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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

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Emergent task allocation for mobile robots l.jpg

Emergent Task Allocation for Mobile Robots

Nuzhet Atay

Doctoral Student Seminar

Advisor : Burchan Bayazit


Motivation l.jpg

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

Nuzhet Atay


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Robotic Systems

  • Heterogeneous robots with limited

    • Speed

    • Sensing range

    • Communication range

  • Multiple robot coordination

    • Task allocation

  • Goal:

    • Optimum assignment of robots

Nuzhet Atay


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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

  • Problem Definition

  • Model

  • Centralized (Global Optimal) Solution

  • Emergent Approach

  • Comparison of two methods

  • Experimental Results

  • Conclusion

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Problem definition l.jpg

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?

Nuzhet Atay


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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

Nuzhet Atay


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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

  • 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

  • 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

  • Maximum area coverage is obtained

    • Sensor overlap is minimized

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Exploration

  • Robots store the places they have visited

  • Each robot tries to locate itself outside the explored region

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Optimum Distribution

  • Sample distribution for maximizing

    • Target coverage

    • Communication

    • Area coverage

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Additional Constraints

  • Environment obstacles

  • Convex

    • Partitioned into convex obstacles

    • Convex box surrounding the obstacle

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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

Nuzhet Atay


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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

  • Robots send their intended locations to neighbors

  • Each robot assumes these locations are final

    • Finds its optimal location

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Directives

  • Robots send expected locations of neighbors

  • Each robot chooses the best among them

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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

  • 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

  • Emergent approach is more efficient

    • Computation

    • Communication

  • Approximate solution to global optimal

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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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Solution - Global

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Solution - Emergent

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Comparison

  • Solution quality is comparable

# of Targets Covered at Each Step

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Evaluation

  • Emergent approach is 400 times faster than global approach

Solution Time

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Scalability

  • Execution time remains constant with larger networks

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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

Motion Planning Group

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

?

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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

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Solution Quality

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Solution Quality

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Solution Quality

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Solution Quality

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Solution Quality

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