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Quick Multi-Robot Motion Planning by Combining Sampling and Search

Quick Multi-Robot Motion Planning by Combining Sampling and Search

More Decks by Keisuke Okumura | 奥村圭祐

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  1. Quick Multi-Robot Motion Planning
    by Combining Sampling and Search
    Keisuke Okumura1,2 & Xavier Defago3
    Macao, 19th – 25th Aug. 2023
    IJCAI-23
    https://kei18.github.io/sssp
    1National Institute of Advanced Industrial Science and Technology (AIST)
    2University of Cambridge
    3Tokyo Institute of Technology

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    2
    MASON TRINCA/THE WASHINGTON POST/GETTY IMAGE
    Kim Kyung Hoon / reuters
    AP Photo/Ross D. Franklin

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    3
    everything happens in real-time;
    neither pre-computed trajectories nor environment representation

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    4
    MRMP: Multi-Robot Motion Planning
    Each robot operates in its own configuration space
    Each robot has initial and goal configurations
    solution: collision-free trajectories
    *not addressed in this study
    in continuous spaces
    allowing heterogeneity
    allowing kinematics/dynamics* constraints
    extremely challenging!

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    5
    connect
    free space
    true
    false
    steer collide
    true
    false
    sample distance
    0.18
    Blackbox Helper Functions
    solve MRMP efficiently only with the five functions

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    6
    Naïve Strategy for MRMP
    two-phase planning
    graph representation of workspace;
    typically done by
    sampling-based motion planning (SBMP)
    compute collision-free paths
    e.g., [Svestka+ 98; Solovery+ IJRR-16; Cohen+ SoCS-19; Solis+ RA-L-21]
    Construct agent-wise roadmaps by some means
    1.
    Solve multi-agent pathfinding (MAPF) on these roadmaps
    2.

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    7
    Pitfall of Two-Phase Planning
    roadmaps truly used in planning process?
    two-phase planning
    Construct agent-wise roadmaps by some means
    Solve multi-agent pathfinding (MAPF) on these roadmaps
    1.
    2.
    decoupled

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    Construct agent-wise roadmaps by some means
    Solve multi-agent pathfinding (MAPF) on these roadmaps
    1.
    2.
    8
    Advanced Strategy
    develop agent-wise roadmaps
    according to MAPF search progress
    coupled
    this study

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    9
    quick MRMP
    multi-robot motion planning
    PRM [Kavraki+ 96]
    EST [Hsu+ ICRA-97]
    RRT [LaValle 98]
    RRT-Connect [Kuffner+ ICRA-00]
    PRM*/RRT* [Karaman+ IJRR-11]
    SPARS [Dobson+ IJRR-14]
    FMT* [Jason+ IJRR-15]
    +
    SBMP:
    sampling-based motion planning
    A*+OD [Standley AAAI-10]
    CBS [Sharon+ AIJ-15]
    M* [Wagner+ AIJ-15]
    PBS [Ma+ AAAI-19]
    BCP [Lam+ COR-22]
    LNS [Li+ IJCAI-21; AAAI-22]
    LaCAM* [Okumura AAAI-23; IJCAI-23]
    +
    MAPF:
    multi-agent pathfinding
    search
    roadmap
    seamless
    integration
    Concept

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    10
    PRM [Kavraki+ 96]
    EST [Hsu+ ICRA-97]
    RRT [LaValle 98]
    RRT-Connect [Kuffner+ ICRA-00]
    PRM*/RRT* [Karaman+ IJRR-11]
    SPARS [Dobson+ IJRR-14]
    FMT* [Jason+ IJRR-15]
    +
    SBMP:
    sampling-based motion planning
    A*+OD [Standley AAAI-10]
    CBS [Sharon+ AIJ-15]
    M* [Wagner+ AIJ-15]
    PBS [Ma+ AAAI-19]
    BCP [Lam+ COR-22]
    LNS [Li+ IJCAI-21; AAAI-22]
    LaCAM* [Okumura AAAI-23; IJCAI-23]
    +
    MAPF:
    multi-agent pathfinding
    Contribution
    seamless
    integration
    search
    roadmap
    example algorithm:
    SSSP
    simultaneous
    sampling-and-search
    planning

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    How SSSP works
    goal
    start
    alternate between vertex and search-node expansions
    exhaustive search while constructing roadmaps by random walks
    [MAPF; Standley AAAI-10] [SBMP; Hsu+ ICRA-97]

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    How SSSP works
    0
    1
    2
    0
    1 2
    3
    initial roadmap construction
    by single-robot SBMP

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    How SSSP works
    0
    1
    2
    0
    1 2
    3
    search tree
    00
    next action

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    How SSSP works
    vertex expansion
    0
    00
    invoked

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    How SSSP works
    vertex expansion
    4 5
    0
    00
    sampling new vertices
    by random walk

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    How SSSP works
    4 5
    0
    00
    1 2
    3
    update roadmap
    vertex expansion

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    How SSSP works
    4 5
    0
    00
    1 2
    3
    search node expansion

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    How SSSP works
    00
    search node expansion
    4 5
    0
    1 2

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    How SSSP works
    search node expansion
    4 5
    0
    1 2
    00 10 20 40 50
    00

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    How SSSP works
    00 10 20 40 50
    00
    closed
    next node

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    How SSSP works
    vertex expansion
    00 10 20 40 50
    00
    0

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    How SSSP works
    vertex expansion
    00 10 20 40 50
    00
    0
    3
    4
    sampling new vertices
    by random walk

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    How SSSP works
    00 10 20 40 50
    00
    0
    3
    4
    update roadmap
    1
    2

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    How SSSP works
    00 10 20 40 50
    00
    0
    3
    4
    1
    2
    search node expansion

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    How SSSP works
    search node expansion
    0
    3
    4
    1
    00 10 20 40 50
    00

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    How SSSP works
    50 51 53 54
    00 10 20 40 50
    00
    search node expansion
    0
    3
    4
    1

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    How SSSP works
    50 51 53 54
    00 10 20 40 50
    00
    next node
    continue until
    all agents reach their goal
    Combined with some techniques, SSSP eventually finds a solution for solvable instances
    c.f., probabilistic completeness in SBMP

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    Demo & Evaluation

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    SSSP is applicable to various problems thanks to the minimum assumptions

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    30








    runtime (sec)
    solved instances (%)
    Point2d
    DOF: 2N
    - 100 instances with 10 random seeds
    - each instance is randomly generated
    - number of robots: 2–10
    - heterogeneous robots
    Comparison with
    Standard Approaches

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    RRT-Connect [Kuffner+ ICRA-00]
    RRT [LaValle 98]
    PRM [Kavraki+ 96]
    CBS [Sharon+ AIJ-15] on PRMs
    (with adjusted heuristics)
    PP [Silver AIIDE-05] on PRMs
    SSSP
    two-phase planning
    direct application of SBMP








    runtime (sec)
    solved instances (%)
    Point2d
    DOF: 2N
    - 100 instances with 10 random seeds
    - each instance is randomly generated
    - number of robots: 2–10
    - heterogeneous robots
    Comparison with
    Standard Approaches

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    runtime (sec)
    solved instances (%)
    Point2d
    DOF: 2N
    Point3d
    DOF: 3N
    Line2d
    DOF: 3N
    Capsule3d
    DOF: 6N
    Arm22
    DOF: 2N
    Arm33
    DOF: 6N
    Dubins2d
    DOF: 3N
    Snake2d
    DOF: 6N

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    runtime (sec)
    solved instances (%)
    SSSP can quickly solve various MRMP

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    Concluding Remarks
    https://kei18.github.io/sssp
    SSSP
    example algorithm combining sampling and search for quick multi-robot motion planning
    future directions
    improving solution quality (current: not excellent)
    incoporating dynamics
    further integration of SBMP and MAPF algorithms

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