1. Behavior-based Multirobot Architectures

2. Why Behavior Based Control for Multi-Robot Teams? Multi-Robot control naturally grew out of single robot control Multi-Robot control naturally grew out of single robot control –Reactive: No state information –Planner: State space is already huge  Adding n additional robots to a state space of s results in an state space of s n –Hybrid: Same problems as a planning control system

3. Why Behavior Based Control for Multi-Robot Teams? Behavior Based Control Behavior Based Control Pros: Pros: –Since control is locally situated it scales well –No reliance on global communication or planning results in robots better able to handle sensor and actuator noise –Primitive Behaviors are relatively simple

4. Why Behavior Based Control for Multi-Robot Teams? Cons: Cons: –Difficult to create  Experimental –Difficult to analyze  Actions of robots depend on the actions of other robots  Behavior of the team is based on the interactions between robots instead of an individual robots control strategy

5. Issues in Behavior Based Multi- Robot Control How to create and combine behaviors to accomplish a given goal? How to create and combine behaviors to accomplish a given goal? How to coordinate robot behaviors? How to coordinate robot behaviors? –Use Communication? –What kind of knowledge should the team have?  Purely local control?  Hybrid local and global control?

6. Behavior Creation and Selection Bottom-Up Bottom-Up –Primitive behaviors should be minimalist in that sense that a primitive behaviors can not be derived from other primitive behaviors –Constrained by the robot’s physical capabilities –Constrained by the environment Top-Down Top-Down –Behaviors are constrained by the types of goals the must be accomplished by a team

7. Test Cases Equipment Equipment –20 mobile robots equipped with infra-red sensors, micro-switches, sonar, and radio Evaluation Evaluation –Repeatability –Stability –Robustness –Scalability

8. Test Cases Continued Primitive Behaviors Primitive Behaviors – Avoidance –Following –Aggregation –Dispersion –Homing –Wandering –Grasping / Dropping

9. Test Cases Continued Flocking Flocking –Summation of  Avoidance  Aggregation  Wandering –Addition of Homing for goal directed behavior Results Results –Goal directed behavior without dependence on a leader and robust in case of single robot failure –Flocking Flocking

10. Test Cases Continued Foraging Foraging –Temporally switch between: avoidance, dispersion, following, homing, and wandering Results Results –Basic behaviors were empirically shown to be robust and flexible in collecting pucks and dropping them off at a goal location

11. Reference Mataric, Issues and Approaches in the Design of Collective Autonomous Agents Mataric, Issues and Approaches in the Design of Collective Autonomous Agents

12. Behavior Based Multi-Robot Team Coordination Communication Communication –Often times relies on Master-Slave Hierarchy  Inherent brittleness to this approach –Bandwidth limitations –Robustness - Master failure? –Heterogeneous or Homogeneous approach? –Is explicit communication needed or is implicit communication enough to achieve the goal? –If communications are used how much is needed and what should be communicated?

13. Cooperation Without Communication Is cooperation in a behavior based multi- robot team without communication possible? Is cooperation in a behavior based multi- robot team without communication possible? If so, how effective is it? If so, how effective is it?

14. Behavioral Composition Forage Forage –Noise –Avoid static obstacles –Avoid robots Acquire Acquire –Move to goal –Avoid static obstacles –Noise Deliver Deliver –Move to goal –Avoid static obstacles –Avoid robots –Noise

15. Test Cases Simulation Simulation Map Size: 64 x 64 units Map Size: 64 x 64 units Maximum Sensor Distance: 25 units Maximum Sensor Distance: 25 units Forage Forage –Noise Gain: 1.2 –Noise Persistence: 4 –Avoid Obstacles Gain: 1.0 –Avoid Robots Gain: 0.5 Acquire/Deliver Acquire/Deliver –Noise Gain: 0.2 –Noise Persistence: 2 –Move to Goal Gain: 1.0 –Avoid Robots Gain: 0.1

16. Results 2 Robots / 1 Attractor 2 Robots / 1 Attractor

17. Results 4 robots / 4 attractors 4 robots / 4 attractors

18. Results Without using communication, the simulation still shows coherent cooperation between the team members Without using communication, the simulation still shows coherent cooperation between the team members –Cheaper hardware –Fewer points of failure

19. References Arkin, Cooperation without communication Arkin, Cooperation without communication For more quantitative comparisons between levels of communication see: Balch/Arkin, Communication in Reactive Multiagent Robotic Systems For more quantitative comparisons between levels of communication see: Balch/Arkin, Communication in Reactive Multiagent Robotic Systems

20. Behavior Based Multi-Robot Team Coordination Continued Local versus Global Control Laws Local versus Global Control Laws –Local Control  Simple and contain emergent properties  Oftentimes unclear as to how to design local control laws –Global Control  Allow for more coherent team cooperation  Often results in increased communication requirements

21. Global Control Laws Global Goal Knowledge Global Goal Knowledge –Information concerning the overall goal of the agents behavior –Can be encoded into robot if the goal is not dynamic Global Knowledge Global Knowledge –Information concerning what other robots are doing –Information concerning what other robots will do Obtaining this knowledge must often come from outside sources Obtaining this knowledge must often come from outside sources The knowledge is computationally costly The knowledge is computationally costly Oftentimes all the needed global knowledge is not known Oftentimes all the needed global knowledge is not known

22. Local Control Laws Computationally Simple Computationally Simple Handle dynamic environments well Handle dynamic environments well Oftentimes do not produce optimal results Oftentimes do not produce optimal results Must rely on physical sensors Must rely on physical sensors

23. Experiment Simulation of mission involving formation maintenance while moving to goal Simulation of mission involving formation maintenance while moving to goal 4 different strategies with increasing global control 4 different strategies with increasing global control Quantitatively measured via deviation from the formation and time taken to reach goal Quantitatively measured via deviation from the formation and time taken to reach goal

24. Experiment Continued Strategy I: Local Control Only Strategy I: Local Control Only –Effective for smooth trajectories –Sharp turns cause formation to break up due to local control  Robots maintain their position by staying a fixed distance from a certain side of neighbor

25. Experiment Continued Strategy II: Local Control Augmented by Global Goal Strategy II: Local Control Augmented by Global Goal –Robots given knowledge of global goal: Maintain line formation –Robot D now moves to a more globally appropriate position –May be inappropriate if B is merely avoiding obstacle

26. Experiment Continued Strategy III: Local Control Augmented with Global Goal and Partial Global Information Strategy III: Local Control Augmented with Global Goal and Partial Global Information –At time of robot B’s turn, other robots are informed of destination of waypoint X

27. Experiment Continued Strategy IV: Local control augmented by global goal and more complete global information Strategy IV: Local control augmented by global goal and more complete global information –Robots are given complete knowledge of leaders route –Allows other robots to predict future positions of the leader and resulting positions for themselves

28. Results As global information increases formation error and completion time decreases As global information increases formation error and completion time decreases Global goals are useful to incorporate if goals are known at run time Global goals are useful to incorporate if goals are known at run time Global information is useful in static, well defined environments Global information is useful in static, well defined environments

29. Results Continued Local control in situations where accomplishing the task as opposed to how the task accomplished often provides a suitable approximation of optimal behavior Local control in situations where accomplishing the task as opposed to how the task accomplished often provides a suitable approximation of optimal behavior Behavioral analysis in local control my approximate global knowledge Behavioral analysis in local control my approximate global knowledge General Rule: “Local control information should be used to ground global knowledge in the current situation. This allows the agents to remain focused on the overall goals of their group while reacting to the dynamics of their current situation.” General Rule: “Local control information should be used to ground global knowledge in the current situation. This allows the agents to remain focused on the overall goals of their group while reacting to the dynamics of their current situation.”

30. References Parker, Designing Control Laws for Cooperative Agent Teams Parker, Designing Control Laws for Cooperative Agent Teams

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