Collision Avoidance from Multiple Passive Agents with Partially Predictable Behavioropen access
- Authors
- Zuhaib, Khalil Muhammad; Khan, Abdul Manan; Iqbal, Junaid; Ali, Mian Ashfaq; Usman, Muhammad; Ali, Ahmad; Yaqub, Sheraz; Lee, Ji Yeong; Han, Changsoo
- Issue Date
- Sep-2017
- Publisher
- MDPI
- Keywords
- collision avoidance; multiple passive agents; Mobile Robot Navigation; pedestrian environment; kinodynamic planning; velocity obstacle
- Citation
- Applied Sciences-basel, v.7, no.9, pp.1 - 18
- Indexed
- SCIE
SCOPUS
- Journal Title
- Applied Sciences-basel
- Volume
- 7
- Number
- 9
- Start Page
- 1
- End Page
- 18
- URI
- https://scholarworks.bwise.kr/erica/handle/2021.sw.erica/9042
- DOI
- 10.3390/app7090903
- Abstract
- Navigating a robot in a dynamic environment is a challenging task, especially when the behavior of other agents such as pedestrians, is only partially predictable. Also, the kinodynamic constraints on robot motion add an extra challenge. This paper proposes a novel navigational strategy for collision avoidance of a kinodynamically constrained robot from multiple moving passive agents with partially predictable behavior. Specifically, this paper presents a new approach to identify the set of control inputs to the robot, named control obstacle, which leads it towards a collision with a passive agent moving along an arbitrary path. The proposed method is developed by generalizing the concept of nonlinear velocity obstacle (NLVO), which is used to avoid collision with a passive agent, and takes into account the kinodynamic constraints on robot motion. Further, it formulates the navigational problem as an optimization problem, which allows the robot to make a safe decision in the presence of various sources of unmodelled uncertainties. Finally, the performance of the algorithm is evaluated for different parameters and is compared to existing velocity obstacle-based approaches. The simulated experiments show the excellent performance of the proposed approach in term of computation time and success rate.
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Collections - COLLEGE OF ENGINEERING SCIENCES > DEPARTMENT OF ROBOT ENGINEERING > 1. Journal Articles
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