Coordination in Multi Robot Systems Motion Planning Shreyans Kumar - - PowerPoint PPT Presentation

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Coordination in Multi Robot Systems Motion Planning Shreyans Kumar - - PowerPoint PPT Presentation

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MIN Faculty Department of Informatics Coordination in Multi Robot Systems Motion Planning Shreyans Kumar Bhansali University of Hamburg Faculty of Mathematics, Informatics and Natural Sciences Department of Informatics Technical Aspects of

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SLIDE 1 MIN Faculty Department of Informatics

Coordination in Multi Robot Systems

Motion Planning Shreyans Kumar Bhansali

University of Hamburg Faculty of Mathematics, Informatics and Natural Sciences Department of Informatics Technical Aspects of Multimodal Systems
  • 18. December 2017
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SLIDE 2

Outline

Motivation Motion Planning Centralized Approach Decentralized Approach Conclusion
  • 1. Motivation
  • 2. Motion Planning
  • 3. Centralized Approach
  • 4. Decentralized Approach
  • 5. Conclusion
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SLIDE 3

Motivation

Motivation Motion Planning Centralized Approach Decentralized Approach Conclusion 1 1<https://www.popsci.com/these-new-motivational-posters-will-get-your-robot-out-its-slump>
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Motivation

Motivation Motion Planning Centralized Approach Decentralized Approach Conclusion

Why do we need coordination in multi-robot systems?

◮ Force multiplication (PRL-based research in multi-rover coordination for conceptual lunar-surface assembly operations. 2 ) 2<https://www-robotics.jpl.nasa.gov/facilities/facility.cfm?Facility=4>
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SLIDE 5

Motivation

Motivation Motion Planning Centralized Approach Decentralized Approach Conclusion ◮ Simultaneous presence (Multi-robot system in an eye-in-hand configuration. 3) 3<http://www.mdpi.com/1424-8220/11/10/9839/htm>
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Motivation

Motivation Motion Planning Centralized Approach Decentralized Approach Conclusion ◮ Faster execution ◮ Redundancy, fault tolerance ◮ Greater efficiency ◮ Larger range of task domains - cooperative manipulation (Cooperative manipulation 4) 4<https://homepages.laas.fr/afranchi/robotics/?q=node/251>
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Applications

Motivation Motion Planning Centralized Approach Decentralized Approach Conclusion ◮ Warehouse management ◮ Competitions - Robot Soccer ◮ Product assembly ◮ Digital entertainment (a) Kiva Systems/Amazon 5 (b) Robot Soccer 6 (c) Clash of Clans 7 Example Applications 5<https://www.youtube.com/watch?v=lTJ1sIqBoro> 6<https://tams.informatik.uni-hamburg.de/lehre/2016ws/seminar/ir/doc/slides/JuliusMayer- Genetic_Algorithms_in_Robotics.pdf> 7<https://www.youtube.com/watch?v=Bzc7P99be9E>
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SLIDE 8

Research

Motivation Motion Planning Centralized Approach Decentralized Approach Conclusion ◮ Multi-robot motion planning ◮ Traffic control ◮ Multi-robot docking ◮ Foraging ◮ Multi-robot soccer ◮ Exploration and localization
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SLIDE 9

Research

Motivation Motion Planning Centralized Approach Decentralized Approach Conclusion ◮ Multi-robot motion planning ◮ Traffic control ◮ Multi-robot docking ◮ Foraging ◮ Multi-robot soccer ◮ Exploration and localization
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Objective

Motivation Motion Planning Centralized Approach Decentralized Approach Conclusion

Enable robots to navigate collaboratively to achieve spatial positioning goals. [6]

Motion planning in dynamic environment with moving obstacles is NP-hard

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SLIDE 11

Approaches

Motivation Motion Planning Centralized Approach Decentralized Approach Conclusion (Motion planning in Multi-robot systems 8) ◮ What information does an approach access? ◮ Global = Centralized ◮ Local = Decentralized 8http://multirobotsystems.org/sites/default/files/slides/2015_RSS_MRS_Bekris.pdf
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SLIDE 12

Approaches

Motivation Motion Planning Centralized Approach Decentralized Approach Conclusion (Motion planning in Multi-robot systems 9)

Key question for Centralized Approaches

◮ What space does the approach search over? ◮ Composite space of all robots = Coupled approach ◮ Individual robot space and co-ordination = Decoupled approach 9http://multirobotsystems.org/sites/default/files/slides/2015_RSS_MRS_Bekris.pdf
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Approaches

Motivation Motion Planning Centralized Approach Decentralized Approach Conclusion (Motion planning in Multi-robot systems 10)

Key question for Decentralized Approaches

◮ How does a local method access information from robots? ◮ Sensing or communication ◮ Inference or shared information 10http://multirobotsystems.org/sites/default/files/slides/2015_RSS_MRS_Bekris.pdf
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Centralized Approach - Coupled

Motivation Motion Planning Centralized Approach Decentralized Approach Conclusion

Treat multiple robot as just one robot Single agent "leader" plans for the entire team

◮ Plan path in configuration space Q = Q1 x Q2 x . . . QN (Centralized approach11) 11<http://www.robotmotionplanning.org/teaching/LecRoboMultiRobots.pdf>
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SLIDE 15

Centralized Approach - Coupled

Motivation Motion Planning Centralized Approach Decentralized Approach Conclusion

Treat multiple robot as just one robot

Click!

12 12<https://www.cs.rutgers.edu/ kb572/pubs/scalable_asympt_opt_multi_robot.pdf>
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Centralized Approach - Decoupled

Motivation Motion Planning Centralized Approach Decentralized Approach Conclusion ◮ First compute individual path for each robot for their

corresponding space Qi

◮ The consider path interaction to produce a solution in

composite space

◮ When successful, they solve problems faster than coupled

approach Various methods for Decoupled approach are:

◮ Prioritized planning ◮ Velocity tuning
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Centralized Approach - Decoupled

Motivation Motion Planning Centralized Approach Decentralized Approach Conclusion (Decoupled approach13) 13<http://www.robotmotionplanning.org/teaching/LecRoboMultiRobots.pdf>
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Centralized Approach

Motivation Motion Planning Centralized Approach Decentralized Approach Conclusion

Advantages:

◮ Single robot motion planning algorithms can be directly applied ◮ Leader can take all information into account ◮ In theory, co-ordination can be perfect ◮ Guarantees probabilistic completeness

Disadvantages:

◮ Computationally hard ◮ Vulnerable to malfunction of leader ◮ Heavy communication load
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SLIDE 19

Decentralized Approach

Motivation Motion Planning Centralized Approach Decentralized Approach Conclusion ◮ Control processing is distributed among agents ◮ Each robot basically independent ◮ Robots use locally observable information to make plans (Decentralized approach14) 14<http://www.robotmotionplanning.org/teaching/LecRoboMultiRobots.pdf>
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SLIDE 20

Decentralized Approach

Motivation Motion Planning Centralized Approach Decentralized Approach Conclusion

Why do need decentralized approach?

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SLIDE 21

Decentralized Approach

Motivation Motion Planning Centralized Approach Decentralized Approach Conclusion

Why do need decentralized approach?

Robust to limits on or loss of communication

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Decentralized Approach

Motivation Motion Planning Centralized Approach Decentralized Approach Conclusion (Multiple robot systems using Dynamic Networks 15) 15http://multirobotsystems.org/sites/default/files/slides/2015_RSS_MRS_Bekris.pdf
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Decentralized Approach

Motivation Motion Planning Centralized Approach Decentralized Approach Conclusion (Multiple robot systems using Dynamic Networks 16) 16http://multirobotsystems.org/sites/default/files/slides/2015_RSS_MRS_Bekris.pdf
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Decentralized Approach

Motivation Motion Planning Centralized Approach Decentralized Approach Conclusion ◮ Every time a new network is

formed, data is exchanged.

◮ Each robot uses its own

centralized motion planner to construct trajectories

◮ After each robot has received

a plan from all other robots, it will implement the best plan.

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Inter-robot communication

Motivation Motion Planning Centralized Approach Decentralized Approach Conclusion

Objective of communication

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Inter-robot communication

Motivation Motion Planning Centralized Approach Decentralized Approach Conclusion

Objective of communication Enable robots to exchange state and environmental information with a minimum bandwidth requirement [4]

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SLIDE 27

Decentralized Approach

Motivation Motion Planning Centralized Approach Decentralized Approach Conclusion

Advantages:

◮ Dimensionality of configuration space does not increase ◮ Faster response to dynamic conditions ◮ Little computation required ◮ Very robust

Disadvantages:

◮ Plans based only on local information ◮ The solutions are often highly sub optimal
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SLIDE 28

Conclusion

Motivation Motion Planning Centralized Approach Decentralized Approach Conclusion
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Conclusion

Motivation Motion Planning Centralized Approach Decentralized Approach Conclusion (17) 17http://multirobotsystems.org/sites/default/files/slides/2015_RSS_MRS_Bekris.pdf
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Conclusion

Motivation Motion Planning Centralized Approach Decentralized Approach Conclusion

Any Question?

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Sources

Motivation Motion Planning Centralized Approach Decentralized Approach Conclusion

[1]

Bekris, K. (2015). Motion Planning in Multi-Robot Systems. [2] Guo, Y. and Parker, L. (2014). Distributed and optimal motion planning for multiple mobile robots. [3] Likhachev, M. (2017). Planning Techniques for Robotics. [4] Plaku, E. (n.d.). Path Planning for Multiple Robots. [5] Black, J. (2015). Multi-Robot Systems. [6] Tang, D. (2016). Multi-Robot Path Planning. [7]
  • C. M. Clark, S. M. Rock and J. C. Latombe, "Motion planning for multiple mobile robots using dynamic
networks," 2003 IEEE International Conference on Robotics and Automation (Cat. No.03CH37422), 2003,
  • pp. 4222-4227 vol.3.
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