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Emergent Task Allocation for Mobile Robots Nuzhet Atay Doctoral Student Seminar Advisor : Burchan Bayazit Motivation Given an unknown environment and a swarm of mobile robots Achieve some goals under a set of constraints Explore the environment Regions of interest Dynamic Unpredictable

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emergent task allocation for mobile robots

Emergent Task Allocation for Mobile Robots

Nuzhet Atay

Doctoral Student Seminar

Advisor : Burchan Bayazit

motivation
Motivation
  • Given an unknown environment and a swarm of mobile robots
  • Achieve some goals under a set of constraints
    • Explore the environment
    • Regions of interest
      • Dynamic
      • Unpredictable
      • Spread or shrink
    • Obstacles
  • Real-life scenarios

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robotic systems
Robotic Systems
  • Heterogeneous robots with limited
    • Speed
    • Sensing range
    • Communication range
  • Multiple robot coordination
    • Task allocation
  • Goal:
    • Optimum assignment of robots

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planning and task allocation
Planning and Task Allocation

Task

Distribution

Task

Distribution

  • Multi-robot systems require efficient and accurate planning
  • Global optimal solutions are expensive
    • communication overhead
    • planning time
  • Our Solution: An emergent approach
    • Emergent: Solution results from interactions of robots
    • Local approximation to global optimal
    • Low cost and feasible in real-time

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outline
Outline
  • Problem Definition
  • Model
  • Centralized (Global Optimal) Solution
  • Emergent Approach
  • Comparison of two methods
  • Experimental Results
  • Conclusion

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problem definition
Problem Definition
  • Objective is to assign robots to
    • Cover regions of interest
    • Provide communication between all robots
    • Control maximum total surface
    • Explore new regions
  • We can define this problem as an optimization problem
    • Given the robot information and environment properties
    • What is each robot’s ideal next step?

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model
Model
  • Robots
    • Constant communication and sensing range
    • Limited speed
  • Regions of interest
    • Targets that need to be tracked by the robots
    • Several robots may be needed
  • Input:
    • Information about the robots and the environment
    • Expected target positions after n steps
  • Output
    • Optimum locations of robots

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centralized solution
Centralized Solution

Task Assignment

Information Collection

  • Problem is defined as a mixed-integer linear program
    • Non-linear constraints
    • Flexible
    • Easy to customize
  • Objective: maximize
    • Target Coverage
    • Communication
    • Area Coverage
    • Exploration

R7

T3

R8

R9

R6

T1

R4

R5

R3

T2

R1

R2

Central

Server

Task Allocation is Determined

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target coverage
Target Coverage
  • Each robot has a sensing range
  • Each target has a coverage requirement
  • A target is covered
    • Necessary number of robots has the target in sensing range

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communication
Communication
  • Two robots can communicate
    • If they are within communication range of each other
    • There is a series of robots that can provide communication

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area coverage
Area Coverage
  • Maximum area coverage is obtained
    • Sensor overlap is minimized

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exploration
Exploration
  • Robots store the places they have visited
  • Each robot tries to locate itself outside the explored region

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optimum distribution
Optimum Distribution
  • Sample distribution for maximizing
    • Target coverage
    • Communication
    • Area coverage

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additional constraints
Additional Constraints
  • Environment obstacles
  • Convex
    • Partitioned into convex obstacles
    • Convex box surrounding the obstacle

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problems of global approach
Problems of Global Approach
  • Solution is not feasible with large number of robots
    • Solving mixed-integer linear program is NP-Hard
  • Central server
    • Too much data transfer
  • Our solution:
    • Solve small local problems
    • Integrate to approximate optimal solution
  • Advantage is to avoid
    • Communication overhead
    • Exponential computation time

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emergent task allocation
Emergent Task Allocation

Find a Solution with Local Information

Information Sharing

  • Robots find optimal solutions with local information
  • Each robot has different information about
    • Robots in the environment
    • Targets to be tracked
    • Environment properties
  • Solutions are different
    • Independent suboptimal solutions
  • To find better solution, robots
    • Exchange information
    • Recompute their solutions
  • Final result depends on
    • Information content
    • Number of iterations

T3

R7

R8

R9

R6

T1

R4

R5

R3

T2

R1

R2

Recompute Solution with Updated Information

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intentions
Intentions
  • Robots send their intended locations to neighbors
  • Each robot assumes these locations are final
    • Finds its optimal location

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directives
Directives
  • Robots send expected locations of neighbors
  • Each robot chooses the best among them

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intentions and directives
Intentions and Directives
  • Robots send both their and neighbors computed locations
  • Each robot finds the best location using options

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intentions directives and target
Intentions, Directives and Target
  • Robots send
    • Their and their neighbors’s locations
    • Possible target assignments
  • Each robot
    • Decides a target

assignment

    • Finds the best location

using options

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comparison of global and emergent
Comparison of Global and Emergent
  • Emergent approach is more efficient
    • Computation
    • Communication
  • Approximate solution to global optimal

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experiments
Experiments
  • How well emergent performs?
    • Comparison to global
    • Experiment scenario
      • 8 robots
      • 6 targets
      • 3 obstacles
  • How scalable is the emergent?
    • 20 robots – 10 targets
    • 30 robots – 15 targets

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comparison
Comparison
  • Solution quality is comparable

# of Targets Covered at Each Step

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evaluation
Evaluation
  • Emergent approach is 400 times faster than global approach

Solution Time

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scalability
Scalability
  • Execution time remains constant with larger networks

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conclusion
Conclusion
  • Planning framework for multi-robot task allocation
  • Low communication cost and suitable for real-time applications
  • 400 times faster than the global optimal solution
  • Comparable solution
  • Future work:
    • Different negotiation strategies
    • Implementation on real robots
    • Different utility functions

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questions
Questions

Motion Planning Group

http://www.cse.wustl.edu/~bayazit

?

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convergence
Convergence
  • ETA approaches to CGO after finite number of steps
  • Observation:
    • If all robots find the same solution, then this solution is the same as CGO
  • At each step
    • Robots find a solution
    • Exchange information and negotiate
  • Assuming all state information is shared
    • Robots will have information about other robots’ views
  • After p steps
    • All robots have the same information and find the same solution

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solution quality
Solution Quality

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solution quality32
Solution Quality

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solution quality33
Solution Quality

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solution quality34
Solution Quality

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solution quality35
Solution Quality

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Solution Quality

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