The Journal of Korea Robotics Society (로봇학회논문지)

Korea Robotics Society (한국로봇학회)
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A Comparative Study on Collision Detection Algorithms based on Joint Torque Sensor using Machine Learning

기계학습을 이용한 Joint Torque Sensor 기반의 충돌 감지 알고리즘 비교 연구

  • Jo, Seonghyeon (Kyungpook National University) ;
  • Kwon, Wookyong (Daegu-Gyeongbuk research center, Electronics and Telecommunications Research Institute (ETRI))
  • Received : 2020.01.22
  • Accepted : 2020.02.21
  • Published : 2020.05.31
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Abstract

This paper studied the collision detection of robot manipulators for safe collaboration in human-robot interaction. Based on sensor-based collision detection, external torque is detached from subtracting robot dynamics. To detect collision using joint torque sensor data, a comparative study was conducted using data-based machine learning algorithm. Data was collected from the actual 3 degree-of-freedom (DOF) robot manipulator, and the data was labeled by threshold and handwork. Using support vector machine (SVM), decision tree and k-nearest neighbors KNN method, we derive the optimal parameters of each algorithm and compare the collision classification performance. The simulation results are analyzed for each method, and we confirmed that by an optimal collision status detection model with high prediction accuracy.

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References

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