A Real-Time Receding Horizon Sequence Planner for Disassembly in A Human-Robot Collaboration Setting

TL;DR

This paper introduces a real-time receding horizon sequence planner for human-robot collaborative disassembly, optimizing task distribution, safety, and handling uncertainties in a dynamic environment.

Contribution

It presents a novel real-time planning method that considers human motion, safety, and product variability for automated disassembly in collaborative settings.

Findings

The planner successfully identifies disassembly sequences respecting safety constraints.

Robots can locate and disassemble components following the optimal sequence.

The system effectively collaborates with human operators considering real-time motion.

Abstract

Product disassembly is a labor-intensive process and is far from being automated. Typically, disassembly is not robust enough to handle product varieties from different shapes, models, and physical uncertainties due to component imperfections, damage throughout component usage, or insufficient product information. To overcome these difficulties and to automate the disassembly procedure through human-robot collaboration without excessive computational cost, this paper proposes a real-time receding horizon sequence planner that distributes tasks between robot and human operator while taking real-time human motion into consideration. The sequence planner aims to address several issues in the disassembly line, such as varying orientations, safety constraints of human operators, uncertainty of human operation, and the computational cost of large number of disassembly tasks. The proposed disassembly sequence planner identifies both the positions and orientations of the to-be-disassembled items, as well as the locations of human operator, and obtains an optimal disassembly sequence that follows disassembly rules and safety constraints for human operation. Experimental tests have been conducted to validate the proposed planner: the robot can locate and disassemble the components following the optimal sequence, and consider explicitly human operator's real-time motion, and collaborate with the human operator without violating safety constraints.