TY - GEN
T1 - Biologically Inspired Collision Avoidance without Distance Information
AU - Marinho, Thiago
AU - Amrouche, Massi
AU - Stipanovic, Dusan
AU - Cichella, Venanzio
AU - Hovakimyan, Naira
N1 - Funding Information:
VIII. CONCLUSION In this paper, we addressed the issue of avoiding collision with an unknown, uncooperative pop-up obstacle with limited sensing capabilities. The key feature of the proposed algorithms is that it does not require measurement of the distance to the obstacle. We introduced a collision avoidance algorithm that also guarantees a minimum time-to-collision. Overall, the control strategies developed in this work are designed to work along with a nominal tracking controller. Future extension of this work is to develop controllers and analysis for different vehicles, like car-like kinematics, that are suitable for self-driving cars. In addition, the avoidance control law will be derived at the dynamics level, where bounds on control rates can be derived that are desirable in a real-world application. IX. ACKNOWLEDGMENTS This work is supported by Air Force Office of Scientific Research, NASA Langley Research Center, the National Science Foundation NRI grants #1830639 and #2019-04791 (project accession no. 102028 from the USDA National Institute of Food and Agriculture). REFERENCES
Publisher Copyright:
© 2021 American Automatic Control Council.
PY - 2021/5/25
Y1 - 2021/5/25
N2 - Biological evidence shows that animals are capable of evading eminent collision without using depth information, relying solely on looming stimuli. In robotics, collision avoidance among uncooperative vehicles requires measurement of relative distance to the obstacle. Small, low-cost mobile robots and UAVs might be unable to carry distance measuring sensors, like LIDARS and depth cameras. We propose a control framework suitable for a unicycle-like vehicle moving in a 2D plane that achieves collision avoidance. The control strategy is inspired by the reaction of invertebrates to approaching obstacles, relying exclusively on line-of-sight (LOS) angle, LOS angle rate, and time-to-collision as feedback. Those quantities can readily be estimated from a monocular camera vision system onboard a mobile robot. The proposed avoidance law commands the heading angle to circumvent a moving obstacle with unknown position, while the velocity controller is left as a degree of freedom to accomplish other mission objectives. Theoretical guarantees are provided to show that minimum separation between the vehicle and the obstacle is attained regardless of the exogenous tracking controller.
AB - Biological evidence shows that animals are capable of evading eminent collision without using depth information, relying solely on looming stimuli. In robotics, collision avoidance among uncooperative vehicles requires measurement of relative distance to the obstacle. Small, low-cost mobile robots and UAVs might be unable to carry distance measuring sensors, like LIDARS and depth cameras. We propose a control framework suitable for a unicycle-like vehicle moving in a 2D plane that achieves collision avoidance. The control strategy is inspired by the reaction of invertebrates to approaching obstacles, relying exclusively on line-of-sight (LOS) angle, LOS angle rate, and time-to-collision as feedback. Those quantities can readily be estimated from a monocular camera vision system onboard a mobile robot. The proposed avoidance law commands the heading angle to circumvent a moving obstacle with unknown position, while the velocity controller is left as a degree of freedom to accomplish other mission objectives. Theoretical guarantees are provided to show that minimum separation between the vehicle and the obstacle is attained regardless of the exogenous tracking controller.
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U2 - 10.23919/ACC50511.2021.9482820
DO - 10.23919/ACC50511.2021.9482820
M3 - Conference contribution
AN - SCOPUS:85111921429
T3 - Proceedings of the American Control Conference
SP - 4383
EP - 4388
BT - 2021 American Control Conference, ACC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 American Control Conference, ACC 2021
Y2 - 25 May 2021 through 28 May 2021
ER -