Multi-Robot Task Allocation for Homogeneous Tasks with Collision Avoidance via Spatial Clustering

TL;DR

This paper introduces a spatial clustering framework combining multi-robot task allocation and collision avoidance, significantly reducing computation time and eliminating collisions in industrial environments with homogeneous tasks.

Contribution

The novel framework integrates task allocation and collision avoidance through spatial clustering, improving efficiency and safety in multi-robot systems handling homogeneous tasks.

Findings

Achieves up to 93% time reduction in task scheduling

Improves solution quality by up to 7% over existing methods

Effectively eliminates collision points in multi-robot operations

Abstract

In this paper, a novel framework is presented that achieves a combined solution based on Multi-Robot Task Allocation (MRTA) and collision avoidance with respect to homogeneous measurement tasks taking place in industrial environments. The spatial clustering we propose offers to simultaneously solve the task allocation problem and deal with collision risks by cutting the workspace into distinguishable operational zones for each robot. To divide task sites and to schedule robot routes within corresponding clusters, we use K-means clustering and the 2-Opt algorithm. The presented framework shows satisfactory performance, where up to 93\% time reduction (1.24s against 17.62s) with a solution quality improvement of up to 7\% compared to the best performing method is demonstrated. Our method also completely eliminates collision points that persist in comparative methods in a most significant sense. Theoretical analysis agrees with the claim that spatial partitioning unifies the apparently disjoint tasks allocation and collision avoidance problems under conditions of many identical tasks to be distributed over sparse geographical areas. Ultimately, the findings in this work are of substantial importance for real world applications where both computational efficiency and operation free from collisions is of paramount importance.