Aerial robots are a well-established solution for exploration, monitoring, and inspection, thanks to their superior maneuverability and agility. However, in many environments, they risk crashing and sustaining damage after collisions. Traditional methods focus on avoiding obstacles entirely, but these approaches can be limiting, particularly in cluttered spaces or on weight-and compute-constrained platforms such as drones. This paper presents a novel approach to enhance drone robustness and autonomy by developing a path recovery and adjustment method for a high-speed collision-resilient aerial robot equipped with lightweight, distributed tactile sensors. The proposed system explicitly models collisions using pre-collision velocities, rates and tactile feedback to predict post-collision dynamics, improving state estimation accuracy. Additionally, we introduce a computationally efficient vector-field-based path representation that guarantees convergence to a user-specified path, while naturally avoiding known obstacles. Post-collision, contact point locations are incorporated into the vector field as a repulsive potential, enabling the drone to avoid obstacles while naturally returning to its path. The effectiveness of this method is validated through Monte Carlo simulations and demonstrated on a physical prototype, showing successful path following, collision recovery, and adjustment at speeds up to 3.7 m/s.

翻译：凭借卓越的机动性与敏捷性，空中机器人已成为勘探、监测与巡检领域的成熟解决方案。然而在许多环境中，它们面临碰撞后坠毁与结构损伤的风险。传统方法侧重于完全规避障碍物，但这些策略在复杂密集空间或受重量与计算资源限制的平台（如无人机）上存在局限性。本文提出一种创新方法，通过为配备轻量化分布式触觉传感器的高速抗碰撞空中机器人开发路径恢复与调整机制，以增强无人机鲁棒性与自主性。该系统利用碰撞前速度、角速度及触觉反馈对碰撞过程进行显式建模，以预测碰撞后动力学行为，从而提升状态估计精度。此外，我们引入一种计算高效的基于矢量场的路径表征方法，该方法在保证收敛至用户指定路径的同时，能自然规避已知障碍物。碰撞发生后，接触点位置作为排斥势场融入矢量场，使无人机在自然回归原路径的同时规避障碍物。通过蒙特卡洛仿真验证了该方法的有效性，并在物理样机上进行了演示，结果表明在速度高达3.7米/秒时仍能实现成功的路径跟踪、碰撞恢复与轨迹调整。