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.

Abstract
1. 서 론
2. 연구 대상 및 데이터 처리
    2.1 연구 대상 구성
    2.2 데이터 수집
    2.3 데이터 전처리
3. 충돌 감지를 위한 충돌 판별 모델 개발
    3.1 분류 알고리즘
    3.2 검증
    3.3 모델의 성능 평가
4. 충돌 판별 모델을 이용한 성능 비교
    4.1 최적의 Hyperparameter 설정
    4.2 모델 성능 비교
5. 결 론
References

• 조성현(Kyungpook National University) | Seonghyeon Jo
• 권우경(Daegu-Gyeongbuk research center, Electronics and Telecommunications Research Institute (ETRI)) | Wookyong Kwon Corresponding author