Vision-based neural formation tracking control of multiple autonomous vehicles with visibility and performance constraints
•The feedback information about the leader is only the relative distance and bearing angle that are measured by the on-board vision sensors mounted on the follower.•No communication is required among the vehicles, and the follower’s acceleration is also not required.•The proposed formation controlle...
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| Published in: | Neurocomputing (Amsterdam) Vol. 492; pp. 651 - 663 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier B.V
01-07-2022
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| Subjects: | |
| Online Access: | Get full text |
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| Summary: | •The feedback information about the leader is only the relative distance and bearing angle that are measured by the on-board vision sensors mounted on the follower.•No communication is required among the vehicles, and the follower’s acceleration is also not required.•The proposed formation controller not only guarantees the collision and connectivity constraints, but also achieves satisfaction of prescribed performance specifications on the formation errors.
Limited sensing capability is one of features of onboard vision sensors. This paper addresses the formation tracking control problem for multiple nonholonomic autonomous vehicles with modeling uncertainties under limited sensing capabilities. Within the leader–follower formation control framework, the desired formation geometry of the vehicular team can be achieved by controlling the relative distance and angle between each pair of leader–follower vehicles. Each vehicle in the group is equipped with limited field-of-view onboard vision sensors to measure only the relative distance and angle with respect to its leader. For this scenario, each vehicle must remain within the visibility region relative to the leader in order to maintain the connectivity of the multi-vehicle system over time, while avoiding the possible collision with its leader. By incorporating the prescribed performance control methodology into the formation control design, the boundedness of the closed-loop system signals as well as the predefined performance indexes are achieved, where the neural networks (NNs) are employed to compensate for the uncertain dynamics. Visibility maintenance and collision avoidance between the leaders and followers are also proven mathematically. The proposed control strategy is decentralized in the sense that each vehicle uses only local relative information with respect to its leader to calculate its own control signals, as well as robust with respect to modeling uncertainties. Simulations and experiments on Pioneer-3AT robots are conducted to demonstrate the effectiveness of the proposed control strategy. |
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| ISSN: | 0925-2312 1872-8286 |
| DOI: | 10.1016/j.neucom.2021.12.056 |