Collision avoidance and path finding in a robotic mobile fulfillment system using multi-objective meta-heuristics

TL;DR

This paper introduces multi-objective algorithms for collision avoidance and task assignment in robotic systems, optimizing for energy, travel time, and collision reduction, with demonstrated improvements over existing methods.

Contribution

It presents novel multi-objective algorithms for collision avoidance and task assignment in AGVs, considering energy consumption alongside traditional metrics.

Findings

Proposed methods outperform existing approaches in collision avoidance.

Algorithms effectively balance energy use and travel time.

Enhanced task assignment efficiency demonstrated through evaluations.

Abstract

Multi-Agent Path Finding (MAPF) has gained significant attention, with most research focusing on minimizing collisions and travel time. This paper also considers energy consumption in the path planning of automated guided vehicles (AGVs). It addresses two main challenges: i) resolving collisions between AGVs and ii) assigning tasks to AGVs. We propose a new collision avoidance strategy that takes both energy use and travel time into account. For task assignment, we present two multi-objective algorithms: Non-Dominated Sorting Genetic Algorithm (NSGA) and Adaptive Large Neighborhood Search (ALNS). Comparative evaluations show that these proposed methods perform better than existing approaches in both collision avoidance and task assignment.