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구분 SCI
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학술지 A robust collision prediction and detection method based on neural network for autonomous delivery robots
Cited 11 time in scopus Download 100 time Share share facebook twitter linkedin kakaostory
저자
서성훈, 정훈
발행일
202304
출처
ETRI Journal, v.45 no.2, pp.329-337
ISSN
1225-6463
출판사
한국전자통신연구원 (ETRI)
DOI
https://dx.doi.org/10.4218/etrij.2021-0397
협약과제
21HR3600, 5G 기반 집배원 고중량 이동형 배달지원 기술 개발 및 상용화 실증, 김은혜
초록
For safe last-mile autonomous robot delivery services in complex environments, rapid and accurate collision prediction and detection is vital. This study proposes a suitable neural network model that relies on multiple navigation sensors. A light detection and ranging technique is used to measure the relative distances to potential collision obstacles along the robot's path of motion, and an accelerometer is used to detect impacts. The proposed method tightly couples relative distance and acceleration time-series data in a complementary fashion to minimize errors. A long short-term memory, fully connected layer, and SoftMax function are integrated to train and classify the rapidly changing collision countermeasure state during robot motion. Simulation results show that the proposed method effectively performs collision prediction and detection for various obstacles.
KSP 제안 키워드
Acceleration time, Autonomous robot, Collision Prediction, Complex environment, Detection Method, Last-Mile, Light detection and Ranging(LiDAR), Long-short term memory(LSTM), Relative Distance, Robot motion, Softmax Function
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