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Cooperation Issues and Distributed Sensing for Multirobot Systems These soccer-playing robots can cooperate and change roles as needed, and their 360 degree vision can be combined into team-wide observations. By Enrico Pagello, Member IEEE, Antonio D’Angelo, and Emanuele Menegatti, Member IEEE Adviser : Ming- Yuan Shieh Student : Ching- Chih Wen SN :M9820108 1

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Page 1: Cooperation Issues and Distributed Sensing for Multirobot Systems These soccer-playing robots can cooperate and change roles as needed, and their 360 degree

Cooperation Issues and Distributed Sensing for Multirobot Systems

These soccer-playing robots can cooperate and change roles as needed, and their 360 degree vision can be combined into team-wide observations.By Enrico Pagello, Member IEEE, Antonio D’Angelo, and Emanuele Menegatti, Member IEEE

Adviser : Ming-Yuan ShiehStudent : Ching- Chih Wen SN :M9820108

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ABSTRACT INTRODUCTION USING A BEHAVIOR-BASED APPROACH

A. Implementing Schemas SINGLE SENSOR OBSERVATION BUILDING HYBRID ARCHITECTURE FOR COORDINATION

A. Integrating Deliberation B. Implementing Coordination

FUSING MULTIPLE OBSERVATIONS EXPERIMENTAL RESULTS CONCLUSION

Outline

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This paper considers the properties a multirobot system should exhibit to perform an assigned task cooperatively.

Our experiments regard specifically the domain of RoboCup middle-size league (MSL) competitions.

In our paper, each individual robot has been designed to become reactively aware of the environment configuration.

In addition, a dynamic role assignment policy among teammates is activated, based on the knowledge about the best behavior that the team is able to acquire through the shared sensorial information.

ABSTRACT

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A MULTIROBOT system (MRS) is characterized by attributes like size, composition, communication topology, and range [12], as well as agent redundancy and collective intelligence [20].

Thus, solving cooperatively complex tasks requires an intelligent multi-robot system to show dynamic group reconfigure-ability and communication among individuals.

In the explicit communication, signals are intentionally shared between two or more individuals, while in the implicit communication, the robots observe other robots’ actions.

INTRODUCTION(1/3)

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On one hand, we investigate under what conditions an MRS is able to perform a given task cooperatively by using a dynamic role assignment mechanism.

On the other, we discuss the problem of developing a distributed sensoring system based on omnidirectional vision sensors to cooperatively track and share the information about moving objects.

In our approach, each robot has been designed to become aware of distinguishing configuration patterns in the environment by evaluating descriptive conditions as macro-parameters at the reactive level.

INTRODUCTION(2/3)

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When the environment is static, the agent can analyze its subcomponents and store the acquired information in a sort of memory [22].

In [26], we proposed an omnidirectional distributed vision system (ODVS) capable of tracking moving objects in a highly dynamic environment by sharing the information gathered by every single robot.

INTRODUCTION(3/3)

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A. Implementing Schemas The behavior-based approach [6] assumes a robot to be situated

within its environment.

Moreover, since robots are not merely information processing systems, their embodiments require that both all acquired information and all delivered effector commands must be transmitted through their physical structure.

Among them, schema-based theories have been adapted by Arbib [1] to build the basic blocks of robot behaviors.

USING A BEHAVIOR-BASED APPROACH(1/5)

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Our implementation assumes only one schema to be active at a time, in a winner-take-all fashion.

Moreover, the output is not a continuous signal but either a motor command to feed some servo or an evaluated condition affecting the activation/inhibition mechanism for another schema.

Following [2], we implemented a primitive behavior with one motor schema, representing the physical activity, and one perceptual schema, which includes sensing.

USING A BEHAVIOR-BASED APPROACH(2/5)

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The resulting governor’s unit of each individual robot is a hybrid architecture whose deliberative/reactive tradeoff stems from the hierarchical organization of its behaviors.

Thus, the overall architecture is organized at many levels of abstraction, the lowest one being directly coupled with the environment by the robot servos.

A behavior is fired by an activation-inhibition mechanism built on evaluating condition patterns.

USING A BEHAVIOR-BASED APPROACH(3/5)

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Thus, a primitive behavior at the reactive level results in appending just one perceptual schema to one motor schema in order to get the sensorimotor coordination that the individual robot is equipped with.

The reactive level uses only information coming from sensors and feeds motors with the appropriate commands.

USING A BEHAVIOR-BASED APPROACH(4/5)

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Fig.1

USING A BEHAVIOR-BASED APPROACH(5/5)

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In Fig. 2, we sketched the perception module implemented inside our robots.

Fig.2

SINGLE SENSOR OBSERVATION(1/4)

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The measures in the common frame of reference are sent to the other robots and to the distributed vision module (DV), where they are fused with the measures received by the teammates.

Fig.3 Fig.4

SINGLE SENSOR OBSERVATION(2/4)

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Every measurement is made in the reference frame of the robot and is then transformed into the reference frame of the field of play by the DV.

This assumes that the robot knows perfectly its pose in the environment while it moves in the field of play.

This is done using the self-localization algorithm developed by our RoboCup team [28].

SINGLE SENSOR OBSERVATION(3/4)

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Only the plot of the data about the distance object robot is displayed, since the variance on the azimuth resulted in being so small that one could assume a zero error on azimuth.

We, therefore, assumed a certain nonzero variance, increasing with the distance from the robot. This will also take into account the errors introduced by a nonperfect localization of the robot.

Assumptions about the time interval between two measurements cannot be made; in fact, we are working with robots with every different computational power, with vision systems working at different frame rates (from 10 to 25 fps).

SINGLE SENSOR OBSERVATION(4/4)

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A. Integrating Deliberation Generally, individual robot behaviors are triggered by

coordination in such a way that some actions that are a part of an agent’s own goal-achieving behavior repertoire, but have effects in the world, help other agents to achieve their goals [24].

Thus, the problem could be stated as follows: how much deliberation should be implemented between agents to ensure the emergence of cooperative behavior?

The first concerns the ability of any robot to recognize the circumstances under which it can be engaged in a collective behavior.

BUILDING HYBRID ARCHITECTUREFOR COORDINATION(1/8)

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The second requires that those circumstances become effective, to allow the group of robots to cooperate.

In the hybrid multilevel architecture that we have devised for our robot team (see Fig. 5), two intermediate levels have been provided to allow robot individuals to communicate.

Fig.5

BUILDING HYBRID ARCHITECTUREFOR COORDINATION(3/9)

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B. Implementing CoordinationAs previously stated, coordination has been implemented at two stages: the lower, dealing with the reactive level, provides the necessary conditions to be verified to start an activation cycle of cooperation.

When an individual robot succeeds in recognizing a distinguishing configuration pattern in the environment, it tries to become a master of a collective action indexed by that pattern.

This can occur because at the reactive level some stigmergic condition forces the estimation of a given utility function to evaluate over a fixed threshold.

BUILDING HYBRID ARCHITECTUREFOR COORDINATION(4/9)

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In the case two robots try to simultaneously advocate a master role, and the utility functions computed by both robots give the same value, then a random selection is done to choose which one must be the master.

Roles are played at different levels; let us call them can be, assume, acquire, and advocate where the first three refer to a supporter and the last one is committed to the master.

As an example, we will discuss the coordination task between two robots which try to carry the ball towards the opponent goal, passing and eventually defending it from opponents’ attacks.

BUILDING HYBRID ARCHITECTUREFOR COORDINATION(5/9)

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Because the two robots are required to play well-specified roles, we assign the master role to the robot chasing the ball; whereas, the other can be considered the supporter.

behavior clampmaster

haveBall(me)&haveBall(mate)

acquire(Master);

acquire(Master)&Notify(Master)

advocate(Master);

behavior clampsupporter

acquire(Master)&canBe(Supporter)

assume(Supporter);

assume(Supporter)&Notify(Supporter)

acquire(Supporter):

BUILDING HYBRID ARCHITECTUREFOR COORDINATION(6/9)

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Since the role assignment depends on ball possession, we can use the condition haveBall to discriminate among robots which one is really carrying the ball.

The basic rule is that a master role must be advocated, whereas the supporter role should be acquired.

To this aim, we require two reciprocity rules where a role is switched either from acquire to advocate or from assume to acquire, provided that a notification is made to the referred teammate.

By doing so, the former robot issues a behavior of chaseBall, whereas the latter exhibits a behavior of approachBall.

BUILDING HYBRID ARCHITECTUREFOR COORDINATION(7/9)

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C. RoboCup MSL Teams Coordination PoliciesThe first attempts were made on a static basis, where each robot takes a fixed role within the team, but if we consider the integrating robot societies [35], characterized by a small number of heterogeneous and specialized members, it becomes important for each individual to develop the ability of modifying dynamically its behavior while performing an assigned task.

It can be stated as follows. Given n robots, n prioritized single-robot roles, and some estimation of how well each robot is expected to play each role, assign robots to roles in order to maximize the overall expected performance [15].

BUILDING HYBRID ARCHITECTUREFOR COORDINATION(8/9)

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Thus, the dynamic role assignment problem is the natural evolution of an iterated assignment problem in the domain of multirobot systems.

At beginning, the choice of the appropriate role was obtained by considering collision avoidance issues and competitive behaviors [33].

Then, we introduced a hybrid architecture [10], [34], where the deliberative component interacts with the reactive one and vice versa, as it has been described in the previous sections.

BUILDING HYBRID ARCHITECTUREFOR COORDINATION(9/9)

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The measures of the position and speed of the tracked objects can come from two sources: the repeated observations of the single robot or the observations of the teammates.

In our implementation every robot fuses all the received measurements.

Every time a new measurement is received, independently of whether it comes from itself or from another robot, it is compared with the existing tracks of the objects. If it is compatible with an existing track, the measurement is added; otherwise, a new track is initialized.

FUSING MULTIPLE OBSERVATIONS(1/6)

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This approach also allows the storage of multiple tracks for a single object; i.e., the creation of multimodal distributions for every object. The real position of the object is decided to be the one with smallest variance, i.e., the one with smaller uncertainty.

For instance, if for some reason two balls are on the field of play, every robot will instantiate two tracks for the ball and will consider the “real” ball the one with the smallest variance (probably the ball closer to it).

FUSING MULTIPLE OBSERVATIONS(2/6)

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Page 26: Cooperation Issues and Distributed Sensing for Multirobot Systems These soccer-playing robots can cooperate and change roles as needed, and their 360 degree

A. Fusing Observations From Different Robots Our system is designed to be totally independent from the number of

robots active on the field. Every robot uses all the measurements

available, independently from the number of teammates.

Fig.6

FUSING MULTIPLE OBSERVATIONS(3/6)

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The first problem is that, in order to combine the different observations, all the robots must share the same spatiotemporal frame of reference.

A second problem is that when an agent is cooperating with other agents, it needs to trust the other agents.

A third problem emerging when working with heterogeneous vision systems running at different speeds is that the measurements arrive at different instants in time.

FUSING MULTIPLE OBSERVATIONS(4/6)

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Given its proximity to the object, this robot will report very accurate measurements which can improve the estimate generated by a robot with a faster, but less precise, vision system.

Fig.7

FUSING MULTIPLE OBSERVATIONS(5/6)

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B. Related ApproachesIn our system , every robot fuses the information coming from the teammates without the need of an external computer.

At the same time, these measures are used to improve the ones made by the robot itself. However, we believe that a robot should not fully trust the information coming from teammates, since they may be in a misleading situation.

FUSING MULTIPLE OBSERVATIONS(6/6)

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Fig.8

EXPERIMENTAL RESULTS(1/3)

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Fig.9 Fig.10

EXPERIMENTAL RESULTS(2/3)

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Fig.11

EXPERIMENTAL RESULTS(3/3)

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In this paper, we have tried to understand how to enhance the cooperative capability of a robot team playing in the RoboCup MSL competitions.

Our current work is a direct evolution of our past experience in designing behavior arbitration which triggers and is triggered by purely stigmergic mechanisms, namely, implicit communication [32], [33].

In the same frame, we have illustrated the implementation of an Omnidirectional Distributed Vision System used to share the information needed for planning the cooperation.

CONCLUSION

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