A Tactile Feedback Approach to Path Recovery After High-Speed Impacts for Collision-Resilient Drones

Abstract

Highlights: What are the main findings? The proposed approach exploits tactile feedback from collisions to infer obstacle locationsin the environment. Our collision-aware estimator uses pre-collision velocities, rates and tactile feedback topredict post-collision velocities and rates alongside a vector-field-based path representationand recovery strategy to improve state estimation and ensure safe traversal ofcluttered environments at low computational cost. What are the implications of the main findings? The proposed method enables robust navigation in environments where traditionalvision- or range-based sensing is unreliable. The proposed method allows drones to recover in-flight from high-speed collisions andadapt their paths afterwards, preventing repeated impacts and improving resilience incluttered settings. 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 computationally 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 (Formula presented.) (Formula presented.) / (Formula presented.).