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Navigation for a team of agents

Navigation for a team of agents

「エージェント群のナビゲーション」
招待講演(2h)@組合せ遷移・第27回セミナー資料
https://core.dais.is.tohoku.ac.jp/report/event/detail/---id-124.html

More Decks by Keisuke Okumura | 奥村圭祐

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  1. !" #$ !"#$%& '()#&* ౦ژ޻ۀେֶ 5PLZP*OTUJUVUFPG5FDIOPMPHZ !"#$%&'()*+",-% Jul. 11th, 2022

    27th CoRe Seminar
  2. /123 2 !"#$%& '()#&* +,-. (2020–) /01234DEFAGO Xavier 15 https://kei18.github.io/

    @_kei18 !"#$%&' ()*+,-./0123456789 AI & Robotics / Multi-Agent Planning / Multi-Robot Coordination Keisuke Okumura | !"#$ JSPS DC1 (20–) YKK Doctor21 (20–) Miyake Lab, TokyoTech (16–20) NEC R&D (18) OMRON SINIC X (21) LIP6, Sorbonne Univ. (22) career / intern Next…?
  3. /123 3 Opening

  4. /123 4 !"#$%&'()*&+, -./01234 YouTube/Mind Blowing Videos :,;<=>; YouTube/WIRED ?@ABC+DE.

    YouTube/Tokyo 2020 *.FG (+,-./)
  5. /123 5 67 89 :; <=>?@ AB CD !"#$ 5+6789"&:";<%

    EFGHGI><=>JKLMNO>&P>QRSTUVWGX>YZ[G\]ULMGI^_ %&
  6. /123 6 `a %&'( TUVWGX )*+,+- .#/0 HIJK !"#$%&=>8?@%A%B &

    CD
  7. /123 7 `a1 %&'( `a2 LMNOP QR'SP !"#$%&'()*+",-% )*+,+- .#/0

    HIJK T#P U2VWXYZ
  8. /123 8 ./!"#$%&'0 123456789:;<=>?@ bcRdef>gGhQGi>jkAl^m nop>%qr>sta uGvJuwxN>JyOX>zVbTGX … [\]^ _`.a.b

    Jcde 1 2
  9. /123 9 [Zhang 18] {|}b~U•€G YouTube/StarCraft •hg‚Uf [van den Berg+

    ISRR-11] NFƒi•w]zU•€G [Song+ ICCBB-01] „… [Flatland Challenge, AIcrowd] †‡ YouTube/Mind Blowing Videos JVOLMNO [Zhang+ SIGGRAPH-20] ˆ‰R [Le Goc+ UIST-16] eGdU{WUO '()*+,-./0123(4%56789 :;<=0>*.?*@A-B0
  10. /123 10 EFGHIJKLMN 3456786, 9:;.<=>, ?@ABCD+EFGHI>FGJB KLMN+OPEQRSLTU+V WXYZ / C[\]^_:`aVbcB8d

    efg:`hij)CLklmZ>FGJB
  11. /123 11 Outline C[nOP>E.# opqr>E)*+,+- DskKLMN+Otuvw MAPF; Multi-Agent Path Finding

    /123
  12. /123 12 Outline C[nOP>E.# opqr>E)*+,+- DskKLMN+Otuvw MAPF; Multi-Agent Path Finding

    /123
  13. /123 14 MAPF: Multi-Agent Path Finding given agents (starts) graph

    goals solution paths without collisions
  14. /123 15 OPQ8RS T CDUVR fgb`?hSJcijk1lm%&'no# NP pq [Nebel ICAPS-20]

    rgb`?hS pebble motion problem sktu7 k#$vw O(n^3) xs)ysJcijk1z{hy% [Kornhauser 84, Röger+ SOCS-12, Yu+ WAFR-15] n: |}~, F•/ €•‚ƒZhk„%* …†S 3-SAT ‡ˆs‰Šu7 (‹Œ) wikipedia *0123 8•Ž••‘’“fgb`?h$vw€•‚ƒZhk„% [Boeta+ JAIR-18]
  15. /123 16 wikipedia NxN ”•–s—˜d~slkSNPpq [Ratner+ AAAI-86] WXY,Z[\] qŽ™š, 8•Ž

    (M›) MAPF Sxœ1•ž
  16. /123 17 WXY8^_`a length: k ring makespan: k+1, sum-of-costs: 2k+3

    ƒŸx7 makespan: k+2, sum-of-costs: k+6 ƒŸx7 k > 3 h¡ƒ—¢£SHi [Yu+ AAAI-13] ¤. 1. last arrival time (aka. makespan) 2. total arrival time (aka. sum-of-costs, flowtime) 3. total distance G,¥+ 4. max distance
  17. /123 18 OPQ8RS T WXY 456 3-SAT 789:;<= makespan sum-of-costs

    total distance max distance [Surynek AAAI-10, Yu+ AAAI-13, Yu RA-L-15, Ma+ AAAI-16] —¦£Sž§¨ NP pq bB©ª«h makespan, sum-of-costs, total distance —¦£S NP pq [Banfi+ RA-L-17, Geft+ AAMAS-22] makespan —¦£5¬Ž¨ 4/3 -«s®¯k1°%&'S NP pq [Ma+ AAAI-16]
  18. /123 19 (xy=;V) bcd/ MAPF 2 eLRfghMN,iJL

  19. /123 20 CjkZ[RLlLm\n

  20. /123 21 opqrstuvwx y"yz"{ |}z"{ ~•z"{ +z{|L_

  21. /123 22 opqrstuvwx; PP: Prioritized Planning D._–, ±9, ™&™&²9k, J³´,

    Ž‡ŽHµ¶ *+,-./5·¸¹º1»¼¨% 1. ·¸¹º¹5½¾¿ÀÁŸà nÄu7Å9·¸¹º1ÆÀ*+,-./sÁÂ'sÇÈ1É„% 2. ÊËV¹ÌÍ„hÆk„V9 Î]¤: HCA*: Hierarchical Cooperative A* [Silver AIIDE-05] >?@AB3CDEFGH (A* @IJKLMN )@OPQRSTUVWX3YZN Ïд5ѳÒv%&'Æ€9 [Wang+ JAIR-11, Bnaya+ ICRA-14] Ó—¢ÔHµ¶Ôű [Erdmann+ 87] ÕÖ
  22. /123 23 ·¸¹ºs»¼s×Ø Ù¼7, ÚÛ7e, ÜÝ+B;<=©>, Þ»¼¨, »¼no1ß+à, áâãä, etc

    [Azarm+ ICRA-97, Bennewitz+ 02, van Den Berg+ ICRA-05, Andreychuk+ AAMAS-18, Ma+ AAAI-19] RPP: Revisit Prioritized Planning [Cap+ T-ASE-15] well-formed V•.;F.;h$vwµ¶K1å… opqrstuvwx PP 8€*?{ Ðæ´V_`.a.b'sçK#²9 [Velagapudi+ IROS-10, Cap+ T-ASE-15] well-formed ill-formed è*+,-./s;F+/Ôé+–1 MˆV9ÁÂ#êVë'ƽÀSìíž%
  23. /123 24 y"yz"{ ’“•‘b`?s–+_Ðk1ѳ: BIBOX [Surynek ICRA-09, Surynek FLAIRS-09] ••‘Ô’“•‘Vfgb`?îsïð

    [Botea+ JAIR-18] Ó—¢Ôµ¶ÔñűÔ8•ŽÁÂsòS🤔 b`?1óô£, 9ëÀ‡sõ.ö+÷./5Ðk [Ryan JAIR-08] ¶øùsѳ [Peasgood T-RO-08] Push&Swap, Push&Rotate [Luna+ IJCAI-11, de Wilde+ AAMAS-13] _©DÝ6úh*+,-./11¾¿Àé+–5g‡û¨)‡ž üÖ5ýþ¨;ÿ©_6úh2¾s*+,-./sº!1"v#$% %~≥3s&+ª +Ys'(&+ªx2 )*: +,Jc [Sajid+ SOCS-12], ÐæJc [Wiktor+ IROS-14, Wei+ 14, Zhang+ DARS-16, Wang+ RA-L-20, etc] TASS: Tree-based Agent Swapping Strategy [Khorshid+ SOCS-11] Æ¡þušV-•.-
  24. /123 25 ~•z"{ —¢Ôµ¶ uëNˆv¨9%/0î12Ž¨3³4–5hkë CSP: 6789/0 [Ryan ICRA-10] SAT:

    89ijK/0 [Surynek PRICAI-12, Surynek+ ECAI-16, Surynek+ IJCAI-19] ILP: :~ŸÃ/0 [Yu+ T-RO-16] ASP: k;<_:b`=.b [Erdem+ IJCAI-13] BCP: Branch-and-cut-and-price [Lam+ COR-22]
  25. /123 26 |}z"{ —¢Ôµ¶ >?V@$: A* 1¢ýÒA% 1ÀsWX&+ª1 { ž§¨s*+,-./sº!

    } 5BýÒAvw99 8•ŽWXYZ#2yžC¨d5D$V9 A* with Operator Decomposition & Independent Detection [Standley AAAI-10] MAPF EFsGÍ„H I"Ž9JZKL1M" + ÏÐ/0îsл ^ƒ}h—ÆEFÒv¨9% & N9Od#99s# CBS: Conflict-based Search [Sharon+ AIJ-15]
  26. /123 27 CBS: Conflict-based Search[Sharon+ AIJ-15] —¢k! cost: 5 P*+,-./#

    “9ÀÔÊ&” 1Nû¨9„Vs‡1WXž% 2QRsWX constraint tree sST high-level: low-level: 675Uš—˜ÁÂ1WX t=1 cost: 5 replan stay t=1 cost: 6 replan t=1 t=2 stay cost: 6 replan t=1 t=2 stay cost: 6 replan >?9[\]^_` [Boyarski+ IJCAI-15, Boyarski+ AAAI-21] abcd2OPQRSTUVW [Felner+ ICAPS-18, Li+ IJCAI-19] efgh [Gange+ ICAPS-19] ijk9lm [Li+ AIJ-21] nopq [Boyarski+ IJCAI-20] ML X9rstRVu [Huang+ AAAI-21] vwxDyz{|9}~ (E)ECBS [Barer+ SoCS-14, Li+ AAAI-21] ”ÿ?–Vïð#ìí, e.g., •L€C•‚ƒ„……
  27. /123 28 •‚z"{ Ó—¢ÔHµ¶ ès4–5+1Nû¨VW.+F1;m% 1. *+,-./sX)1ãä PF•Y;<©_, %s->DE.1Z[ž%öBD+1ú% 2.

    öBD+1P*+,-./5¢³Ž¨_`.a.b 3. [Damani+ RA-L-21] A. "\.+Fs]Ÿ B. ãä^.–Ô ?:+s]Ÿ Š‹&P [Sartoretti1+ RA-L-19] GNN [Qingbiao+ IROS-20] … ÕÖ
  28. /123 29 Dy %&'()*+, ƒ„y…†‡ˆ8‰#;<A%B- with MAPF Benchmark [Stern+ SOCS-19]

    example, 194x194; 13,214 *•L€%†L„…‡vˆ2‰Š‹` ./01*+, ~100ŒQ•Ž••-‘’ %“”•–—€˜™Ll`… ≥1000ŒQ•Ž••-‘š
  29. /123 30 Dy %&'()*+, ƒ„y…†‡ˆ8‰#;<A%B BCP [Lam+ COR-22] CBS [Sharon+

    AIJ-15, Li+ AIJ-21] ./01*+, ~100ŒQ•Ž••-‘’ %“”•–—€˜™Ll`… ≥1000ŒQ•Ž••-‘š
  30. /123 31 Dy %&'()*+, ƒ„y…†‡ˆ8‰#;<A%B EECBS [Li+ AAAI-21] HCA* [Silver

    AIIDE-05] BCP [Lam+ COR-22] CBS [Sharon+ AIJ-15, Li+ AIJ-21] ./01*+, ~100ŒQ•Ž••-‘’ %“”•–—€˜™Ll`… ≥1000ŒQ•Ž••-‘š
  31. /123 32 Dy %&'()*+, ƒ„y…†‡ˆ8‰#;<A%B Push & Swap/Rotate [Luna+ IJCAI-11,

    de Wilde+ AAMAS-13] EECBS [Li+ AAAI-21] HCA* [Silver AIIDE-05] BCP [Lam+ COR-22] CBS [Sharon+ AIJ-15, Li+ AIJ-21] ./01*+, ~100ŒQ•Ž••-‘’ %“”•–—€˜™Ll`… ≥1000ŒQ•Ž••-‘š
  32. /123 33 Dy %&'()*+, ƒ„y…†‡ˆ8‰#;<A%B Push & Swap/Rotate [Luna+ IJCAI-11,

    de Wilde+ AAMAS-13] EECBS [Li+ AAAI-21] HCA* [Silver AIIDE-05] BCP [Lam+ COR-22] CBS [Sharon+ AIJ-15, Li+ AIJ-21] & s ` • . 1 _ 78 9 ` ./01*+, ~100ŒQ•Ž••-‘’ %“”•–—€˜™Ll`… ≥1000ŒQ•Ž••-‘š
  33. /123 34 Dy %&'()*+, ƒ„y…†‡ˆ8‰#;<A%B Push & Swap/Rotate [Luna+ IJCAI-11,

    de Wilde+ AAMAS-13] EECBS [Li+ AAAI-21] HCA* [Silver AIIDE-05] BCP [Lam+ COR-22] CBS [Sharon+ AIJ-15, Li+ AIJ-21] PIBT [Okumura+ AIJ-22] my work! ./01*+, ~100ŒQ•Ž••-‘’ %“”•–—€˜™Ll`… ≥1000ŒQ•Ž••-‘š
  34. /123 35 Priority Inheritance with Backtracking for Iterative Multi-agent Path

    Finding planning KO, Manao Machida, Xavier Defago & Yasumasa Tamura https://kei18.github.io/pibt2 IJCAI-19 => AIJ-22 MAPF 1abŽkë;C+`A–VÓ—¢-–éB•Y (PIBT) ¶*+,-./#c´d5efž%&'1å… ≥500TUVWGX>50msŒ• Ž•••bg‘’“ gGFeG”•
  35. /123 36 locations at t=1 t=2 t=3 repeat one-timestep prioritized

    planning high low mid How PIBT works – 1/8 … 1 2 3 4 5 6 7 8 9 decision order time-window
  36. /123 37 How PIBT works – 2/8 simple prioritized planning

    is incomplete high low mid stuck
  37. /123 38 How PIBT works – 3/8 high low mid

    as high priority inheritance [Sha+ IEEE Trans Comput-90]
  38. /123 39 high low mid How PIBT works – 4/8

    1 3 2 decision order … …
  39. /123 40 How PIBT works – 5/8 high as high

    as high as high as high stuck but still not feasible
  40. /123 41 How PIBT works – 6/8 invalid valid re-plan

    re-plan valid You can move invalid You must re-plan, I will stay introduce backtracking
  41. /123 42 Proof sketch. highest as high as high ›9œsW{6•žŸ

    ‚¡… ¢£Y9¤¥¦Qu@Ÿ §L¨“C”©ª œsW{9 D«9¦Qu invalid valid ¬QTQ-®•6¯L°±² How PIBT works – 7/8 +,³ g0: ’“•‘b`?hhS—Å·¸¹ºs*+,-./#ijs'(&+ª5()ij
  42. /123 43 g0: ’“•‘b`?hhS—Å·¸¹ºs*+,-./#ijs'(&+ª5()ij How PIBT works – 8/8 kl:

    (reachability) ž§¨s*+,-./#fmƒZhc´d5efž% +)´V·¸¹º»¼ n•é+–5eŠŽ¨9V9*+,-./#Å9·¸o1ÆÀuš5ž% 9À‡—Å·¸¹º1ÆÀ => g0s¢³
  43. /123 44 25!"#$"#, %&'(30), *!+#,"-./01232, 194x19445678 PIBT(+) PIBT (+) PIBT

    (+) PIBT(+) S'¨Æ;C+`A– & ™&™&²9k !"#$%&'( )!"#*'*&'( !"#+,&'( )!"#$%&'( )!"#-./0 ksò ŸpƒZ {qr PIBT8QV
  44. /123 45 Multi-agent Pickup & Delivery Sushi

  45. /123 46 Dy %&'()*+, ƒ„y…†‡ˆ8‰#;<A%B Push & Swap/Rotate [Luna+ IJCAI-11,

    de Wilde+ AAMAS-13] EECBS [Li+ AAAI-21] HCA* [Silver AIIDE-05] BCP [Lam+ COR-22] CBS [Sharon+ AIJ-15, Li+ AIJ-21] PIBT [Okumura+ AIJ-22] ./01*+, ~100ŒQ•Ž••-‘’ %“”•–—€˜™Ll`… ≥1000ŒQ•Ž••-‘š
  46. /123 47 Dy %&'()*+, ƒ„y…†‡ˆ8‰#;<A%B Push & Swap/Rotate [Luna+ IJCAI-11,

    de Wilde+ AAMAS-13] EECBS [Li+ AAAI-21] HCA* [Silver AIIDE-05] BCP [Lam+ COR-22] CBS [Sharon+ AIJ-15, Li+ AIJ-21] PIBT [Okumura+ AIJ-22] iterative refinement [Okumura+ IROS-21] large neighborhood search [Li+ IJCAI-21] my work! ./01*+, ~100ŒQ•Ž••-‘’ %“”•–—€˜™Ll`… ≥1000ŒQ•Ž••-‘š
  47. /123 48 planning IROS-21 Iterative Refinement for Real-Time Multi-Robot Path

    Planning KO, Yasumasa Tamura & Xavier Defago ijsk1"\5'û¨s%tu1cš?v+Yÿ+> wìs MAPF -–éB•Y1xy´5z³ https://kei18.github.io/mapf-IR 300*+,-./ ŸpƒZ ({) õ;/ / «|
  48. /123 49 Concept – 1/4 }a4E5~;8d•€•Gh ‚ƒ„gJB …†E‡sˆa~‰6Š~ }‹0 }~k1Ó—¢4–5hű5lm%

    1. - 9ëÀ‡*+,-./1Z[ - —¢4–51Nû¨Z[Òv8 *+,-./sÁÂ1tu -«1abž: 2. agent goal
  49. /123 50 Concept – 2/4 agent goal wait }~k1Ó—¢4–5hű5lm% 1.

    - 9ëÀ‡*+,-./1Z[ - —¢4–51Nû¨Z[Òv8 *+,-./sÁÂ1tu -«1abž: 2. }a4E5~;8d•€•Gh ‚ƒ„gJB …†E‡sˆa~‰6Š~ }‹0
  50. /123 51 Concept – 3/4 agent goal wait }~k1Ó—¢4–5hű5lm% 1.

    }a4E5~;8d•€•Gh ‚ƒ„gJB …†E‡sˆa~‰6Š~ }‹0 - 9ëÀ‡*+,-./1Z[ - —¢4–51Nû¨Z[Òv8 *+,-./sÁÂ1tu -«1abž: 2.
  51. /123 52 Concept – 4/4 agent goal –—˜™Yš›^kœ•’MAPFcRžb‰fœŸ ¡¢£¤¥¦ }~k1Ó—¢4–5hű5lm%

    1. }a4E5~;8d•€•Gh ‚ƒ„gJB …†E‡sˆa~‰6Š~ }‹0 - 9ëÀ‡*+,-./1Z[ - —¢4–51Nû¨Z[Òv8 *+,-./sÁÂ1tu -«1abž: 2.
  52. /123 53 Dy %&'()*+, Push & Swap/Rotate [Luna+ IJCAI-11, de

    Wilde+ AAMAS-13] EECBS [Li+ AAAI-21] HCA* [Silver AIIDE-05] BCP [Lam+ COR-22] CBS [Sharon+ AIJ-15, Li+ AIJ-21] PIBT [Okumura+ AIJ-22] iterative refinement [Okumura+ IROS-21] large neighborhood search [Li+ IJCAI-21] Š‹"Œ/ MAPF 8W•Ž ~•¾34sbB©ª€5mkž%Vˆ 10{Æ$vw•Ms`©_/©_h‡V7²9k#°ˆv% ./01*+, ~100ŒQ•Ž••-‘’ %“”•–—€˜™Ll`… ≥1000ŒQ•Ž••-‘š
  53. /123 54 MAPF 8••‘’

  54. /123 55 MAPD: Multi-agent Pickup & Delivery delivery loc. pickup

    loc. given agents graph package ‚ƒs„…†‡14Ž8/0]k [Ma+ AAMAS-17] solution paths without collisions task assignment PIBThˆ5k„% / Æ'Æ'S‰.`•.s]k•#‰?`•.s]kÆ [Liu+ AAMAS-19]
  55. /123 57 Unlabeled/Anonymous MAPF given agents (starts) graph targets solution

    paths without collisions target assignment
  56. /123 58 Unlabeled-MAPF ,C,Rt“”L G•>;”.—¢£S—2?:+/0îsŠ‹5u7€•‚ƒZhk„%?! [Yu+ WAFR-13] unlabeled-MAPF •.;F.; source

    sink t=0 t=1 time expanded network512 Œs •ŽS ž§¨1 ¤$w Ford-Fulkerson s-–éB•Yh—2?:+/01kë' O( *+,-./~ x |}~ x G•>;”.) —2?:+: 1 Jcijk#V9 source sink t=0 t=1 t=2 —2?:+: 2 makespan—¢Vk
  57. /123 59 Unlabeled-MAPF f•–N—kRtKL ŸpShy%#ƒZ'G^B1•š *assuming |𝐸| = 𝑂(|𝑉|) MAPF

    benchmarks [Stern+ SOCS-19] 418x530 43,151 257x256 28,178 194x194 14,784 |𝑉| [Yu+ WAFR-13] G•>;”.—¢kS 𝑂( 𝐴 ⋅ 𝑉 ()* hln%
  58. /123 60 planning execution Solving Simultaneous Target Assignment and Path

    Planning Efficiently with Time-Independent Execution KO & Xavier Defago ˆdUG§¨ ©ªt«¬ ≥1000TUVWGX 1-Œ• F+•©/»¼'ÁŸÃ1¡ƒ5këÓ—¢Ôµ¶V-–éB•Y(TSWAP) ‰?`•.Ô‰.`•.ŸÃs‘’hN$% https://kei18.github.io/tswap The Best Student Paper Award! ICAPS-22
  59. /123 61 Proposed Algorithm: TSWAP ijsF+•©/»¼1lm% Step 1. dU‚xX®8¯m^°±1OLxEkEFGHGI®²³´ Step

    2. 𝑂( 𝐴 ( ⋅ 𝑑𝑖𝑎𝑚 𝐺 ⋅ (𝛼 + 𝛽)) ´µgh+¶Vu¬VW·) ÕÖ 2000 agents [Yu+ WAFR-13] optimal algorithm TSWAP 56.3 89.8 230.2 0.4 0.4 1.1 1.36 1.17 1.02 ŸpƒZ({) Ó—¢K (makespan) lak303d den520d brc202d “”¤
  60. /123 62 swap targets rotate targets shortest path move shortest

    path TSWAP F+•©/1•2ŽV#ˆ1;<©_s_`.a.b1abž Step 2. stay currently assigned target stay (otherwise) 456¸T•-¹{º‘@Z», XY€-•¼{½
  61. /123 63 2y9*+,-./ [Thomas+ 15, Li+ AAAI-19, Atzmon+ SOCS-19] Any-angle

    MAPF [Yakovlev&Andreychuk ICAPS-17] Multi-Goal [Surynek AAAI-21, Zhang+ AAMAS-22] ˜8™8 MAPF 8š›Kœ (un)-labeled MAPF1 ½–£: TAPF [Ma+ AAMAS-16] *+,-./# —v%‡ÆŽvV9MAPF [Atzmon+ JAIR-20, Atzmon+ ICAPS-20, Shahar+ JAIR-21] 1 2 1, 2 3 4 ()ƒZ5Ir1M" [Peltzer+ 19] MAPFR : Œ5“˜1M" [Walker+ IJCAI-18] 3 2 •™ƒZ: Continuous MAPF [Andreychuk+ AIJ-22] VÊVÊ
  62. /123 64 http://mapf.info/ ¬•ž%š›œ• IJCAI, AAAI AAMAS ICAPS ICRA, IROS,

    WAFR SOCS general AI ž–à*+,-./ AI_`.a.b :Ÿ<=>; <AWX … š¡sõ=Ýa<= 8¢ËìíŽV9… £Ñß•/ •–N•žKL] https://github.com/Kei18 5¤¥¦§Ž¨nž ¤¥¨Ëh˜89
  63. /123 65 Outline C[nOP>E.# opqr>E)*+,+- DskKLMN+Otuvw MAPF; Multi-Agent Path Finding

    /123
  64. /123 66 Failure Demo of PIBT

  65. /123 67 access:. 6th Jun. 2022 https://www.ft.com/content/aaddf4b1-a78b-4289-b42f-fd3f5cd7f176 “appears to have

    been caused by the collision of three bots on the grid” “more than 1,000 robots buzz around a grid, stopping to grab crates of food” CDEF9GDH
  66. /123 68 IJK./F078 LM-N-O PQPRSTUKL µ¶, ObxE, ·G¸UTFU, 9¹º», yxLbU¼½,

    OdxNgUyU{JU, @¾, JKxX¿kÀÁÂ, ÃÄÅk^˜yI, etc VWG78XYZ[\
  67. /123 69 MAPF-POST[Hönig+ ICAPS-16] 2 moves 2 moves +1 turn

    model: execution: J:Ÿ©/sX)1r© MAPF plan 1ª«l †)ã´671¬8ž;C,Ý+–1z{ A B C D E B C F C D ƒZ´¹Ì¬-1ó® A B C D E B C F C D 5 0 0 16 25 32 48 29 33 64 —˜J^ƒZ1lm% E D A B B C … 0 0 0 0 -1 ∞ -2 -1 0 ∞ ∞ ∞ ∞ ∞ ∞ ∞ 0 0 -4 -8 -4 ∞ ¯°b`?512 E D A B B C … [1,∞] source sink [0,0] [0,0] [1,∞] [2,∞] [8,∞] [4,∞] [4,∞] [0,∞] [0,∞ ] [0,∞ ] )ú5Öž%ƒZ1-&<+DE. c.f., STN: simple temporal network [Dechter+ AIJ-91] ¾¿LÀ¢… [DÁ™Â, D%™Â] t=1 t=2 t=3 t=4 t=0 "\: MAPF plan A B C F D E
  68. /123 70 2•(Ÿ{&/CD2,/L ¡] 2(34356789:;<=>?@AB ACDEFGH? or IDIDJK

  69. /123 71 1 2 1 2 3 4 Planning Execution

    Delay
  70. /123 72 1 2 1 2 3 4 Planning Execution

    ¢£/„?("€ – ¤¥¦§ wait arrival time: 3 †ˆ‡5r±#€™š… —²Ž8*+,-./1¶*+,-./#³À
  71. /123 73 •[¨J©£/ª« – q¬`-8®¯ [Cap+ IROS-16, Ma+ AAAI-17] Œr=•Ž••akNn^

    1 2 1 2 3 4 => ‘’, GO arrival time: 2 1 2 1 2 3 4 Planning Execution 1 2 3 4 0 1 2 3 4 progress )Æ Ä« ´^/µ+
  72. /123 74 E°k©£\] 1 2 1 2 3 4 1

    2 3 4 5 6 delay negative effect 60 agents, solved by PIBT ¶·´V MAPF •.;F.; LMNOGPQH agents actions
  73. /123 75 Raspberry Pi x8 32 robots Bluetooth ABCD(EF8G HI&J77<K

    1000¾34s:Ÿ©/0s8ms ¸kŽ8÷©/ÿ+>[\…? nû8ën†hSV9
  74. /123 76 CD±k²()*&f ³A´†%B'µ¶·¸”h¹«,ºL

  75. /123 77 ?@%A%B28 »¼k`”h½¾f”¿l Àl6Á L82,]

  76. /123 78 »¼ÂÃÄ/ Åy€!"#$%&uvwx @ÇbÈÉÊ>ˆb /121

  77. /123 79 OTIMAPP solution multi-agent pathfinding solution 2 1 4

    3 0 0 1 2 —²5¹9 Offline Time-Independent Multi-Agent Path Planning KO, Froncios Bonnet, Yasumasa Tamura & Xavier Defago planning execution https://kei18.github.io/otimapp IJCAI-22 ºŽ9/0 OTIMAPP sk»£, kt, ke )ÃÄ4‚¡CÅÆÇ0¨
  78. /123 80 given start goal graph solution path Problem Def.

    – OTIMAPP s.t. RGSTUVWXYHCD Z[G\'];^S:_`
  79. /123 81 Æ‹‡'RÇh] OdUX žUR

  80. /123 82 CDUVR8RS no reachable cyclic deadlock no reachable terminal

    deadlock abcdefB2g efHijV.©ª:©>Æìí, …†Sö<.D¥–¬~1Nš
  81. /123 83 OPQ8RS OTIMAPP B hiGjkHlm *3-SAT‡ˆsŠ‹h…† }l†“•Gh? (‹”•I) =>

    NP –7 —˜}h™ (š›) => co-NP œ• ¼‘y
  82. /123 84 Solvers agents 0 20 40 60 80 100

    0 20 40 60 80 100 random-32-32-10 32x32 0 40 80 120 160 200 0 20 40 60 80 100 random-64-64-10 64x64 0 40 80 120 160 200 0 20 40 60 80 100 den520d 257x256 success rate (%) ≤ 5 min MAPF avoids collisions OTIMAPP avoids deadlocks prioritized planning deadlock-based search extending conflict-based search [Sharon+ AIJ-15] extending conventional PP [Erdmann+ Algorithmica-87] $%½o2y9/0hÆ^J´VƒZhk„% MAPF ske1 OTIMAPP 5¢ý
  83. /123 85 Execution Demo no synchronization only local interactions centralized

    style with toio robots decentralized style with AFADA [Kameyama+ ICRA-21; our work!] ž§¨s:Ÿ©/#c´d5efž%&'1å…
  84. /123 86 Planning Stage Acting Stage inspired by “Automated Planning

    and Acting,” Ghallab+ 2016 M6È2ÉIltK ?@%A%BNCD8`- Offline / Deliberative
  85. /123 87 Planning Stage Acting Stage Online / Reactive Ê/h„?("€•

    ÉIlËh
  86. /123 88 Collision Avoidance Based Ÿp#`•/, ;C+`A– ¾~A¿®ÀÁ5Æ ýhy% ÐæÔMÂVŽ…P

    <= ÃŽ9 B©àÔB-–F•YVÄÅ#üÖ .©ª:©>?B+5SVvV9 —ÆVc)S'vV9 JS¡~´ 2. ¢À‡ˆV9 & é+–5g‡š->DE.1Ÿp => c) 1. ÇÈs*+,-./sÄÅ1"d -«1˜9Ç~habž ÕÖ [Şenbaşlar+ DARS-18] https://youtu.be/LbWRvLfdwTA Optimal Reciprocal Collision Avoidance (ORCA) 1Sþm'Ž¨r~sde#ìí [van den Berg+ ISRR-11] ÉÊS±o
  87. /123 89 »¼ÂÃÄ8ÉI¹f XÌ2t/LÍÎ[\] PIBT / TSW AP Again!

  88. /123 90 Online Time-Independent Unlabeled-MAPF given: start target graph or

    Policy µ¶K: T#ÊËV¹Ìh)9¨Æ ¶Ë#é+–58Ê7Šë [Okumura+ ICAPS-22] *+,-./Ss%´5 -/=©>V->DE.1Jcž% ¹ÌSõ./:+–hyV9
  89. /123 91 Online TSWAP *›¡6ÈÉÊËÌ ÕÖ* compute arbitrary initial target

    assignment 1. offline phase 2. online phase when is activated: ‰?`•.sÌ<'¡þ` &s-–éB•YSµ¶` nopXKqgr/<=Gst [Okumura+ ICAPS-22]
  90. /123 92 »¼ÂÃÄQ8ϳ ƒZ5¬ž%HIJK5Bý ÍÎV¡~d™y#9ˆV9

  91. /123 93 ϳÐ

  92. /123 94 •œÎÑ9%@‹%'@zyÒž8 »¼ÂÃÄ/uvwx•ÒžÈ”

  93. /123 95 planning execution Time-Independent Planning for Multiple Moving Agents

    KO, Yasumasa Tamura & Xavier Defago AAAI-21 PIBT s‰.`•.ƒZÏÐì5+,E.h$% Causal-PIBTs ÑÒ *+,-./1n)´5c)ž%KLÓ(Ô'Ž¨kÕ 1 2 1 2 3 execution (online) planning (offline) delay necessity to address timing uncertainties https://kei18.github.io/time-independent-planning/
  94. /123 96 Planning Stage Acting Stage žŸ •¡¢…™ )*+,+-B.#Er;V £j£\]¤¥n)l†“•G

    Œr;••G¦§.Aa ¨©•Gª0 CDÓ"&ÈNÔ
  95. /123 97 Outline C[nOP>E.# opqr>E)*+,+- DskKLMN+Otuvw MAPF; Multi-Agent Path Finding

    /121
  96. /123 98 MAPF 8B@Õ,‘’8 “¸Ö” NJl Œ@*Æ)*Æ{×L2LL8\]

  97. /123 99 bB©ª5л lÖ´VÁÂ

  98. /123 100 kVŽ ×Ø… bB©ª5л lÖ´VÁÂ

  99. /123 101 .u?vrwxG.uU`U? y/z{.|}B~S•H.k|€}•UH ØÙ8ÚÛY8ÜÝÞ ßàá¼28uvwxf â–M8ãä\åÉIhæçÒž

  100. /123 102 artificial potential field sampling-based rule-based goal start LMNO4!"#$%&PQ(RS%T%U

  101. /123 103 SBMP: Sampling-Based Motion Planning Ùûë7ÕÖ: 2. :+ªž©_(b`?)1Úº 1.

    configuration space ‡ˆKL1`.ÛYß._B.b -«sabŽ ÊšÜû¨ß._B.bž%‡, ÊšÜû¨:+ªž©_1Úºž%‡5À9¨ .Ý•.àE•;#ìí, ~€s-–éB•Y#ÑÒÒv¨y8 RRT: Rapidly-exploring Random Tree [Lavalle 98] PRM: Probabilistic Roadmap [Kavraki+ 96] Images are from Wikipedia
  102. /123 104 Configuration Space & Motion Planning :Ÿ©/sKLS (x, y)

    h]Òv% &sYZh ÁŸÃ1kë Å%‹5Vû¨Æ¡þ` 1. :Ÿ©/sKL1kÕ 2. efhyV9Þø1Oß 3. ÁŸÃ1àðû¨kë (x, y) S&sÞø¡5 án%üÖ#$%
  103. /123 105 artificial potential field sampling-based rule-based 趕á¼28?@%A%B N«¬>-®xV¯b°l…™ ‚ƒ„

  104. /123 106 ßàá¼28?@%A%BfRmKÔ8 Š‹"Œ/„?("€ 1. SBMP >±KLMN+OECL²Dn)a³G 2. ´µ¶·¸CL²Dn)¹> MAPF

    a}6 [Hönig+ TR-O-18] https://youtu.be/7KIa9FlmbRc
  105. /123 107 …†‡U.6ˆ‰Š‹HŒ•Ž•(•'‘’“2)6”:^H produced by PRM [Kavraki+ 96] KÍJéNJêëÒh â

    ã 2 ¦ WXsä\ Å å ksò ž–à*+,-./[\hSæç´
  106. /123 108 ìÌí •–'—˜3™;AšC ›bUœ•;žxkC •'‘’“26Ÿ >•¡¢H ^£k¤¥/–'—˜3™A/ ¦§D¨©?abGª? ’4«¬]/]']X›bUœ•6

    -x>?/B®<„XBUH…
  107. /123 109 ìÌí •–'—˜3™;AšC ›bUœ•;žxkC •'‘’“26Ÿ >•¡¢H ¯°±²G³©I´ ´µ`Hš^2(34356¶·;¸¹C ›bUœ•6±²‹º•¡¢H

    (»¼ª:±²)
  108. /123 110 <½;lm6.`¾˜'¿ MAPF algorithm new instance 𝐹!"#$ random walk

    sampling module next locations for all agents starts path generation compositing solution … t=0 t=1 t=2 CTRMs ÀÁ¾˜'¿ 𝐹!"#$ model training instances & solutions predict next locations CTRMs: Learning to Construct Cooperative Timed Roadmaps for Multi-agent Path Planning in Continuous Spaces representation KO*, Ryo Yonetani, Mai Nishimura & Asako Kanezaki .+Fè)·:+ªž©_z{, _`.a.bsä\1éê https://omron-sinicx.github.io/ctrm AAMAS-22 *work done as an intern at OMRON SINIC X
  109. /123 111 SPARS [Dobson+ IJRR-14] simplified PRM [Karaman+ IJRR-11] square

    as agent-specific roadmaps grid as used in MAPF studies CTRMs8QV CTRMs sparse dense ksò1åû8nn WXä\1éê
  110. /123 112 Œ•ÂÃ;ÄÅCabUd£u •'‘’“26ÆÇ>•¡HH/XBÈ ÉÊË®(•'‘’“2)6ÌÍ>?A 2(3435GÎ;U?@AGÏ|š^

  111. /123 113 Quick Multi-Robot Motion Planning by Combining Sampling &

    Search KO & Xavier Defago planning representation ž–à:Ÿ©/^+DE._`.a.b1ű5kë-–éB•YSSSP :+ªž©_ST'ÁŸÃ1¡ƒ5cš https://kei18.github.io/sssp
  112. /123 114 Proposed Algorithm: SSSP 0 1 2 0 1

    2 3 0 1 2 3 4 5 0 1 2 2 3 5 0 1 2 3 4 0 1 4 00 +9:;:<=> 00 10 20 40 50 00 50 51 53 54 00 10 20 40 50 00 vertex expansion search-node expansion & goals new vertices closed next action `.ÛYëì+>hß._B.b & JZKL1M"Ž8íîß+à [Standley AAAI-10] [Hsu+ ICRA-97]
  113. /123 115 0 200 400 600 800 1000 solved ins

    ances 0 100 200 300 run ime (sec) PRM RRT RRT-C PP CBS SSSP Point2d DOF: 2N 0 200 400 600 800 1000 solved ins ances 0 100 200 300 run ime (sec) PRM RRT RRT-C PP CBS SSSP Point3d DOF: 3N 0 200 400 600 800 1000 solved ins ances 0 100 200 300 run ime (sec) PRM RRT-C RRT CBS PP SSSP Line2d DOF: 3N 0 200 400 600 800 1000 solved ins ances 0 100 200 300 run ime (sec) PRM RRT PP CBS RRT-C SSSP Capsule3d DOF: 6N 0 200 400 600 800 1000 olved in tance 0 100 200 300 runtime ( ec) PRM PP/CBS RRT-C RRT SSSP Arm22 DOF: 2N 0 200 400 600 800 1000 solved ins ances 0 100 200 300 run ime (sec) PRM PP CBS RRT RRT-C SSSP Arm33 DOF: 6N 0 200 400 600 800 1000 solved ins ances 0 100 200 300 run ime (sec) PRM RRT RRT-C PP CBS SSSP Dubins2d DOF: 3N 0 200 400 600 800 1000 solved ins ances 0 100 200 300 run ime (sec) RRT-C CBS PP/RRT SSSP Snake2d DOF: 6N SSSP8QV '¨Æfï
  114. /123 116 MRMP: Multi-Robot Motion Planning P*+,-./SðfsKLYZ1ÆÀ ñ95òóŽV9ôõ1ŸpŽ89 MAPF S

    MRMP sö÷C+; 8•ŽøùV]khÆÏ›5qŽ9 [Hopcroft+ IJRR-84] [Zhang+ SIGGRAPH-20] (123456*7'895:) >?-ÍÎ2Ï ú~‰A,->/1)‡ž/0ûž§¨ü5ý³#$% ¤: AGV/AMR, •M6ý, :Ÿ©/-+Y, Nþsÿ, CG, !" Ð)KL6
  115. /123 117 MAPF Multi-Agent Path Finding SBMP Sampling-Based Motion Planning

    ML Machine Learning as Heuristics integration VW MRMP 8XYZR["\]^_`ab@ MRMP sEF1#Ž$m¨9„w %Á&'s‰+/G+DE.£5ÀV#%‡Æ…P 🤔
  116. /123 118 Closing

  117. /123 119 EFGHIJK\–KMN 3456786, 9:;.<=>, ?@ABCD+EFGHI>FGJB KLMN+OPEQRSLTU+V WXYZ / C[\]^_:`aVbcB8d

    efg:`hij)CLklmZ>FGJB
  118. /123 120 Summary C[nOP>E.# opqr>E)*+,+- DskKLMN+Otuvw MAPF; Multi-Agent Path Finding

  119. /123 121 representation planning execution !"#$%&+8Šîï";<%k æç/ðñ8çò integration

  120. /123 122 !"#$%&'()*+ ,-./0123456789:;< Wókô

  121. /123 More Info? => Check My Website! https://kei18.github.io/ Thank You

    for Listening! º*[»¼>•½
  122. /123 124 Reference

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