A duality approach to path planning for multiple robots

Abstract

Abstract-In this paper, we propose an optimization-based framework for path planning for multiple robots in presence of obstacles. The objective is to find multiple fixed length paths for multiple robots that satisfy the following constraints: (i) bounded curvature, (ii) obstacle avoidance, (iii) and collision avoidance. First, we formulate a relaxation of the path planning problem using polygonal approximations. We show that path planning problem for multiple robots under various constraints and missions, such as curvature and obstacle avoidance constraints as well as rendezvous and maximal total area coverage, can be cast as a nonconvex optimization problem. Then, we propose an alternative dual formulation that results in no duality gap. We show that the alternative dual function can be interpreted as minimum potential energy of a multiparticle system with discontinuous spring-like forces. Finally, we show that using the proposed duality-based framework, an approximation of the minimal length path planning problem (also known as Dubins' problem) in presence of obstacles can be solved efficiently using primal-dual interior-point methods.

Asymptotic Stability of LTV Systems With Applications to Distributed Dynamic Fusion

“…Non-negativity and stochasticity are standard in the literature concerning relevant iterative algorithms and multi-agent fusion, see e.g. [10]- [13]. When there is a neighboring anchor, Eq.…”

“…Finally, we point out that the bounds in Eqs. ( 8)- (10), are naturally satisfied by LTI dynamics: x(k + 1) = P x(k) + Bu(k), with nonnegative matrices; a topic well-studied in the context of iterative algorithms, [34], [35], and multi-agent fusion.…”

“…they are non-negative and each row sums to at most 1. Examples include leader-follower algorithms, [8], [9], consensus-based control algorithms, [10]- [12], and sensor localization, [13], [14]. † The authors are with the Department of Electrical and Computer Engineering, Tufts University, 161 College Ave, Medford, MA 02155, {sam248,khan}@ece.tufts.edu.…”