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Fault-Tolerant Offline Multi-Agent Path Planning

Fault-Tolerant Offline Multi-Agent Path Planning

More Decks by Keisuke Okumura | 奥村圭祐

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  1. Fault-Tolerant Offline Multi-Agent Path Planning
    Keisuke Okumura
    Tokyo Institute of Technology, Japan
    ౦ژ޻ۀେֶ
    5PLZP*OTUJUVUFPG5FDIOPMPHZ
    Feb. 7st – 14th, 2023
    Washington, DC, USA
    AAAI-23
    https://kei18.github.io/mappcf
    Sebastien Tixeuil
    Sorbonne University, CNRS, LIP6,
    Institut Universitaire de France, France
    fault
    solution
    multiple paths
    assuming crashes

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    2
    Motivation
    multi-agent path planning (MAPP) is important
    necessity of building reliable systems
    cutting-edge studies assume perfect agents
    robot faults are common => fault-tolerance
    e.g., mean time between failure of one robot: 125days*
    *https://fr.autostoresystem.com/benefits/reliable
    aws.amazon.com
    path planning where agents may unexpectedly crash at runtime
    MAPPCF (w/crash faults)

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    3
    goal
    start
    graph
    0
    Conventional Solution Concept
    solution

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    4
    1
    Conventional Solution Concept

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    5
    2
    Conventional Solution Concept

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    6
    3
    Conventional Solution Concept
    done!

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    7
    0
    With Unforeseen Crash

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    8
    online replanning
    offline approach: preparing backup paths from the beginning
    or
    crash detected
    => then?
    1
    With Unforeseen Crash
    crashed (forever stop)

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    9
    primary path
    Solution Concept of MAPPCF
    backup path
    when is detected
    transition rule
    &
    0

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    10
    Solution Concept of MAPPCF
    1

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    11
    Solution Concept of MAPPCF
    2

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    12
    Solution Concept of MAPPCF
    more than two agents may crash
    => backup path of backup path
    3
    done!

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    13
    Problem Formulation of MAPPCF
    given
    solution
    s.t. all non-crashed agents eventually reach their destination,
    regardless of crashes (up to f )
    & transition rules
    & maximum number of crashes f
    defined with failure detector & execution model
    centralized planning followed by decentralized execution

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    14
    Failure Detectors
    oracle that tells status of neighboring vertices
    response:
    1. no agent
    query
    2. non-crashed agent
    named FD
    3. crashed agent
    anonymous FD
    unable to identify who crashes
    c.f., [Chandra+ JACM-96]

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    15
    Execution Models
    how agents are scheduled at runtime
    synchronous model
    all agents act simultaneously
    solutions avoid collisions
    MAPF: multi-agent pathfinding
    [Stern+ SOCS-19]
    solution
    solutions avoid deadlocks
    each agent acts spontaneously
    while locally avoiding collisions
    offline time-independent MAPP
    [Okumura+ IJCAI-22]
    sequential model
    (async)
    solution
    possible schedule

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    16
    Model Power Analyses
    SYN + AFD
    SYN + NFD
    SEQ + NFD
    SEQ + AFD
    synchronous model
    sequential model
    named failure detector
    anonymous FD
    SYN
    SEQ
    NFD
    AFD
    strictly stronger
    SEQ+AFD
    SYN+AFD
    solvable instances
    weakly stronger
    SYN+AFD
    SYN+NFD
    SYN+AFD
    SYN+NFD
    or
    solvable in SYN
    unsolvable in SEQ

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    17
    Computational Complexity
    1. finding solutions is NP-hard
    2. verification is co-NP-complete
    regardless of FD types or execution models
    the proofs are reductions from 3-SAT
    MAPPCF is
    computationally
    intractable

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    18
    Solving MAPPCF
    proposal:
    decoupled crash faults resolution framework (DCRF)
    synchronous model + named FD
    number of maximum crashes f =2
    example*
    *DCRF is applicable to other models
    1. find initial paths
    2. identify unresolved events
    3. compute backup path & update solution
    4. back to step-2

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    19
    How DCRF Solves MAPPCF
    find initial paths

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    How DCRF Solves MAPPCF
    identify unresolved events
    unresolved events queue

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    How DCRF Solves MAPPCF
    identify unresolved events
    unresolved events queue

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    22
    How DCRF Solves MAPPCF
    unresolved events queue
    identify unresolved events

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    23
    How DCRF Solves MAPPCF
    resolve event

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    24
    How DCRF Solves MAPPCF
    resolve event

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    How DCRF Solves MAPPCF
    update solution

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    How DCRF Solves MAPPCF
    identify unresolved events

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    How DCRF Solves MAPPCF
    resolve event

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    How DCRF Solves MAPPCF
    resolve event

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    How DCRF Solves MAPPCF
    update solution

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    How DCRF Solves MAPPCF
    identify unresolved events

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    How DCRF Solves MAPPCF
    resolve event

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    How DCRF Solves MAPPCF
    resolve event

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    How DCRF Solves MAPPCF
    update solution

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    How DCRF Solves MAPPCF
    identify unresolved events

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    How DCRF Solves MAPPCF
    empty
    obtain solution
    DCRF is correct but incomplete
    unresolved events queue

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    36
    Empirical Results




    success rate
    #agents
    fixed #crashes: f =1
    SYN
    SEQ






    success rate
    #crashes f
    fixed #agents: 15
    SYN
    SEQ
    solving MAPPCF becomes difficult with more {agents, crashes}
    SEQ is harder than SYN
    random-32-32-10
    32x32 (|V|=922)
    from [Stern+ SOCS-19]
    30sec timeout
    with named FD
    synchronous SYN v.s. sequential SEQ

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    37
    Empirical Results
    MAPPCF provides better solution concept than finding disjoint paths
    random-32-32-10
    32x32 (|V|=922)
    from [Stern+ SOCS-19]
    30sec timeout
    with named FD




    success rate
    #agents
    DCRF/SYN
    disjoint paths







    costs / lower bound
    #agents
    DCRF/SYN
    disjoint paths fixed #crashes: f =1
    adapted from CBS [Sharon+ AIJ-15]
    v.s. finding vertex disjoint paths
    traveling time when no crashes

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    38
    Concluding Remarks
    MAPPCF
    novel path planning problem for multiple agents that may crash at runtime
    fault
    solution
    multiple paths
    assuming crashes
    https://kei18.github.io/mappcf
    future directions:
    complete algorithms, optimization, other types of failure detectors

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