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Emergent Task Allocation for Mobile RobotsPowerPoint Presentation

Emergent Task Allocation for Mobile Robots

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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
- Spread or shrink

- Obstacles

- Real-life scenarios

Nuzhet Atay

Robotic Systems

- Heterogeneous robots with limited
- Speed
- Sensing range
- Communication range

- Multiple robot coordination
- Task allocation

- Goal:
- Optimum assignment of robots

Nuzhet Atay

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

Nuzhet Atay

Outline

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

Nuzhet Atay

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

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

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

Nuzhet Atay

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

Nuzhet Atay

Communication

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

Nuzhet Atay

Exploration

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

Nuzhet Atay

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

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

Nuzhet Atay

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

- Decides a target

Nuzhet Atay

Comparison of Global and Emergent

- Emergent approach is more efficient
- Computation
- Communication

- Approximate solution to global optimal

Nuzhet Atay

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

Nuzhet Atay

Solution - Global

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Solution - Emergent

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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

Nuzhet Atay

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

Nuzhet Atay

Solution Quality

Nuzhet Atay

Solution Quality

Nuzhet Atay

Solution Quality

Nuzhet Atay

Solution Quality

Nuzhet Atay

Solution Quality

Nuzhet Atay

Solution Quality

Nuzhet Atay

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