TELE-ROBOTS WITH SHARED AUTONOMY:

TELE-PRESENCE FOR HIGH LEVEL OPERABILITY

Thomas Geerinck, Valentin Enescu, Alexandru Salomie, Sid Ahmed Berrabah, Kenny Cauwerts,

Hichem Sahli

Electronics and Information Processing Department (ETRO)

Vrije Universiteit Brussel, Pleinlaan 2, 1050 Brussels, Belgium

Keywords: tele-robotics, tele-operation, shared autonomy, tele-presence.

Abstract: The aim is to improve the operability of an advanced demonstration platform incorporating reflexive tele-

operated control concepts developed on a mobile robot system. The robot is capable of autonomously

navigating in semi-structured environments. Reflexive tele-operation mode employs the robot extensive

onboard sensor suite to prevent collisions with obstacles when the human operator assumes control and

remotely drives the robot to investigate a situation of interest. For the shared autonomy aspect, four levels of

autonomy have been implemented: tele-operation, safe mode, shared autonomy and autonomous mode. The

operability level is enhanced by improving significantly the situational awareness of the operator by using

an inertial tracker in combination with a head mounted display creating a certain feeling of presence. As

such, the system permits precision observation and pinpoint data collection without subjecting the user to a

possibly hazardous remote environment.

1 INTRODUCTION

The design of a mobile robot capable of performing

a complex task autonomously, even if it operates in

well-structured environments, requires the

integration of different technologies. Important

aspects are the mechanical design, the low-level

electronic control, perception of the environment

using different sensors including cameras, sensor

fusion, efficient path planning, and intelligent robot

control.

Tele-robotics, tele-presence, tele-manipulation,

tele-operation, tele-service are all terms that describe

ways of handling machines and materials remotely.

The term tele-operation refers simply to the

operation of a vehicle or system over a distance

(Fong et al, 2001). Traditionally, tele-operation is

divided into direct tele-operation and supervisory

control (Sheridan, 1992). In direct tele-operation the

operator closes all the control loops himself. In

between autonomous robots and direct tele-

operation, levels of supervised or shared autonomy

(control) can be created. Depending on the degree

the human operator is directly involved to the robot

control, some variables or functions are supervised

and some are controlled directly by the operator.

It is generally accepted that a more efficient

achievement of the task can be obtained by

increasing the number of data feedback and by using

proper multimedia interfaces (Diolaiti et al, 2002).

However, the need for the operator effort decreases

the autonomy of the robot (Scholtz et al, 2003)

(Yanco et al, 2004) and increases the operator load

and the transferred amount of information between

the operator and the robot. Poorly designed user

interfaces, on the other hand, can result in spatial

disorientation, lack of situational awareness,

confusion, and frustration (Olivares et al, 2003). The

human-machine interface must be designed so that it

maximises information transfer while minimising

cognitive load (Meier et al, 1999). One good

approach to enhance the quality of information

available to the operator is the use of sensor fusion

to efficiently display multisensor data.

Step-by-step, the tele-operation technology

improves towards tele-presence based technology by

increasing the sensory feedback, using HMDs, head

motion trackers, datagloves, etc. (Tachi et al, 1989).

Tele-presence means that the operator receives

sufficient information about the robot and the task

243

Geerinck T., Enescu V., Salomie A., Ahmed Berrabah S., Cauwerts K. and Sahli H. (2005).

TELE-ROBOTS WITH SHARED AUTONOMY: TELE-PRESENCE FOR HIGH LEVEL OPERABILITY.

In Proceedings of the Second International Conference on Informatics in Control, Automation and Robotics - Robotics and Automation, pages 243-250

DOI: 10.5220/0001184302430250

 SciTePress

environment, displayed in a sufficiently natural way,

that the operator feels physically present at the

remote site (Sheridan, 1992). The optimal degree of

immersion required to accomplish a task is still a

topic for discussion. Some researchers claim that

high-fidelity tele-presence or tele-existence (Tachi,

2003) requires feedback using multiple modalities

(visual, auditory, haptic) (Meier et al, 1999). The

most important feedback provider remains the visual

system mounted on the robot. A more human

viewpoint is created by using stereovision. Vision is

the sensor which is able to give the information

“what" and “where" for the objects a robot is likely

to encounter most completely.

Furthermore, a distinction can be made between a

passive and an active vision system (Davison, 1998).

Humans are most certainly in possession of an active

vision system. Translated to the robot, this implies

the need of zoom function and a manual or

automatic tracking system on the vision system of

the robot. Active vision can be thought of as a more

task driven approach than passive vision. With a

particular goal in mind for a robot system, an active

sensor is able to select from the available

information only that which is directly relevant to a

solution, whereas a passive system processes all of

the data to construct a global picture before making

decisions. In this sense it can be described as data

driven.

These approaches work fine when there is little

delay in communication, however once transmission

delay is introduced the systems become unstable.

The instability problem can be eliminated by shared

(supervisory) control. Under shared control the

operator gives a goal to the tele-robot. The goal is

then executed by a local control system and

supervised by the operator. This emerging field of

Human-Robot Interaction (HRI) represents an

interdisciplinary effort that addresses the need to

integrate human factors, cognitive science and

usability concepts into the design and development

of robotic technology. As the physical capabilities of

robots improve, the reality of using them in

everyday locations such as offices, factories, homes

and hospitals, as well as in more technical

environments such as space stations, distant planets,

mines, ocean floors and battlefields, is quickly

becoming more feasible (Yanco et al, 2002).

Generally speaking, robots are more adept at

making some decisions by themselves than others

(Scholtz, 2003). Unstructured decision making,

however, remains the domain of humans, especially

whenever common sense is required. In order for

robots to perform better, therefore, they need to be

able to take advantage of human skills (perception,

cognition, etc.) and to benefit from human advice

and expertise. To do this, robots need to function not

as passive tools, but rather as active partners.

Numerous robot control architectures have

addressed the problem of mixing humans with

robots. Systems like adjustable autonomy, mixed

initiative systems (Marble et al, 2004) and

collaborative control (Fong, 2001) (Fong et al, 2002)

have recently received considerable research

attention. As an example of adjustable autonomy

obstacle avoidance behaviour (Borenstein, 1990) can

be considered.

The main contributions of this paper lie in

presenting a functionality scheme of the global

system, incorporating the mentioned state of the art

hardware devices and software algorithms. This way

an advanced demonstration platform is created, ideal

for further enhancements in the area of mobile

robotic research.

The remainder of this paper is organized as

follows. First, an overview of the system

architecture is given (Figures 1 and 2), explaining all

the co-existing modules, starting from hardware

level, containing the physical units used in this

project, through implementation and task description

level up to the programming environment level,

where different threads arise and form the modules.

Then in the discussion some results are given, before

necessary conclusions are drawn.

Figure 1: Robot system architecture

2 SYSTEM OVERVIEW

The overview of the system is presented in Figure 1,

the robot site, and Figure 2, the client or operator

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244

site. In order to structure this description, first,

traditional or direct tele-operation will be the task to

achieve, including the creation of feeling of

presence. All the indispensable modules will be

briefly explained. To introduce supervisory or

shared autonomy control, additional units must be

added. These will be presented subsequently.

In order to introduce shared or supervisory

autonomy control aspects to the existing architecture

of direct tele-operation, a choice must be made in

how to define the responsibilities for both robot and

tele-operator. We chose to provide fixed static

responsibilities for human and robot. Based on the

statement that the aim of robotics is to serve and

help humans, our implemented system is well-suited

for exploring purposes. The fixed responsibilities are

defined in 4 levels of autonomy:

• Tele-operation: The user has full, continuous

control of the robot at low level. The robot takes

no initiative except perhaps to stop once it

recognizes that communications have failed. It

does indicate the detection of obstacles in its path

to the user, but will not prevent collision. This is

the default autonomy level.

• Safe Mode: The user directs the movements of

the robot, but the robot takes initiative and has

the authority to protect itself. For example, it will

stop before it collides with an obstacle, which it

detects via multiple US and IR sensors.

• Shared Control: The robot takes the initiative to

choose its own path in response to general

direction and speed input from the operator.

Although the robot handles the low level

navigation and obstacle avoidance, the user

supplies intermittent input to guide the robot in

general directions.

• Full Autonomy: The robot performs global path

planning to select its own routes, acquiring no

operator input. The goal of the robot can be

specified by the operator or by the robot’s vision

system.

Figure 2: Client or operator system architecture

A. Tele-operation

Robot Navigation Module

The green boxes (Figure 1) represent robot devices.

The robotic platform is the Nomad200 (Figure 3), an

electrical driven mobile robot build by the Nomadic

Technologies, Inc. company. Build at early 90's; it

has now reached the status of a somewhat antiquated

machine in world of robotics. It is equipped with

three sensory modules: ultrasonic,

infrared and tactile. With its strong and stable

structure, the Nomad200 provides an ideal platform

for adding extra mechanical and/or sensory

structures. The red boxes are taken care of by the

internal computer system of the Nomad robot.

In direct tele-operation, the navigation module

has several tasks: communication with the robot,

building a map of the environment, and manage the

strategy of navigation. These tasks, blue coloured in

Figure 1, have been implemented in another PC

platform (Figure 3), placed on top of the Nomad200

and described further on. A coax cabled ethernet

connection links both systems.

The navigation strategy manager selects which

driving behaviour is executed. For direct tele-

operation, this behaviour is straightforward: simply

feed the acquired speed and steering commands to

the robot’s motion controller.

For direct tele-operation the building of a map of

the robot’s local environment is not indispensable.

However, it is useful for development purposes. The

map is constructed combining the US and IR

sensory information.

Robot Communication Module

The robot is equipped with a wireless

receiver/transmitter (802.11g/2.4GHz Wireless PCI

Adapter) to achieve communication with a remote

user. Using two socket based connections two tasks

have to be accomplished in this module. Receiving

the remote operator’s input commands and sending

back an acknowledgment message is the first task.

Subsequently the sending of the camera frames

when received a proper command.

TELE-ROBOTS WITH SHARED AUTONOMY: TELE-PRESENCE FOR HIGH LEVEL OPERABILITY

245

Figure 3: The Nomad200 robot with the upper PC-

platform and the biclops head on top. The stereo vision

system forms the robot’s eyes

Robot Vision Module

The robot vision module consists at the hardware

level of a stereo head, type Biclops, and two

miniature CCD Color Cameras. The head is

mounted on the upper PC platform, carrying both

cameras (Figure 3).

This module performs two tasks concurrently,

namely, (1) to accurately control the pan and tilt

angle of the stereo head to allow seamless changing

the viewpoint, and (2) to capture in a synchronized

way frames from the left and right cameras by

means of a well suited frame grabber. Further, the

captured frames are sent to the remote user. In order

to reduce the time needed for the transfer the frames

are compressed either using the classical JPEG

encoder or a Wavelet based coding technique.

The employed Wavelet based coding scheme, i.e.

SQuare Partitioning (SQP) (Munteanu et al, 1999)

was developed at our department. It allows rate-

distortion performances comparable with state-of-

the art encoding techniques, allowing lossy-to-

lossless reconstruction and resolution scalability. As

illustrated in Figure 4, which shows a side-by-side

visual comparison of a frame compressed at 45:1

with JPEG and SQP, SQP clearly outperforms JPEG

in terms of rate-distortion. Although the encoding

with SQP is roughly 4 times slower than with JPEG,

still a CIF image can be compressed in real-time at

25 frames per second on a 2Ghz processor.

The compressed frames are handed to the

communication module that further sends them via

the wireless link to the client. The resolution

scalability feature of SQP comes in handy when

progressively streaming the data, since the decoder

at the client site does not need to wait until all the

data has arrived, but it may start reconstructing a

lower resolution of the image (from the received

data) and start processing that image first while

waiting to receive the remaining data that would

allow to reconstruct the image at full resolution.

JPEG, CR=45 SQP, CR=45

Figure 4: Image was compressed at 45:1 with JPEG and

SQP

Client Control Module

The main task of this module is the regular update of

input commands conferred by the operator. By

means of two hardware devices the operator controls

the robot and the stereo head. The robot is controlled

by use of a joystick, which is interfaced using Direct

Input. The stereo head is controlled by movement of

the operator’s head. A motion tracking device is

placed on the head of the operator and registers the

rotations of the head made by the operator. The

InertiaCube from InterSense is a precision

orientation reference system and performs an

inertial-based tracking from integration of 9 sensing

elements. The range of this device is 360° and has an

update rate of 180 Hz. It has 3 DOF (Yaw, Pitch and

Roll). However, only 2 of them are actually used,

according to the 2 DOF of the pan-tilt stereo head.

The range of the pan-tilt stereo head is also limited:

120° of tilt range, 240° of pan range.

Whenever the update of input commands is done

and the user interface is updated, this new data is

send to the robot, via the previously mentioned

socket based connection.

Client Communication Module

The client’s computer is equipped with a

802.11g/2.4GHz Wireless Broadband Router.

According to the robot’s communication setup also

here 2 socket objects are attached to 2 different ports

of the PC.

Client Active Vision Module

The received compressed images from the robot’s

eyes, are of course decompressed, before they fill

the buffers of a NVIDIA Quadrox graphical card.

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246

The operator sees a 3D view of the robot’s

environment, due to the stereovision setup. Being

able to look around freely, from a remote location, at

a sufficiently high frame rate, due to image

compression, provides the operator with a certain

feeling of presence at the remote site.

B. Shared Autonomy Control

The addition of the mentioned levels of autonomy

implicates changes to the existing modules. In this

section these implications to the existing tele-

operation modules are discussed in detail.

Robot Navigation Module

According to the selected level of autonomy, the

navigation strategy controller selects the proper

robot behaviour. In safe mode the available map is

checked for collision danger and if necessary a

emergency stop is performed. In shared control

mode as well as in autonomous mode the robot has

the responsibility of the local navigation. To

accomplish this task an obstacle avoidance

controller is included in the system. As shown in

Figure 5 the output from the obstacle avoidance

controller is combined with the input direction from

the operator.

This combining should be seen as a weighted sum of

two vectors:

• the operator's reference command F

• the obstacle avoidance feedback F

generated

by the autonomous obstacle avoidance

algorithm.

rrtts

FwFwF

⋅+⋅=

1=+

),,(

321 obstacleobstacleobstacler

dddfw =

Figure 5: Obstacle avoidance control architecture.

The steering of the robot is aligned with the

direction of the resultant vector F

Res

and yields

continuous and smooth motion. In the absence of

obstacles, F

= 0, the robot follows the operator's

directions. If the robot approaches an obstacle, F

(usually pointing away from the object) and w

gradually increase in magnitude and cause a

progressive avoidance manoeuvre. This gradual shift

in control is completely transparent.

The basic building block of the present

navigation strategy is a behaviour, defined here as a

representation of a specific sequence of actions

aimed at attaining a given desired objective. Each

behaviour comprises a set of fuzzy-logic rules.

The present robot-navigation strategy involves

four behaviours, denoted seek-goal, avoid-obstacle,

go straight ahead, and make U-turn. These

behaviours are described in detail in the following.

Goal seeking behaviour

This controller allows the mobile robot, starting

from the actual position, to reach a target point. This

operation is realised in an environment where there

are no obstacles around the robot.

Given the azimuth (ϕ) and the range to the target

(ρ), a fuzzy controller calculates the turn angle and

speed commands to apply to the robot to reach it.

The used controller is of zero order Sugeno’s type

and uses linguistic decision rules of the form:

If (ρ is A

) and (ϕ is B

) then (∆θ is C

)

Where A

and B

are fuzzy sets defined

respectively in ρ and ϕ universes of discourse, and

is a constant.

In this controller (Figure 6)

the change in angle to

apply to the robot to reach the target increases as ϕ

and ρ decreases, i.e. as the target is closer to the

robot and far from its direction.

Figure 6: Transfer function of the controller for Goal

seeking

Obstacle Avoidance

If an obstacle is detected in front of the robot, the

nearest point (of this obstacle) to the robot and

making the smallest angle (azimuth) with its axis is

marked.

A fuzzy controller using the information

provided by the sensors is initiated. It considers the

polar coordinates in the robot frame of the detected

points from the obstacles to estimate the change in

angle to apply to the robot to avoid these obstacles.

The used controller is a zero order Sugeno’s type

too and its transfer function is given by Figure 7. In

TELE-ROBOTS WITH SHARED AUTONOMY: TELE-PRESENCE FOR HIGH LEVEL OPERABILITY

247

this controller the change in angle to apply to the

robot is more important as the obstacle is closer to

the robot and closing its way.

Figure 7: Transfer function of the controller OAC

Go straight ahead behaviour

This action is used by the robot if there is an

obstacle embarrassing it to go toward its goal but no

obstacle is detected in front of it. In this case the

robot continues moving with its currents speed and

orientation.

Make U-turn behaviour

The robot uses this action in order to leave some

blockage situations like a closed way or a narrow

way. When this action is activated, the robot makes

a U-turn in its position and moves straight ahead

until a rotation at the right or at the left is possible.

Robot Vision Module

In all control modes the operator selects a certain

goal or location of interest based on the the visual

feedback information received from the robot. The

human in the control loop is fully responsible for

this goal selection using his own capabilities for

active visual search tasks. However, one could think

of merging this target choice towards the robot.

Therefore biologically inspired visual attention

models must be considered for the automated

selection of a region of interest, combined with

tracking algorithms to keep the target in the field of

view. Yet, research must be done to allow useful

cooperation between robot visual actions and human

visual actions.

3 DISCUSSION

Tele-operation performance

The performance of control and visual feedback

loops are essential when the performance of the tele-

operation system is evaluated. The tele-operation

system in the test platform is coordinated, i.e. the

position of steering and throttle is transferred to the

position of those actuators. When the actuators are

speed controlled servos the position control loops

are made in the robot’s main computer.

Performance of the test vehicle was examined by

measuring the step responses of the steering and

throttle over the tele-operation loop, ignoring the

time delay in this ideal communication test case. The

steering and throttle step responses are given in

Figure 8. It can be seen that the steering has a

relatively big delay, about 2 s. Also the throttle’s

delay is quite big before reaching the desired speed,

300 ms. Compared to the mean human reaction time,

the speed delay is acceptable. The steering delay

however, makes the operator wait for the robot’s

accurate reaction. To create a reactive system the

update frequency of the data is 10 Hz.

The delay in communication is not a constant

value. It depends on the distance between router and

antenna, and the obstacles in between, e.g. wall of

room. However, most of the delay comes from the

slowness of the actuators.

The vision loop consists of two processes. First of

all the motion-head-tracking-servo-head loop

provides some delay (Figure 9: 600ms/50dgr)

mainly caused by the servo system. Internally the

update rate of the head motion occurs at 180 Hz. The

update of these angles to the robot occurs every 100

ms (10 Hz), again to keep this system reactive.

The loop of capturing, compressing and sending

stereo images contains two delays. The first delay is

the time it takes to compress and decompress, the

second is the transmission delay. The time lost with

compression and decompression is on the other hand

recuperated by the much faster transmission of the

images compared to the case without compression.

The resolution of the camera is 384 by 288, resulting

in a image with size 300Kb, and an obtained frame

rate of approximately 5Hz without compression.

This is obviously too low to provide good and

smooth view of the environment. When using

compression a frame rate of approximately 20 Hz is

obtained. This augmentation in frame rate is limited

by inherent limited processing capacity of the PC.

Feeling of presence: tele-presence

A general problem when applying augmented tele-

presence systems are technical complexity and the

need for broad bandwidth for data transmission. Use

of image compression partly solves this bandwidth

issue. When wireless communication is the only

choice, the cost of the data transmission system for

long distances can be very high. For the simulation

of the human active vision system at robot’s site, a

complex servo system for cameras should have up to

7 DOFs if all head and eye movements were to be

tracked and repeated. In practical applications,

mostly due to cost and fault probability, the

minimum number of DOFs which is still acceptable

from the operator point of view is 2.

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248

Ergonomic problems are not minor and need a

careful consideration. The problem of simulator

sickness (SS) can be significant in tele-presence

based tele-operation. The most typical reason is the

Figure 8: Speed and angular step response from the

Nomad200 robot platform

Figure 9: Biclops robotic head step response

cue conflict. In cue conflict different nerves get

different information from the environment. Typical

here is the conflict between visual and vestibular

inputs. Other possible reasons can be the resolution

of the HMD and the time lags in vision and control

resulting in motion blur and a significant decrease of

situational awareness. In our system the resolution

of the HMD is 800x600 pixels.

Nevertheless, the feeling of presence remains a

highly subjective feeling. It is very difficult to

design good experiments from which meaningful

conclusions can be drawn. It is our believe that a

good combination of feedback modalities can

provide a sufficient feeling of presence for exploring

and surveillance purposes. The HMD provides the

operator good quality images, thanks to state of the

art compression, at a sufficiently high frame rate, to

obtain a smooth view of the environment. Also the

stereo vision system provides the operator with a

notion of depth, giving him the ability to perceive

absolute distances. In order to be constantly aware of

the motion of the robot, a layer is placed upon the

images in the HMD with the speed, steer angle, and

pan and tilt angle. The one thing that is currently

missing from the setup is a auditory feedback

modality, providing the operator with stereo sounds

from the remote environment as well as robot

messages about his own status. This additional

modality will be added in the near future.

4 CONCLUSION

In this paper, an advanced mobile robot platform is

presented, which can be used for exploring and

surveillance purposes. At present a manual control

system by the human operator using a joystick has

been developed. In order to reduce the operator’s

workload related to the robot’s control, we

introduced shared autonomy principles by defining

several levels of autonomy. An obstacle avoidance

algorithm was implemented based on fuzzy logic,

giving the robot the responsibility of the local

navigation.

The current stereo vision system allows the

human operator not only to feel that they are located

at the remote task area, but also to catch a sense of

distance to the objects existing in the environment.

This is accomplished by integrating different

technologies, such as image compression, wireless

communication, head motion tracking, etc.

ACKNOWLEDGEMENT

This research has been conducted within the

framework of the Inter-Universitary Attraction-Poles

program number IAP 5/06 Advanced Mechatronic

Systems, funded by the Belgian Federal Office for

Scientific, Technical and Cultural Affairs.

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