in a manner that can be integrated into control systems. This will motivate subsequent research that seeks to enhance the overall performance of control systems from the human perspective with an optimization in design and selection of the system parameters by using the proposed metric and psychophysical evaluation.

  robot and x ref (t) ∈ R 3 the target position at time t. In conventional teleoperation, x ref would typically be treated as the position of the master.

4 Fig. 1 .

 41 Fig. 1. Haptic teleoperation of multiple UAVs.

  Fig. 2.Experimental setup. Subject with haptic device (Omega 3) and Graphical User Interfaces (GUIs).

Fig. 3 .

 3 Fig. 3. Sketch of tuning procedure of control parameters.

Fig. 4 .

 4 Fig. 4. Experimental results of control parameters tuning. (a) With right-side obstacle. (b) With upper-side obstacle.

Fig. 5 .

 5 Fig. 5. Screen shot of maneuverability experiment with visible reference path and obstacles, which were rendered invisible during the actual experiment.

Fig. 6 .Fig. 7 .Fig. 8 .

 678 Fig. 6. Maneuverability performance in the Scenario 1. Figures are plotted as median ± standard error: (a) Cross correlation of the position tracking, CC position (%). (b) Bandwidth of the maneuverability, ω bd . (c) H 2 norm of the maneuverability, Φmaneuv 2 .

Fig. 9 .

 9 Fig. 9. Screen shot of test of perceptual sensitivity evaluation with visible obstacles, which were rendered invisible during the actual experiment.

Fig. 10 .

 10 Fig. 10. Summary of JNDs across the haptic cue conditions plotted as median ± standard error.

Accepted Version. Final publication available at ieeexplore.ieee.org

IEEE Trans. on Systems, Man, and Cybernetics, Part B

We refer the interested reader to[START_REF] Bouabdallah | Backstepping and sliding-mode techniques applied to an indoor micro[END_REF],[START_REF] Guenard | A practical visual servo control for an unmanned aerial vehicle[END_REF] for the description of some trajectory trackers for the quadrotor case.

This is value of relative intensity of stimuli for which the test stimuli is perceived to be equaled to the reference stimuli.

Greenhouse-Geisser corrections were applied for instances of unequal variances.

Bonferroni corrected.

This research was supported in part by the Max Planck Society, the WCU (World Class University) program funded by the Ministry of Education, Science and Technology through the National Research Foundation of Korea (R31-10008), and a Korea Research Foundation grant funded by the Korean government (NRF-2011-357-D00003).