Prioritized Multi-agent Path Finding for Differential Drive Robots

Yakovlev, Konstantin; Andreychuk, Anton; Vorobyev, V.

doi:10.1109/ecmr.2019.8870957

Cited by 15 publications

(9 citation statements)

References 22 publications

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“…To further validate the applicability of the proposed approach for practical scenarios, we investigate if the solutions are executable by wheeled robots via a simulation. Our simulator is built using PyBullet [36], and simulates a team of wheeled robots moving with the differential drive kinematics [37] that is one of the most popular mechanisms for many real robots working in indoor environments [38].…”

Section: G Evaluation On the Wheeled Robot Simulationmentioning

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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Section: G Evaluation On the Wheeled Robot Simulationmentioning

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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“…Centralized ones rely on a control center that accumulates information on all agents positions. This group of algorithms includes conflict-based search algorithm [23] and its enhanced versions [24], [25] guaranteeing optimality and prioritized planning algorithms [26] that are capable to build near-optimal solutions under conditions. Decentralized planning consider the problem in the setting when access to information about other agents is limited.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Policy Learning for Multi-Agent Pathfinding

et al. 2021

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In this work we study the behavior of groups of autonomous vehicles, which are the part of the Internet of Vehicles systems. One of the challenging modes of operation of such systems is the case when the observability of each vehicle is limited and the global/local communication is unstable, e.g. in the crowded parking lots. In such scenarios the vehicles have to rely on the local observations and exhibit cooperative behavior to ensure safe and efficient trips. This type of problems can be abstracted to the socalled multi-agent pathfinding when a group of agents, confined to a graph, have to find collision-free paths to their goals (ideally, minimizing an objective function e.g. travel time). Widely used algorithms for solving this problem rely on the assumption that a central controller exists for which the full state of the environment (i.e. the agents current positions, their targets, configuration of the static obstacles etc.) is known and they can not be straightforwardly be adapted to the partially-observable setups. To this end, we suggest a novel approach which is based on the decomposition of the problem into the two sub-tasks: reaching the goal and avoiding the collisions. To accomplish each of this task we utilize reinforcement learning methods such as Deep Monte Carlo Tree Search, Q-mixing networks, and policy gradients methods to design the policies that map the agents' observations to actions. Next, we introduce the policy-mixing mechanism to end up with a single hybrid policy that allows each agent to exhibit both types of behavior -the individual one (reaching the goal) and the cooperative one (avoiding the collisions with other agents). We conduct an extensive empirical evaluation that shows that the suggested hybrid-policy outperforms standalone stat-ofthe-art reinforcement learning methods for this kind of problems by a notable margin.

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“…Push and Rotate [7]. Finally, a prioritized approach [4,19] is also a common choice in numerous cases. Prioritized planners are fast and typically provide solutions of reasonably low cost (very close to optimal in many cases).…”

Section: Related Workmentioning

confidence: 99%

A Combination of Theta*, ORCA and Push and Rotate for Multi-agent Navigation

Dergachev

Yakovlev

Prakapovich

2020

Lecture Notes in Computer Science

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We study the problem of multi-agent navigation in static environments when no centralized controller is present. Each agent is controlled individually and relies on three algorithmic components to achieve its goal while avoiding collisions with the other agents and the obstacles: i) individual path planning which is done by Theta* algorithm; ii) collision avoidance while path following which is performed by ORCA* algorithm; iii) locally-confined multi-agent path planning done by Push and Rotate algorithm. The latter component is crucial to avoid deadlocks in confined areas, such as narrow passages or doors. We describe how the suggested components interact and form a coherent navigation pipeline. We carry out an extensive empirical evaluation of this pipeline in simulation. The obtained results clearly demonstrate that the number of occurring deadlocks significantly decreases enabling more agents to reach their goals compared to techniques that rely on collision-avoidance only and do not include multi-agent path planning component .

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Prioritized Multi-agent Path Finding for Differential Drive Robots

Cited by 15 publications

References 22 publications

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

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

Hybrid Policy Learning for Multi-Agent Pathfinding

A Combination of Theta*, ORCA and Push and Rotate for Multi-agent Navigation

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