Optimized Scheduling and Positioning of Mobile Manipulators in Collaborative Applications

2025-12-19

AI Summary

In human–robot collaborative environments, mobile manipulators operating in shared workspaces face challenges in coordinated scheduling and precise localization. Method: This paper proposes a digital twin–driven end-to-end black-box optimization framework that jointly optimizes base mobility trajectories, manipulator pose sequences, and multi-task scheduling policies. It is the first to apply Particle Swarm Optimization (PSO) to solve this coupled multi-objective problem. A multi-KPI conflict-balancing mechanism is introduced to enable real-time, adaptive responses to dynamic human interference. Results: Evaluated on a box-assembly task, the framework significantly reduces cycle time, improves task-sequence rationality and system responsiveness, and maintains robustness and practicality under dynamic human disturbances. The integration of digital twin feedback enables closed-loop optimization and enhances operational reliability in unstructured, interactive settings.

Technology Category

• Intelligent Robots: Manipulation
• Planning, Routing, and Scheduling: Learning for Planning and Scheduling
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: The sharing economy

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.

Problem

• Optimizes mobile manipulator scheduling and positioning in collaborative environments
• Balances conflicting performance indicators using Particle Swarm Optimization
• Improves cycle time and task sequencing in human-robot collaboration

Innovation

• Digital model-based optimization framework for mobile manipulators
• Particle Swarm Optimization balances conflicting performance indicators
• Optimizes robot base poses and task scheduling collaboratively