센서학회지 (Journal of Sensor Science and Technology)

  • 제29권5호
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  • Pages.348-353
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  • 2020
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  • 1225-5475(pISSN)
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  • 2093-7563(eISSN)
한국센서학회 (The Korean Sensors Society)
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2차원 라이다 센서 데이터 분류를 이용한 적응형 장애물 회피 알고리즘

Adaptive Obstacle Avoidance Algorithm using Classification of 2D LiDAR Data

  • 이나라 (강원대학교 메카트로닉스공학과) ;
  • 권순환 (강원대학교 메카트로닉스공학과) ;
  • 유혜정 (강원대학교 메카트로닉스공학과)
  • Lee, Nara (Mechatronics Engineering, Kangwon National University) ;
  • Kwon, Soonhwan (Mechatronics Engineering, Kangwon National University) ;
  • Ryu, Hyejeong (Mechatronics Engineering, Kangwon National University)
  • 투고 : 2020.09.04
  • 심사 : 2020.09.24
  • 발행 : 2020.09.30
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초록

This paper presents an adaptive method to avoid obstacles in various environmental settings, using a two-dimensional (2D) LiDAR sensor for mobile robots. While the conventional reaction based smooth nearness diagram (SND) algorithms use a fixed safety distance criterion, the proposed algorithm autonomously changes the safety criterion considering the obstacle density around a robot. The fixed safety criterion for the whole SND obstacle avoidance process can induce inefficient motion controls in terms of the travel distance and action smoothness. We applied a multinomial logistic regression algorithm, softmax regression, to classify 2D LiDAR point clouds into seven obstacle structure classes. The trained model was used to recognize a current obstacle density situation using newly obtained 2D LiDAR data. Through the classification, the robot adaptively modifies the safety distance criterion according to the change in its environment. We experimentally verified that the motion controls generated by the proposed adaptive algorithm were smoother and more efficient compared to those of the conventional SND algorithms.

키워드

참고문헌

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