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Sensors 2019, 19(1), 31; https://doi.org/10.3390/s19010031

Bearing-Only Obstacle Avoidance Based on Unknown Input Observer and Angle-Dependent Artificial Potential Field

School of Automation, Northwestern Polytechnical University, Xi’an 710072, China
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Received: 29 November 2018 / Revised: 18 December 2018 / Accepted: 19 December 2018 / Published: 21 December 2018
(This article belongs to the Special Issue Mobile Robot Navigation)
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Abstract

This paper presents the problem of obstacle avoidance with bearing-only measurements in the case that the obstacle motion is model-free, i.e., its acceleration is absolutely unknown, which cannot be dealt with by the mainstream Kalman-like schemes based on the known motion model. First, the essential reason of the collision caused by local minimum problem in the standard artificial potential field method is proved, and hence a revised method with angle dependent factor is proposed. Then, an unknown input observer is proposed to estimate the position and velocity of the obstacle. Finally, the numeric simulation demonstrates the effectiveness in terms of estimation accuracy and terminative time. View Full-Text
Keywords: path planning; obstacle avoidance; unknown input observer path planning; obstacle avoidance; unknown input observer
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Wang, X.; Liang, Y.; Liu, S.; Xu, L. Bearing-Only Obstacle Avoidance Based on Unknown Input Observer and Angle-Dependent Artificial Potential Field. Sensors 2019, 19, 31.

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