Adaptive virtual power-based collision detection and isolation with link parameter estimation

Zhe Qiu, Ryuta Ozawa, Shugen Ma

Research output: Contribution to journalArticle

Abstract

We propose an adaptive virtual power-based collision detection and isolation approach for robotic manipulators with link parameter estimation. The power indexes are obtained using estimated virtual velocities and contact forces. The effectiveness of the power indexes is relied on precise knowledge of link parameters, which is commonly difficult to obtain. Therefore, in this paper, we propose a series of adaptive power indexes using a link parameter estimation scheme, in order to increase the robustness to parameter uncertainties. To show the statistical performance of collision detection and isolation using the proposed approach, we conduct multiple contact tasks using a 2 degree of freedom (DOF) experimental manipulator while considering uncertainties of the link parameters, and most collisions occurred on each link of the 2-DOF manipulator can be correctly detected and isolated. Additionally, the model-based and adaptive power indexes are compared for collision detection and collision isolation, respectively. Using the adaptive power indexes, the unique threshold can be smoothly determined for collision detection; meanwhile, the correct collision isolation rate increases.

Original languageEnglish
JournalAdvanced Robotics
DOIs
Publication statusAccepted/In press - 1 Jan 2020

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Keywords

  • Collision detection and isolation
  • link parameter estimation
  • robotic interactions
  • safe robots

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