Dissipative Avoidance Feedback for Reactive Navigation Under Second-Order Dynamics
<|reference_start|>Dissipative Avoidance Feedback for Reactive Navigation Under Second-Order Dynamics: This paper introduces DAF (Dissipative Avoidance Feedback), a novel approach for autonomous robot navigation in unknown, obstacle-filled environments with second-order dynamics. Unlike traditional APF (Artificial Potential Field) methods, which rely on repulsive forces based solely on position, DAF employs a dissipative feedback mechanism that adjusts the robot's motion in response to both its position and velocity, ensuring smoother, more natural obstacle avoidance. The proposed continuously differentiable controller solves the motion-to-goal problem while guaranteeing collision-free navigation by considering the robot's state and local obstacle distance information. We show that the controller guarantees safe navigation in generic $n$-dimensional environments and that all undesired $\omega$-limit points are unstable under certain \textit{controlled} curvature conditions. Designed for real-time implementation, DAF requires only locally measured data from limited-range sensors (e.g., LiDAR, depth cameras), making it particularly effective for robots navigating unknown workspaces.<|reference_end|>
@article{smaili2024dissipative,
    title={Dissipative Avoidance Feedback for Reactive Navigation Under
  Second-Order Dynamics},
    author={Lyes Smaili, Zhiqi Tang, Soulaimane Berkane and Tarek Hamel},
    journal={arXiv preprint arXiv:2410.02903},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.02903},
    primaryClass={eess.SY cs.SY}
}