Prioritized SIPP for Multi-agent Path Finding with Kinematic Constraints

Ali, Zain; Yakovlev, Konstantin

doi:10.1007/978-3-030-87725-5_1

Cited by 5 publications

(3 citation statements)

References 18 publications

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“…1) by 1 s, SIPP will not find a solution. In (Ali and Yakovlev 2021), accelerating actions were straightforwardly integrated into SIPP, which, again, makes the algorithm incomplete (as we show in this work). (Cohen et al 2019) suggested a variant of SIPP for the problem setting that assumes arbitrary motion patterns.…”

Section: Related Workmentioning

confidence: 98%

Safe Interval Path Planning with Kinodynamic Constraints

Ali

Yakovlev²

2023

AAAI

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Safe Interval Path Planning (SIPP) is a powerful algorithm for solving a single-agent pathfinding problem where the agent is confined to a graph and certain vertices/edges of this graph are blocked at certain time intervals due to dynamic obstacles that populate the environment. The original SIPP algorithm relies on the assumption that the agent is able to stop instantaneously. However, this assumption often does not hold in practice, e.g. a mobile robot moving at a cruising speed cannot stop immediately but rather requires gradual deceleration to a full stop that takes time. In other words, the robot is subject to kinodynamic constraints. Unfortunately, as we show in this work, in such a case, the original SIPP is incomplete. To this end, we introduce a novel variant of SIPP that is provably complete and optimal for planning with acceleration/deceleration. In the experimental evaluation, we show that the key property of the original SIPP still holds for the modified version: it performs much fewer expansions compared to A* and, as a result, is notably faster.

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Section: Related Workmentioning

confidence: 98%

Safe Interval Path Planning with Kinodynamic Constraints

Ali

Yakovlev²

2023

AAAI

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“…However, all of these extensions require computational time that rapidly increases with the number of agents. Other work incorporated SIPP with prioritized planning [28], [29] to improve the scalability for the continous-time setup, which nevertheless only assumed planning on a gridmap. Another apporach has addressed the scalability issue for non-grid graphs by enumerating all possible collisions before the planning [14], but this has only worked for the case of discrete time.…”

Section: Related Workmentioning

confidence: 99%

Prioritized Safe Interval Path Planning for Multi-Agent Pathfinding With Continuous Time on 2D Roadmaps

Kasaura

Nishimura

Yonetani

2022

IEEE Robot. Autom. Lett.

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We address a challenging multi-agent pathfinding (MAPF) problem for hundreds of agents moving on a 2D roadmap with continuous time. Despite its known potential for producing better solutions compared to typical grid and discrete-time cases, few approaches have been established to solve this problem due to the intractability of collision checks on a large scale. In this work, we propose Prioritized Safe-Interval Path Planning with Continuous-Time Conflicts (PSIPP/CTC) that extends a scalable prioritized planning algorithm to work on the 2D roadmap and continuous-time setup by alleviating intensive collision checks. Our approach involves a novel concept named Continuous-Time Conflict (CTC), which describes a pair among vertices and edges associated with continuous-time intervals within which collisions can happen between agents. We precompute CTCs using geometric neighbor-search and sweeping techniques and annotate roadmaps with the CTCs just once before planning starts. Doing so allows us to efficiently enumerate collision-free time intervals for all vertices and edges and find each agent's path with continous time in prioritized planning. Extensive experimental evaluations demonstrate that PSIPP/CTC significantly outperforms existing methods in terms of planning success rate and runtime while maintaining an acceptable solution quality. As a proof of concept, we also confirmed the effectiveness of the proposed approach on a physics simulation with differential wheeled robots.

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“…A G-CBS (Generalized Conflict-based Search) generalized MAPF for heterogeneous agents (G-MAPF) was suggested [3]. The kinematic conditions of the agents were first considered in [4] when planning in MAPF. Multiple types of prioritized based planning algorithms and compared their results in terms of completeness and optimality of the solutions are analyzed in [5].…”

Section: Introductionmentioning

confidence: 99%

Prioritized Anytime Dynamic A* Path Planning for Multi-Agent Vehicles in Heterogeneous Traffic

Nantabut

Abel

2023

Advances in Transdisciplinary Engineering

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For a centralized path planning in the multi-agent path finding (MAPF), especially for road vehicles using traffic rules, a prioritized planning algorithm is one of the key methods that deal with real-time problems. The time used for planning is limited, especially when a number of agents are present, and a suboptimal solution has to be found. If possible, the previous solution should be reused for the replanning. Anytime Dynamic A* (AD*) can both replan the path when the environment is changed dynamically and is able to return a safe, but potentially suboptimal solution in case where a maximum cycle time is exceeded. A prioritized order selection is based on the path length to the goal. To model heterogeneous traffics with vehicles and pedestrians, a combination of the bicycle and pedestrian obstacle reciprocal collision avoidance algorithms (B-ORCA and PORCA) is used to realize a naturalistic interaction among the multi-agent system and the dynamic environment. The number of agents and of dynamic obstacles is randomly generated and the time used for planning is analyzed in view of the limited time available in the simulations. In this work, the path planning of up to three agents can be executed in real-time in MATLAB.

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Prioritized SIPP for Multi-agent Path Finding with Kinematic Constraints

Cited by 5 publications

References 18 publications

Safe Interval Path Planning with Kinodynamic Constraints

Safe Interval Path Planning with Kinodynamic Constraints

Prioritized Safe Interval Path Planning for Multi-Agent Pathfinding With Continuous Time on 2D Roadmaps

Prioritized Anytime Dynamic A* Path Planning for Multi-Agent Vehicles in Heterogeneous Traffic

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