Optimized Scheduling and Positioning of Mobile Manipulators in Collaborative Applications

TL;DR

This paper presents a PSO-based optimization framework for mobile manipulators that enhances task scheduling, positioning, and safety in collaborative environments, leading to improved efficiency and adaptability.

Contribution

It introduces a novel digital model-based optimization approach using PSO for mobile robot coordination in human-robot collaboration.

Findings

Reduced cycle time in task execution

Optimized robot positioning for safety and efficiency

Enhanced adaptability to human presence

Abstract

The growing integration of mobile robots in shared workspaces requires efficient path planning and coordination between the agents, accounting for safety and productivity. In this work, we propose a digital model-based optimization framework for mobile manipulators in human-robot collaborative environments, in order to determine the sequence of robot base poses and the task scheduling for the robot. The complete problem is treated as black-box, and Particle Swarm Optimization (PSO) is employed to balance conflicting Key-Performance Indicators (KPIs). We demonstrate improvements in cycle time, task sequencing, and adaptation to human presence in a collaborative box-packing scenario.