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Emergent Task Allocation for Mobile Robots Centralized Solution We can define this problem as a mixed-integer linear program Given the robot information and environment properties What is each robot’s ideal next step?

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Emergent Task Allocation for Mobile Robots

Centralized Solution

  • We can define this problem as a mixed-integer linear program
    • Given the robot information and environment properties
    • What is each robot’s ideal next step?
  • Optimal solution can be found at the central server after all information about the system is collected
  • Objective: maximize
    • Target Coverage
  • Constraints
    • Robot Speed
  • Communication
  • Communication Range
  • Area Coverage
  • Sensing Range
  • Exploration
  • Obstacles

Problem Definition

  • Target Coverage
  • Each robot has a sensing range
  • Each target has a coverage requirement
  • A target is covered if necessary number of robots has the target in sensing range
  • Communication
  • Two robots can communicate if
    • They are within communication range of each other
    • There is a series of robots that can provide communication
  • Area Coverage
  • Maximum area coverage is obtained when sensor overlap is minimized
  • Exploration
  • Each robot locates itself outside the explored region

Optimum Distribution

Nuzhet Atay Burchan Bayazit

atay@cse.wustl.edubayazit@cse.wustl.edu

Project Webpage

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

Simulation Results

Task Allocation

Motivation

Emergent Task Allocation

  • 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
  • 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
  • Experiment Scenarios
  • 8 robots 6 targets
  • 20 robots 10 targets
  • 30 robots 15 targets
  • 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
    • Adjust their solutions
  • Final result depends on
    • Information content
    • Number of iterations
      • Converges to global optimal

Comparison of Global and Emergent

Local Information Exchange

Information Exchange Policies

  • Emergent approach is more efficient
    • Computation
    • Communication
  • Approximate solution to global optimal

Solution Quality

  • Intentions
  • Robots send their intended locations to neighbors
  • Each robot assumes these locations are final and finds its optimal
  • Directives
  • Robots send expected locations of neighbors
  • Each robot chooses the best among them
  • Number of targets detected and tracked are the same

Solution Time

  • Emergent approach is 400 times faster than global approach
  • Conclusions
  • 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
  • Intentions and Directives
  • Robots send both their and neighbors’ computed locations
  • Each robot finds the best location using options
  • Intentions, Directives and Targets
  • Robots send their and their neighbors’ locations with possible target assignments
  • Each robot decides a target assignment and finds the best location using options