Bearing-only obstacle avoidance based on unknown input observer and angle-dependent artificial potential field

Xiaohua Wang, Yan Liang, Shun Liu, Linfeng Xu

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

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.

Original languageEnglish
Article number31
JournalSensors
Volume19
Issue number1
DOIs
StatePublished - 1 Jan 2019

Keywords

  • Obstacle avoidance
  • Path planning
  • Unknown input observer

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