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

Ahmad Kokhahi, Mary Kurz

TL;DR

This work addresses collision avoidance and task allocation in robotic mobile fulfillment systems by integrating energy-awareness into both path routing and multi-objective task assignment. It introduces a novel energy-based priority rule and leverages two metaheuristics, NSGA-II and ALNS, to optimize TA with respect to running time and energy, while a Modified A* path planner accounts for space-time collisions. The problem is formulated as a multi-objective, NP-hard optimization, with a simplified MDTSP relaxation to explore Pareto fronts on small instances; comprehensive comparisons show the energy-aware approaches outperform existing methods, with NSGA-II enhanced by an ARC operator delivering the strongest Pareto performance. The results offer practical guidance for managers seeking energy-efficient collision avoidance and balanced task distribution in RMFS deployments, and point to future extensions involving real-time dynamics and battery-aware scheduling.

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.

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

TL;DR

This work addresses collision avoidance and task allocation in robotic mobile fulfillment systems by integrating energy-awareness into both path routing and multi-objective task assignment. It introduces a novel energy-based priority rule and leverages two metaheuristics, NSGA-II and ALNS, to optimize TA with respect to running time and energy, while a Modified A* path planner accounts for space-time collisions. The problem is formulated as a multi-objective, NP-hard optimization, with a simplified MDTSP relaxation to explore Pareto fronts on small instances; comprehensive comparisons show the energy-aware approaches outperform existing methods, with NSGA-II enhanced by an ARC operator delivering the strongest Pareto performance. The results offer practical guidance for managers seeking energy-efficient collision avoidance and balanced task distribution in RMFS deployments, and point to future extensions involving real-time dynamics and battery-aware scheduling.

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.

Paper Structure

This paper contains 31 sections, 1 theorem, 6 equations, 12 figures, 3 tables, 6 algorithms.

Table of Contents

  1. Introduction
  2. Problem setting
  3. Literature review
  4. Task assignment
  5. Path planning
  6. Priority rules
  7. Battery management problem
  8. Contributions and paper structure
  9. Problem formulation and mathematical model
  10. Variable definition
  11. Model establishment
  12. Methodology
  13. Path routing
  14. Modified A* algorithm
  15. Priority rule
  16. ...and 16 more sections

Key Result

Proposition 1

Problem MATEC is NP-hard.

Figures (12)

  • Figure 1: Top view of a RMFS
  • Figure 2: Collision Types
  • Figure 3: MDTSP representation: Nodes 0–4 represent tasks. 'E' and 'T' denote the fixed energy and time costs of moving each pod to and from the workstation. Nodes 6–8 correspond to AGV depots. Arc weights represent travel time only, for clarity.
  • Figure 4: A* algorithm
  • Figure 5: Collision inside aisle
  • ...and 7 more figures

Theorems & Definitions (2)

  • Proposition 1
  • proof