TY - GEN
T1 - Guaranteed Collision Avoidance Based on Line-of-Sight Angle and Time-to-Collision
AU - Marinho, Thiago
AU - Amrouche, Massinissa
AU - Cichella, Venanzio
AU - Stipanovic, Dusan
AU - Hovakimyan, Naira
N1 - Publisher Copyright:
© 2018 AACC.
PY - 2018/8/9
Y1 - 2018/8/9
N2 - This paper deals with the problem of collision avoidance amongst autonomous vehicles that can be represented by the unicycle model on a 2D plane. The control strategy is inspired by the way animals navigate, relying only on line-of-sight (LOS) angle and time-to-collision (TTC) as feedback, made available by the on-board gimbaled monocular camera. The novelty of this work is in the proposed avoidance strategy that achieves collision avoidance without the measurement of distance, including the guarantees supported by Lyapunov-based analysis. Additionally, the proposed solution does not require an underlying logic that decides to avert or not the collision, therefore relaxing the conservatism of previous results. The proposed framework is also suitable for evading collisions in a scenario with multiple obstacles. Simulation results validate the collision avoidance law and illustrate its application to multiple obstacles.
AB - This paper deals with the problem of collision avoidance amongst autonomous vehicles that can be represented by the unicycle model on a 2D plane. The control strategy is inspired by the way animals navigate, relying only on line-of-sight (LOS) angle and time-to-collision (TTC) as feedback, made available by the on-board gimbaled monocular camera. The novelty of this work is in the proposed avoidance strategy that achieves collision avoidance without the measurement of distance, including the guarantees supported by Lyapunov-based analysis. Additionally, the proposed solution does not require an underlying logic that decides to avert or not the collision, therefore relaxing the conservatism of previous results. The proposed framework is also suitable for evading collisions in a scenario with multiple obstacles. Simulation results validate the collision avoidance law and illustrate its application to multiple obstacles.
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U2 - 10.23919/ACC.2018.8431871
DO - 10.23919/ACC.2018.8431871
M3 - Conference contribution
AN - SCOPUS:85052579987
SN - 9781538654286
T3 - Proceedings of the American Control Conference
SP - 4305
EP - 4310
BT - 2018 Annual American Control Conference, ACC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 Annual American Control Conference, ACC 2018
Y2 - 27 June 2018 through 29 June 2018
ER -