상세정보
학술지
기계학습을 이용한 Joint Torque Sensor 기반의 충돌 감지 알고리즘 비교 연구
- 저자
- 조성현, 권우경
- 발행일
- 202005
- 출처
- 로봇학회논문지, v.15 no.2, pp.169-176
- ISSN
- 1975-6291
- 출판사
- 한국로봇학회
- 협약과제
- 20ZD1100, 대경권 지역산업 기반 ICT 융합기술 고도화 지원사업, 문기영
- 초록
- 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.
- KSP 제안 키워드
- 3 degree-of-freedom, Classification Performance, Collision Classification, Collision detection, Decision Tree(DT), Degrees of freedom(DOF), Detection model, Human-Robot Interaction(HRI), Joint torque sensor, Machine Learning Algorithms, Optimal parameters