Adaptive Motion Planning for a Collaborative Robot Based on Prediction Uncertainty to Enhance Human Safety and Work Efficiency

Akira Kanazawa, Jun Kinugawa, Kazuhiro Kosuge

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

Industrial robots are expected to share the same workspace with human workers and work in cooperation with humans to improve the productivity and maintain the quality of products. In this situation, the worker's safety and work-time efficiency must be enhanced simultaneously. In this paper, we extend a task scheduling system proposed in the previous work by installing an online trajectory generation system. On the basis of the probabilistic prediction of the worker's motion and the receding horizon scheme for the trajectory planning, the proposed motion planning system calculates an optimal trajectory that realizes collision avoidance and the reduction of waste time simultaneously. Moreover, the proposed system plans the robot's trajectory adaptively based on updated predictions and its uncertainty to deal not only with the regular behavior of workers but also with their irregular behavior. We apply the proposed system to an assembly process where a two-link planar manipulator supports a worker by delivering parts and tools. After implementing the proposed system, we experimentally evaluate the effectiveness of the adaptive motion planning system.

Original languageEnglish
Article number8727974
Pages (from-to)817-832
Number of pages16
JournalIEEE Transactions on Robotics
Volume35
Issue number4
DOIs
Publication statusPublished - 2019 Aug

Keywords

  • Adaptive control
  • cognitive human-robot interaction
  • collision avoidance
  • industrial robot
  • learning and adaptive systems

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

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