A Novel Warehouse Multi-Robot Automation System with Semi-Complete and Computationally Efficient Path Planning and Adaptive Genetic Task Allocation Algorithms

TL;DR

This paper introduces a semi-complete, computationally efficient path planning algorithm and an adaptive genetic task allocation method for multi-robot warehouse automation, improving conflict resolution and system performance.

Contribution

It presents a semi-complete potential field path planning algorithm and a genetic-based task allocation method, both tailored for multi-robot warehouse systems, with proven semi-completeness and heuristic learning.

Findings

The RERAPF algorithm is semi-complete and efficient.

Simulation shows improved conflict resolution.

System achieves better task allocation performance.

Abstract

We consider the problem of warehouse multi-robot automation system in discrete-time and discrete-space configuration with focus on the task allocation and conflict-free path planning. We present a system design where a centralized server handles the task allocation and each robot performs local path planning distributively. A genetic-based task allocation algorithm is firstly presented, with modification to enable heuristic learning. A semi-complete potential field based local path planning algorithm is then proposed, named the recursive excitation/relaxation artificial potential field (RERAPF). A mathematical proof is also presented to show the semi-completeness of the RERAPF algorithm. The main contribution of this paper is the modification of conventional artificial potential field (APF) to be semi-complete while computationally efficient, resolving the traditional issue of incompleteness. Simulation results are also presented for performance evaluation of the proposed path planning algorithm and the overall system.