A MULTI AGENT CONTROLLER FOR A MOBILE ARM

MANIPULATOR

Sébatien Delarue, Philippe Hoppenot and Etienne Colle

IBISC, CNRS FRE 2873 - University of Evry, 40 rue du Pelvoux 91020 Evry cedex, France

Keywords: Multi-agent, arm manipulator, mobile platform.

Abstract: Assistive robotics especially mobile arm manipulator can be useful for restoring manipulation function of

disabled people in everyday life tasks. However, those systems must be designed to be reliable, fault

tolerant and easy to control. This article proposes a method based on multiagent system for controlling the

robot. This kind of distributed architecture makes possible to be fault-tolerant without specifying additional

management of faults what improves reliability. Moreover, it is also possible to add specific constraints, for

example human like behaviors in order to facilitate the use of the system by the person. The multiagent

method is easier to implement than classical robotics approaches.

1 INTRODUCTION

ARPH project (French acronym for Robotic

Assistance to disabled People), developed in IBISC

laboratory, deals with restoring handling function for

handicapped person. It is a semi autonomous mobile

manipulator arm. Three types of control mode are

developed. In automatic one, the operator only gives

the goal and the system achieves it automatically.

However user would like to do by himself so in

manual mode, the operator controls all the degrees

of freedom of the system but the user’s workload

often is too important. The idea consists in

developing shared modes in which operator and

system share robot control. Project is divided into

two research axes: robotics for building autonomous

functions and human machine co-operation. The

paper focuses on robot control while keeping in

mind specificities due to the co-operation between

human and machine. For example industrial robot

performances such as accuracy are less necessary

than easiness of control by the user and fault

tolerance, the person being strongly tributary of the

assistance.

The classical method to control a robot is to

compute mathematical static and/or dynamic models

of the robot (Yoshikawa, 1990). The approach

provides good results in known environments for

carrying out repetitive tasks. If the objective is

known in Cartesian space (

zyxp ),,( ), those

models provide speed or angular value of arm joints

so that end effector performs the task. Generally

models are computed off-line so they are not

modifiable and cannot be adapted easily to quick

changes of robot structure e.g. due to the

dysfunction of one of robot joints. It requires

addition of specific management for making the

system fault-tolerant and taking into account on-line

changes. In assistive robotics, it is an important

criterion which can be solved by exploiting the

system redundancy.

In literature, many works exploit redundancy for

other objectives, for example to keep an optimal

manipulability of the end effector (Yoshikawa,

1990), (Bayle, 2001). Some authors, (Chabane,

2005) (Yoshikawa, 1984), have proposed

manipulability measures related to the task to be

achieved. Our goal is to propose an only model

which exploits robot redundancy and able to perform

task even in case of joint default.

In addition, the model must take into account

some aspects of human machine cooperation. For

instance previous works demonstrated the interest

of giving robot human like behaviors for facilitating

the appropriation of the robot by the user

(Rybarczyk, 2002) (Fuchs, 2001).

Distributed artificial intelligence makes complex

problem resolution possible more easily than

classical approaches. Our approach is based on a

multi-agent architecture divided into two parts, a set

of agents for the arm manipulator and only one for

the mobile platform. After a bibliographical study,

we present the multi-agent architecture able to

292

Delarue S., Hoppenot P. and Colle E. (2007).

A MULTI AGENT CONTROLLER FOR A MOBILE ARM MANIPULATOR.

In Proceedings of the Fourth International Conference on Informatics in Control, Automation and Robotics, pages 292-299

DOI: 10.5220/0001625902920299

 SciTePress

control the arm manipulator. Then, the architecture

is extended in order to control the mobile arm.

2 AGENTS AND MAS

Computer science evolves from a centralized

architecture (sequential treatments) to a distributed

one (parallel treatments). So, very quickly are

appeared autonomous agents, able to achieve

individual task with no external help. MAS for Multi

Agents Systems try to solve complex problem which

cannot be solved by a unique limited-mind entity

(Ferber, 1995). An agent can be defined as a

autonomous and flexible software entity (Weiss,

1999). An agent must be able to answer environment

changes. An agent has to perceive its environment,

to treat the data and then to act. This is a sensori-

motor loop. Stimuli may come from the inside

(agent itself) or the outside (environment). Action is

performed on the agent (internal states) or on the

environment. Agent behavior is the result of

interaction between the agent and its environment.

It exists three ways to implement agent

behaviors: cognitive, reactive, hybrid. Cognitive

way divides the internal treatment into three parts

perception, planning and action. Agent must have its

own world knowledge. It is able to analyze the

situation, to anticipate and then to plan an action.

Issues of this approach are limited speed and limited

flexibility. It is also inadequate in the case of

unexpected events. Reactive agent locally perceives

its environment (and possibly its internal states) and

deduces immediately actions to be carried out only

from this source of information. This principle is

based on reflex action ((Zapata, 1992), (Wooldridge,

1999)

). Hybrid approach merges the first two agents

a basic reactive behavior with a high cognitive level.

It aims at joining reactivity with thinking and

organization abilities of cognitive agents ((Brooks,

1986), (Chaib-Draa, 2001)).

The objective of multi-agents systems (MAS) is

to bring together a set of agents and to organize

them to reach a high level goal. We can find several

kinds of MAS. There are reactive systems which

bring together agents and then try to get an emergent

behaviour that can solve a higher level problem than

each agent can do. Another type of MAS appeared

with the need of making agents communicate in

order to cooperate (Beer, 1998). Parker (Parker,

1999) uses a central machine which supervises

messages. In this case, supervisor organizes groups

of agents whose competences are different but

necessary to succeed.

Reactive MAS are often used to solve problems

with the help of limited-mind agent having poor

world knowledge and poor action ability. The

objective is to find a social emergent behaviour of an

agent society able to solve a complex problem. For

example, design of ants or bird behaviour uses this

type of approach (Drogoul, 1993). Higher level

MAS are frequently used in mobile robotics,

especially in collective robotics (Lucidarme, 2003).

Systems are based on criteria like gratification,

altruism or cooperation (Lucidarme, 2002). A

complex dialog is implemented between agents. A

high hierarchical level entity is needed to oversee

the task to achieve and to centralize decisions and

communications. The objective is for example to

coordinate several tens of mobile robots transporting

containers on quays (Alami, 1998). Some others

applications are developed for path planning,

including obstacle avoidance (potential fields)

associated with artificial life algorithms

(Tournassoud, 1992), (Mitul). In robotics an

application of MAS is the design of arm manipulator

model. We can find few approaches (Duhaut, 1999)

(Duhaut, 1993), which describe how to reach a

Cartesian position without needing arm inverse

kinematic model. The method seems to be well-

adapted to our case. Each link is implemented like

an agent. Task resolution starts with the link of end

effector. It tries to align itself with the goal and to

place end effector on the goal by uncoupling itself

from previous link. Then, next link does the same

with a new sub goal given previously.

Figure 3 shows the beginning of the recursive

algorithm applied to a 3 DOF arm. Only three steps

are shown in order to illustrate how it works. The

goal is the cross. Initial situation is first step. On step

2, the end effector link rotates virtually and

uncouples itself from the arm to reach the goal. On

step 3, second link rotates and uncouples itself from

the arm to join the end effector link. The main

characteristic of the algorithm is that end effector

link makes bigger rotation than second one and so

on. Arm unfolding is not equally distributed between

each joint.

Figure 1: D. Duhaut approach.

Another use of MAS concerns high level

complex system like fault detection (Guessoum,

1997) or data merging (vision, touch, sound ...). It

often uses neuronal networks or fuzzy rules to find

the more pertinent information. In this case the word

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“agent” is usually employed in the meaning of

“expert system”.

3 ALTERNATIVE MAS

APPROACH

3.1 Presentation of the Approach

In our approach, we associate an agent to each arm

joint. Each agent has the same behavior rules as the

others. A joint agent computes the position of the

end effector (

Figure 2). The objective is to move the

end effector as close as possible to the goal. The

agent behavior is described by a very simple

algorithm:

Agent rotates joint in one direction and computes

the new position of the end effector. If this one is

closer to the goal than the present one, it is the good

direction and rotation continues in that direction. If

the new position is not better then rotation follows

the opposite direction.

By repeating this algorithm, the end effector

moves as close as possible to the goal. In the basic

algorithm it is possible that the agent rotates in the

bad direction at the first step. We will see in the next

paragraph that it can be avoided by using arm direct

kinematic model.

Joint agent

End

effector

Goal

Figure 2: Initial Agent position.

This behavior is now extended to a n-joint arm.

Each joint is controlled by an instantiation of the

reactive agent described before. The set of arm

reactive agents process data in parallel way. Agents

are autonomous and each one only tries to minimize

the distance between end effector and goal. The

algorithm of each joint is implemented in separated

processes. As said before we look for an emergent

behaviour that satisfies the global mission: the arm

end effector must reach the position of the goal.

Agent 1

ent 2

ent 3

Agent 4

ℜ

Figure 3: Four agent extent.

Figure 3

shows a four agent example. As

previously explained in the one agent case, each

agent must know the position of the end effector and

the movement generated by its action. It can be done

following two ways: first one, all joint values are

recorded in real time on a blackboard readable by all

agents. So, each joint agent can calculate the

position of the end effector by using the simple

direct kinematic model of the manipulator. This

solution implies to know this model. Second way

avoids the knowledge of any model by using an

external system (video camera, GPS) that can detect

the real position of the end effector. In the first case,

as the manipulator model is known, each agent can

pre-calculate the end effector position without really

moving the joint it controls. So, after a simulation in

one direction, it can decide the right move and

rotates joint directly in the right direction. In the

second case, we don’t need models or any joint

encoders. We don’t need any communication

between agents about joint position but we must

accept that a joint may rotate in the wrong direction

at some steps of the algorithm. What is interesting in

this case is that the algorithm can be implemented

directly on any manipulator without knowing any

arm measurement.

Example of Figure 3 shows the case of a 2D

robot. The method is also efficient for a 3D system.

3.2 Results

3.2.1 Comparison with (Duhaut, 1993)

Figure 4 shows the difference between two

approaches. The first, on the left, uses our MAS

algorithm called MSMA. The second, on the right

and called MD, has been developed by Dominique

Duhaut (Duhaut, 1993). Simulation presents a five-

joint arm manipulator represented on a 2D plane.

The arm unfolding is presented in three steps.

Figure 4: Comparison MSMA and MD.

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We can see that during unfolding, the MD

approach tries to unfold links starting with the end

effector one. The MSMA system unfolds all links at

the same time. So, each joint contributes to the

movement, avoiding links alignment. Moreover,

visual aspect is closer to human behavior. When a

person wants to take an object, he does not stretch

himself to the maximum.

Figure 5 shows an arm folding. Again, we can

see a more distributed movement between joints in

the case of the MSMA approach. There is no

collision between links.

Figure 5: Arm folds.

3.2.2 Behavior in Case of Broken Joint

Duhaut's method (Duhaut, 1993) does not permit to

simply take into account a joint fault so we only

present results with our method. Figure 6 shows

system behavior including two broken joints (dashed

limb and squared joint). In this simulation we can

use only the end effector position knowledge (no

kinematic model, no encoder values). In that case,

broken joint means the motor associated to it is out

of order (joint encoder out of order or not). If we use

a blackboard and the direct kinematic model, as

explained in the presentation of the approach, then,

the joint encoder must not be out of order to

guarantee a correct operation.

Here, the arm works at only 60% of its capacities.

Reachable domain is delimited by two half circles.

Dotted area represents the reachable space taking

into account the reduced capacities. A systematic

test has been realized, covering all the reachable

space: 100% of the 4592 tested positions have been

reached. The figure also shows three sample

positions. It is possible because of the redundancy of

the system.

Figure 6: Two broken joints simulation with MSMA.

3.2.3 Discussion

As agents work independently, all joints participate

to the movement inducing a natural visual aspect of

the arm for the user. For us, this property is very

important because it gives the system a human

behavior and helps human-machine cooperation.

The movement is distributed in all joints and the arm

configuration stays far from singular positions. We

can also see that there is no link collisions while the

arm folds. In the third simulation, without

integrating any fault treatment, we can see that

MSMA using external localization system is fully

fault tolerant if goal is reachable. If a blackboard is

used to store joint values in addition to the direct

kinematic model, joint encoders must not be out of

order even if motor is. In the two cases, even if a

motor is randomly moving because of noises or

control system fault, the system will reach the goal.

We can notice that MAS system does not need to be

informed of joints faults. Each software agent does

its work without taking care if it really controls or

not its associated joint.

4 APPLICATION TO MOBILE

ARM MANIPULATOR

The system to control is composed of a manipulator

arm embarked on a mobile platform. The first

objective deals with human-machine co-operation.

The idea is to give to the system behaviors inspired

from human being. For example, when a person

wants to take a book, he/she tries to keep his/her arm

not extended. If the book is too far to be taken by

arm extension, the person walks in the direction of

the book aligning the body on the hand movement

direction. The second objective is to make the

system more tolerant to some joint fault by

exploiting redundancy without using specifics fault

treatments. Indeed, in assistive field, it is important

to maintain a good quality of service.

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4.1 Mobile Platform Agent

The control of the mobile platform uses an agent

which controls angular and linear speeds. The

mobile platform agent is more complex than the arm

ones. The agent used for the mobile platform is an

hybrid one. Its cognitive capacities give the

possibility to add some interesting behaviors.

Firstly, the mobile platform has to move forward

because sensors for obstacle avoidance are located

on the front side of the system.

The second implemented behavior is to align

direction of displacement of the mobile platform

with the manipulator arm. Indeed, when a person

wishes to catch an object and must move to do it,

he/she generally tights the arm forward in the

direction of the movement of the body.

The third behavior concerns deadlock. In certain

cases, both mobile platform and arm shoulder joint

rotate at the same speed, but one on the right and the

other on the left direction. In that case, the gripper

does not converge to the objective. The agent of the

platform is able to detect this kind of situation. It

introduces a waiting cycle by leaving to the arm the

priority for reaching the goal. Once this cycle ends,

the platform agent tries again to align itself with the

arm if it is possible.

Reactive behavior is the same one as for the arm

agents. Mobile platform agent tries to minimize

distance between the end effector and the goal.

Mobile platform agent works independently from

the arm ones.

4.2 Results

We now simulate the whole system algorithm. First

we compare it with a classical mathematical method

using manipulability constraints. Secondly, we

simulate faults on some joints.

4.2.1 System and Protocol

The system is a 3D mobile arm. It is composed of a

mobile platform equipped with two driving wheels

and a free wheel to ensure stability. A 6 DOF

manipulator arm is fixed on the mobile platform. In

the following simulations, the end effector imposed

task consists in following a straight line with a

constant speed, in the upper-right direction of the

mobile platform.

Figure 7 describes the system and

the associated mathematical frame. The

displacement is perpendicular to the initial

orientation of the platform. The displacement plan is

formed by x and y axis. Initial conditions are

showed in

Table 1.

Figure 7: System description.

Table 1: Initial simulation conditions.

Object Initial value

Platform position (0,0,15) meters

Manus Joint 1 270 degrees

Manus Joint 2 120 degrees

Manus Joint 3 -125 degrees

End effector position (18,79,-20) meters

Simulation steps 400

Sampling rate 60 ms

Shift wanted for each step (0.42 , 0.12 , 0) cm

Total duration 24 s

On the first simulation, objectives are the

following ones:

- End effector must follow the desired trajectory

with good accuracy

- Mobile platform must move forward because of

front side sensors belt

- Mobile platform has to align itself with the arm

orientation

- Arm has to avoid its extended configurations.

We compare our approach (MSMA) using

agents introduced above with the MIM one which

uses manipulability criterion (Chabane, 2005).

Secondly, we check fault tolerance ability of

MSMA approach. So, we simulate a breakdown of

the shoulder and check if the whole system

redundancy (generated by the mobile platform)

permits to the system to reach the goal. We also

simulate a breakdown of joint 2 to check the whole

system behavior.

4.2.2 Results

Her, we do not show end effector trajectory. With

both approaches, the move is correct with a good

accuracy (less than 3mm of difference between the

real path and the desired one). There is no notable

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difference between them. Accuracy is linked to

simulation step duration. We choose a high one of

60 ms because our real system has a 60 ms

command loop.

Figure 8 shows platform trajectory and

orientation. We can see two very different

performances. MSMA works well. It always goes

forward keeping obstacle avoidance sensors in front.

It first turns slowly on the right, and then goes

straight until the end and aligns itself with the arm

orientation. We see a graining point in MIM

simulation and the platform ends the task moving

backwards.

During the move and with both models, the arm

is never bended. Angle between joint 2 and joint 3

stabilizes to a 70 degrees value which is far from the

0 degrees singular value of the arm.

Figure 8: MIM and MSMA platform moves.

We now show how the system reacts in fault

cases. MIM is not designed to be fault tolerant so its

behavior is not interesting here. It is why next

simulations show only MSMA model in three

samples faults situations:

• Arm breakdown joint 1 (shoulder) at 60°

• Arm breakdown joint 1 (shoulder) at 30°

• Arm breakdown joint 2 at 120°

These values are chosen as examples to illustrate

motor joint breakdown fault tolerance. We assume

that if goal is reachable, fault tolerance is effective

in all the cases. Breakdown occurs at time 0. Figure

9 shows end effector trajectory on x axis. We can

see that MSMA satisfy objective even in fault

situations. Results are the same on y and z axis. In

the first two situations, redundancy between arm

joint 1 and the mobile platform rotating movement is

exploited. As end effector follows the wanted path,

we can say that the platform agent works well when

the arm shoulder is broken. In that case, mobile

platform moves forward but does not align with the

arm orientation. Indeed, this alignment is performed

by the redundancy between arm joint 1 and the

mobile platform rotating movement and, in that case,

joint 1 is broken. Moreover, the arm is not tightened

and the angle between joint 2 and 3 is stabilized to

70 degrees. In the third situation, redundancy

between joint 1 and joint 3 is exploited. Both of

them permit a vertical move (y axis). Once again the

end effector follows the trajectory correctly. In that

case, the mobile platform moves forward and aligns

with the arm orientation. We can still notice that the

arm is not tightened and that its posture remains far

from singular position.

Figure 9: End effector trajectory on axis x.

4.2.3 Discussion

On the first simulation, we can see that the classical

model used implies a more complex trajectory for

the mobile platform on the whole system simulation.

There is a graining point making the robot blind (it

can not avoid obstacles anymore because ultrasonic

belt is in front side). It’s due to the manipulability

criterion used in this simulation. Our model shows a

better behavior, closer to a human one. The platform

goes forward and aligns itself on the arm orientation

inducing a more assimilable move for the user. The

use of independent agent helps to considerate

directly human behavior in algorithm. Also, the

system is fault tolerant as shown in simulation. The

end effector desired trajectory is reached even if arm

joint 1 or 2 are broken. This ability is due to multi

agent architecture which is able to run even if a

component is defective.

5 DISCUSSION

First, with our approach, we do not cut out the final

objective in sub goals which each agent would have

to reach. Each one has a local work to do

independently from others. We do not organize any

co-operation. Here, we speak about emergent

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behavior. Indeed, one agent can not reach the goal

alone. It needs others to achieve the task but it does

not know it. This kind of system provides very good

result concerning fault-tolerance.

Secondly, this approach induces a goal for each

agent. It is then possible to influence behavior of

some agents. Then we can easily adjust or add

behaviors to facilitate man machine co-operation.

That is the case for example with platform alignment

on the direction of the end effector in the same way

than alignment of the human body on its hand

direction. That leads to an easier assimilation of the

system by the user. Indeed, with a classical model,

to add a behavior requires the integration of

constraints in the global model itself, which is not

easy with this kind of system.

Our approach has also its limits. If we integrate

many behaviors, it is possible for the system to loose

its wanted emergent behavior. The added constraints

could be in conflict with the initial objective which

is to reach the goal. The system then enters in non

convergence cases. At the same time, we can loose

fault tolerance ability. To avoid this kind of errors, it

is first necessary for agents to keep autonomy

compared to the goal they have to achieve and thus

to be as simple as possible. Secondly, we have to

include priorities in the local objectives of each

agent. Reaching the goal has a high priority, going

forward has a smaller one. Aligning platform on arm

orientation has a very small one. Thirdly, we have to

implement deadlocking treatment by introducing

delays in specific situations. In our system, these

particular treatments are implemented in the agent of

the mobile platform. We do not plan any problem

resolution between agents. In our approach, we keep

simple algorithm to avoid high hierarchical

management. Indeed, high hierarchical management

could then be compared with a system using

mathematical models and including a fault treatment

supervisor to switch between them.

6 CONCLUSION AND

PERSPECTIVES

Our MAS system gives good results in relation to

human behavior. Objects can be caught with an

easily assimilable movement for the user (forward

move, alignment of the platform with arm

orientation, simple trajectory with no turnaround, no

bended arm). Accuracy is similar to classical

method. It is fault tolerant without integrating any

specific treatment. It makes easier the integration of

special human driving mode. There is no need to

compute mathematical model and especially the

inverse model. Our MAS system algorithm is easy to

implant and need only some geometric formulas and

thus very little computing power. It is a real time

one. We now have to implement algorithm on our

mobile platform and create scenarios of

displacement to judge the relevance of our algorithm

on a real robot. The first tests on the real system give

encouraging results. Simulations shown in this

article give an idea of the mobile manipulator

behavior with straight lines trajectory. These kinds

of trajectories are usually used by people driving

ARPH with HMI actually developed. So we think

that MAS system can directly replace the Cartesian

internal manipulator mode actually used. Actual

HMI allows user to control the platform and the arm

manipulator separately (two sets of buttons) as

shown on Figure 8.

Figure 10: The actual HMI two sets of buttons.

With MAS, it’s possible to control the whole

system with only one set of buttons by driving only

the grip, thus limiting the difficulty of seizure

operation by the user.

We also want to improve object grasping. One

possible way is to integrate a neuronal network that

could help the system to have a better posture (eg:

catching an object by the left if user is a left hand

writer...). This improvement should lead us to

manage with agents not only for the mobile arm but

also for the orientation of the grip. Moreover, ARPH

mobile platform has an ultrasonic sensors belt for

obstacle avoidance when moving in a room that has

to be integrated with MAS system to have a fully

operational system.

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