winPIBT: Extended Prioritized Algorithm for Iterative Multi-agent Path Finding

WoMAPF-20
https://kei18.github.io/pibt/

Transcript

1. winPIBT Extended Prioritized Algorithm for Iterative Multi-Agent Path Finding Keisuke Okumura, Yasumasa Tamura & Xavier Defago 7th Jan. 2021 IJCAI-20 Workshop on MAPF Tokyo Institute of Technology, Japan ౦ژ޻ۀେֶ 5PLZP
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   5FDIOPMPHZ virtual conference
2. /28 2 MAPF: Multi-Agent Path Finding given agents (starts) graph goals solution paths without collisions
3. /28 3 Motivation winPIBT: extension of PIBT [okumura+ IJCAI-19] high low result of PIBT ideal anticipating a single-step ahead make PIBT anticipating multi-steps ahead time-window
4. /28 4 PIBT Priority Inheritance with Backtracking [Okumura+ IJCAI-19] solving MAPF iteratively quick & scalable adaptivity for decentralization 500 agents within 0.5 sec Applicable to Multi-agent Pickup & Delivery [Ma+ AAMAS-17] sub-optimal
5. /28 5 locations at t=1 t=2 t=3 repeat one-timestep prioritized planning high low mid How PIBT works – 1/6 … 1 2 3 4 5 6 7 8 9 decision order time-window
6. /28 6 How PIBT works – 2/6 high low mid stuck
7. /28 7 How PIBT works – 3/6 high low mid as high priority inheritance [Sha+ 1990] +backtracking (skip)
8. /28 8 How PIBT works – 4/6 high as high as high as high as high stuck
9. /28 9 How PIBT works – 5/6 invalid valid re-plan re-plan valid You can move invalid You must re-plan, I will stay introduce backtracking
10. /28 10 In biconnected graphs the agent with highest priority can move to any adjacent locations reachability: all agents reach their goals within finite timesteps +dynamic priorities How PIBT works – 6/6
11. /28 11 one-shot MAPF reachability ≠ completeness lifelong scenarios unsolvable, but we can ensure reachability
12. /28 12 winPIBT
13. /28 13 A B C D E F G H E C collision- free potentially collide! E F G H D C G must plan additional paths Paths with Different Lengths – 1/2
14. /28 14 A B C D E F G H E C collision- free safe E A B C D C G G G G can stay at the last location two paths are isolated Paths with Different Lengths – 2/2
15. /28 15 Revisit PIBT A B D E C F high D C F F E D two paths will not be isolated => priority inheritance as high
16. /28 16 Concept of winPIBT repeat until terminate + dynamic priorities In biconnected graphs, winPIBT ensures reachability plan an arbitrary-length path for one agent reserve nodes in the path sequentially when violating the isolation with another agent, make this agent plan a single-step ahead using priority inheritance so that two paths are isolated 1. 2. 3.
17. /28 17 Example of winPIBT high t=0 low t=0 mid t=0 time-window:3 locations at t=1 t=2 t=3 high low mid 1 t=1 goals
18. /28 18 Example time-window:3 locations at t=1 t=2 t=3 high low mid 1 2 t=2 goals low t=0 mid t=0 high t=1
19. /28 19 Example time-window:3 locations at t=1 t=2 t=3 high low mid 1 2 t=3 violate, priority inheritance goals low t=0 mid t=0 high t=2 3
20. /28 20 Example time-window:3 locations at t=1 t=2 t=3 high low mid 1 4 2 3 goals high t=2 low t=0 mid t=0 t=3 t=1
21. /28 21 Example time-window:3 locations at t=1 t=2 t=3 high low mid 1 4 2 3 goals high t=3 low t=1 mid t=0 5 t=1
22. /28 22 Example time-window:3 locations at t=1 t=2 t=3 high low mid 1 4 2 3 goals high t=3 low t=1 mid t=1 5 t=2 violate, priority inheritance 6
23. /28 23 Example time-window:3 locations at t=1 t=2 t=3 high low mid 1 4 2 3 goals high t=3 low t=1 mid t=1 5 t=2 t=2 6 7
24. /28 24 Example time-window:3 locations at t=1 t=2 t=3 high low mid 1 4 2 3 goals high t=3 low t=2 mid t=2 5 t=3 6 7 8
25. /28 25 Example time-window:3 locations at t=1 t=2 t=3 high low mid 1 4 2 3 goals high t=3 low t=2 mid t=3 5 6 7 8 t=3 9
26. /28 26 Example time-window:3 locations at t=1 t=2 t=3 high low mid 1 4 2 3 goals high t=3 low t=3 mid t=3 5 6 7 8 9
27. /28 27 Evaluation with fixed time-windows
28. /28 28 agents cost per agent 2 winPIBT-30 3 10 PIBT agents makespan 2 winPIBT-30 3 10 PIBT empty-32-32 25 instances One-shot MAPF the effect is not significant in open space
29. /28 29 agents runtime (sec) 2 winPIBT-30 3 10 PIBT agents success instances 2 winPIBT-30 3 10 PIBT empty-32-32 25 instances One-shot MAPF c.f., reachability ≠ completeness winPIBT takes time
30. /28 30 Naïve Iterative MAPF kiva-like 2000 targets 10 repetitions agents service time 50 30 5 10 PIBT agents runtime (sec) 50 30 5 10 PIBT longer windows need time winPIBT: good
31. /28 31 Concluding Remarks develop adaptive windows future directions decentralization, c.f., our latest work [Okumura+ AAAI-21] winPIBT to overcome short-sighted behavior of PIBT motivation proposal target solving MAPF iteratively, c.f., reachability