Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162467
Title: Real-time avoidance strategy of dynamic obstacles via half model-free detection and tracking with 2D lidar for mobile robots
Authors: Dong, Huixu
Weng, Ching-Yen
Guo, Chuangqiang
Yu, Haoyong
Chen, I-Ming
Keywords: Engineering::Mechanical engineering::Robots
Issue Date: 2021
Source: Dong, H., Weng, C., Guo, C., Yu, H. & Chen, I. (2021). Real-time avoidance strategy of dynamic obstacles via half model-free detection and tracking with 2D lidar for mobile robots. IEEE/ASME Transactions On Mechatronics, 26(4), 2215-2225. https://dx.doi.org/10.1109/TMECH.2020.3034982
Journal: IEEE/ASME Transactions on Mechatronics
Abstract: Avoidance is a necessary capability for a mobile robot to perform tasks, such as delivering objects in household or industrial scenarios. The existing avoidance strategy based on timed elastic band local planner and cost-map provided by robotics operating system cannot realize the excellent performance when a robot and an obstacle both move. In this article, we present a real-time, simple, and reliable approach to detecting and tracking obstacles via a two-dimensional lidar in dynamic scenarios where the mobile robot and the obstacle are moving. Obstacles are represented by a set of points against their outlines and the information of obstacles is initialized and updated via the raw laser measurement. First, the obstacle is detected by three main steps: preprocessing, segmentation, and merging, classification of consequent measurements. Second, we use a hierarchical method to realize data associations for figuring out the corresponding matches among obstacles with the consecutive time. Last, after doing the data association, we need to estimate the motion of the dynamic obstacle for being tracked by the Kalman filter. Extensive experiments performed in the simulation and practical scenarios indicate that the proposed method enables a mobile robot to perform dynamic avoidances efficiently [Real-time Avoidance Strategy of Dynamic Obstacles via Half Model-free Detection and Tracking (T-MECH)].
URI: https://hdl.handle.net/10356/162467
ISSN: 1083-4435
DOI: 10.1109/TMECH.2020.3034982
Rights: © 2020 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:RRC Journal Articles

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