“SEAR: A Polynomial- Time MultiRobot Path Planning Algorithm with Expected Constant-Factor Optimality Guarantee

Published in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (Conference Paper) with S. Han and J. Yu., 2018

Abstract

We study the labeled multi-robot path planning problem in continuous 2D and 3D domains in the absence of obstacles where robots must not collide with each other. For an arbitrary number of robots in arbitrary initial and goal arrangements, we derive a polynomial time, complete algorithm that produces solutions with constant-factor optimality guarantees on both makespan and distance optimality, in expectation, under the assumption that the robot labels are uniformly randomly distributed. Our algorithm only requires a small constant-factor expansion of the initial and goal configuration footprints for solving the problem, i.e., the problem can be solved in a fairly small bounded region. Beside theoretical guarantees, we present a thorough computational evaluation of the proposed solution. In addition to the baseline implementation, adapting an effective (but non-polynomial time) routing subroutine, we also provide a highly efficient implementation that quickly computes near-optimal solutions. Hardware experiments on the microMVP platform composed of non-holonomic robots confirms the practical applicability of our algorithmic pipeline.

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Citation: S. D. Han, E. Jaramillo Rodriguez and J. Yu, “SEAR: A Polynomial- Time MultiRobot Path Planning Algorithm with Expected Constant-Factor Optimality Guarantee,” 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2018, pp. 1-9.