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

2024-10-18

arXiv.org

AI Summary

To address path instability in high-speed UAVs caused by inevitable collisions in complex environments, this paper proposes a haptic-feedback-driven path recovery method. The approach explicitly incorporates collision events into the state estimation framework and constructs a vector-field-based path representation fused with contact-point information. Real-time binary tactile sensing detects collisions, while an online path adjustment mechanism—integrating collision dynamics modeling and potential-field guidance—enables autonomous post-collision obstacle avoidance and asymptotic convergence to the original trajectory. Evaluated through Monte Carlo simulations and physical experiments, the system achieves stable recovery and sustained path tracking after collisions at speeds up to 3.7 m/s. This significantly enhances the UAV’s collision resilience and autonomous navigation capability in dynamic obstacle-rich environments.

Technology Category

• Intelligent Robots: State Estimation
• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

Aerial robots are a well-established solution for exploration, monitoring, and inspection, thanks to their superior maneuverability and agility. However, in many environments of interest, they risk crashing and sustaining damage following collisions. Traditional methods focus on avoiding obstacles entirely to prevent damage, but these approaches can be limiting, particularly in complex environments where collisions may be unavoidable, or on weight and compute-constrained platforms. This paper presents a novel approach to enhance the robustness and autonomy of drones in such scenarios by developing a path recovery and adjustment method for a high-speed collision-resistant drone equipped with binary contact sensors. The proposed system employs an estimator that explicitly models collisions, using pre-collision velocities and rates to predict post-collision dynamics, thereby improving the drone's state estimation accuracy. Additionally, we introduce a vector-field-based path representation which guarantees convergence to the path. Post-collision, the contact point is incorporated into the vector field as a repulsive potential, enabling the drone to avoid obstacles while naturally converging to the original path. The effectiveness of this method is validated through Monte Carlo simulations and demonstrated on a physical prototype, showing successful path following and adjustment through collisions as well as recovery from collisions at speeds up to 3.7 m / s.

Problem

• Enabling drone path recovery after high-speed collisions
• Improving collision state estimation using tactile feedback
• Developing computationally efficient obstacle-avoiding path representation

Innovation

• Predicts post-collision dynamics using tactile feedback
• Uses vector-field-based path representation for convergence
• Incorporates contact points as repulsive potential for avoidance