Air-to-air Micro Air Vehicle interceptor with an embedded mechanism and deep learning

Lee, Zhi Wei; Chin, Wai Hoe; Ho, Hann Woei

doi:10.1016/j.ast.2023.108192

Cited by 8 publications

(1 citation statement)

References 22 publications

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“…Exceptions regard [14], [15], although in these works the target platform is assumed in near hover conditions. A different application with respect to landing is explored in [16], where the interaction of two flying vehicles is considered: the target speed is limited to 1 m/s during tracking and the experimental results show that the interception occurs with the target in hovering conditions.…”

Section: Introductionmentioning

confidence: 99%

Vision-Based Air-to-Air Autonomous Landing of Underactuated VTOL UAVs

Roggi,

Gozzini,

Invernizzi

et al. 2024

IEEE/ASME Trans. Mechatron.

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This paper addresses the problem of Air-to-Air Autonomous Landing (AAAL) of a small underactuated Unmanned Aerial Vehicle (UAV) on a larger one (Carrier) in a non-cooperative manner using vision-based state estimation. A Kalman-filter based state estimator reconstructs the state of the Carrier relying on a camera mounted on the small UAV (Follower). Then, a three-layer hierarchical architecture is proposed. A hybrid automaton based on a quasi-time optimal approach is used at the position layer to ensure a safe and fast landing. An adaptive observer is developed at the velocity layer to track robustly the reference velocity coming from the position layer and to compensate for the lack of information about the Carrier acceleration. An attitude planner and a geometric stabilizer are used at the innermost layer. The proposed control architecture outperforms the one developed in a previous work by reducing the time to land up to 59% and increasing the accuracy up to 47%. Experimental tests have been performed to assess the performance of the proposed algorithm in representative scenarios.

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Section: Introductionmentioning

confidence: 99%