Ordering-Flexible Multi-Robot Coordination for MovingTarget Convoying Using Long-TermTask Execution

TL;DR

This paper introduces a novel long-term task execution algorithm for multi-robot convoying that guarantees formation and obstacle avoidance in dynamic environments through an online constraint-based optimization approach.

Contribution

It presents a new LTTE framework with constraint integration and slack variables, enabling flexible, resilient, and long-term convoying with arbitrary formations in changing environments.

Findings

Validated with 2D experiments using three robots

Demonstrated robustness against robot breakdowns and obstacles

Proved asymptotic convergence in complex scenarios

Abstract

In this paper, we propose a cooperative long-term task execution (LTTE) algorithm for protecting a moving target into the interior of an ordering-flexible convex hull by a team of robots resiliently in the changing environments. Particularly, by designing target-approaching and sensing-neighbor collision-free subtasks, and incorporating these subtasks into the constraints rather than the traditional cost function in an online constraint-based optimization framework, the proposed LTTE can systematically guarantee long-term target convoying under changing environments in the n-dimensional Euclidean space. Then, the introduction of slack variables allow for the constraint violation of different subtasks; i.e., the attraction from target-approaching constraints and the repulsion from time-varying collision-avoidance constraints, which results in the desired formation with arbitrary spatial ordering sequences. Rigorous analysis is provided to guarantee asymptotical convergence with challenging nonlinear couplings induced by time-varying collision-free constraints. Finally, 2D experiments using three autonomous mobile robots (AMRs) are conducted to validate the effectiveness of the proposed algorithm, and 3D simulations tackling changing environmental elements, such as different initial positions, some robots suddenly breakdown and static obstacles are presented to demonstrate the multi-dimensional adaptability, robustness and the ability of obstacle avoidance of the proposed method.