Simulation-based planning of Motion Sequences for Automated Procedure Optimization in Multi-Robot Assembly Cells

TL;DR

This paper introduces a simulation-based planning method for optimizing motion sequences in reconfigurable multi-robot assembly cells, improving efficiency and collision avoidance over decentralized approaches.

Contribution

It presents a novel decomposition-based motion planning approach that separates core and traverse operations for optimized multi-robot assembly sequences.

Findings

Outperforms decentralized motion planning baseline

Generates collision-free, efficient assembly procedures

Demonstrated effectiveness through simulation experiments

Abstract

Reconfigurable multi-robot cells offer a promising approach to meet fluctuating assembly demands. However, the recurrent planning of their configurations introduces new challenges, particularly in generating optimized, coordinated multi-robot motion sequences that minimize the assembly duration. This work presents a simulation-based method for generating such optimized sequences. The approach separates assembly steps into task-related core operations and connecting traverse operations. While core operations are constrained and predetermined, traverse operations offer substantial optimization potential. Scheduling the core operations is formulated as an optimization problem, requiring feasible traverse operations to be integrated using a decomposition-based motion planning strategy. Several solution techniques are explored, including a sampling heuristic, tree-based search and gradient-free optimization. For motion planning, a decomposition method is proposed that identifies specific areas in the schedule, which can be solved independently with modified centralized path planning algorithms. The proposed method generates efficient and collision-free multi-robot assembly procedures that outperform a baseline relying on decentralized, robot-individual motion planning. Its effectiveness is demonstrated through simulation experiments.