HEPP: Hyper-efficient Perception and Planning for High-speed Obstacle Avoidance of UAVs (2505.17438v1)

Abstract: High-speed obstacle avoidance of uncrewed aerial vehicles (UAVs) in cluttered environments is a significant challenge. Existing UAV planning and obstacle avoidance systems can only fly at moderate speeds or at high speeds over empty or sparse fields. In this article, we propose a hyper-efficient perception and planning system for the high-speed obstacle avoidance of UAVs. The system mainly consists of three modules: 1) A novel incremental robocentric mapping method with distance and gradient information, which takes 89.5% less time compared to existing methods. 2) A novel obstacle-aware topological path search method that generates multiple distinct paths. 3) An adaptive gradient-based high-speed trajectory generation method with a novel time pre-allocation algorithm. With these innovations, the system has an excellent real-time performance with only milliseconds latency in each iteration, taking 79.24% less time than existing methods at high speeds (15 m/s in cluttered environments), allowing UAVs to fly swiftly and avoid obstacles in cluttered environments. The planned trajectory of the UAV is close to the global optimum in both temporal and spatial domains. Finally, extensive validations in both simulation and real-world experiments demonstrate the effectiveness of our proposed system for high-speed navigation in cluttered environments.

Overview of Hyper-efficient Perception and Planning for UAVs

The paper "HEPP: Hyper-efficient Perception and Planning for High-speed Obstacle Avoidance of UAVs" presents an advanced methodology aimed at enhancing the performance of uncrewed aerial vehicles (UAVs) in complex, cluttered environments. Emphasizing both perceptual and planning advances, this research addresses the prevailing challenges of high-speed UAV navigation where existing systems struggle to balance speed and obstacle avoidance.

Key Contributions

The research is structured around three principal modules:

1. Incremental Robocentric Mapping: A novel mapping technique is introduced that reduces computation time by 89.5% compared to existing methods. The mapping method utilizes both distance and gradient information to maintain an efficient and updated local map, benefiting from reduced time and memory overhead.
2. Obstacle-aware Topological Path Search: The paper introduces a unique path search method that evaluates multiple distinct paths for UAV navigation. This approach prioritizes obstacle awareness and facilitates the approximation of optimal UAV trajectories, which is crucial for high-speed settings.
3. Adaptive Gradient-based Trajectory Generation: This segment of the work focuses on generating high-speed trajectories using a time pre-allocation algorithm. The approach significantly lowers latency per iteration (79.24% less time than conventional systems when operating at speeds as high as 15 m/s) and maintains trajectory closeness to global optima in both temporal and spatial domains.

Numerical Findings

The system's performance is evidenced by its real-time efficiency, operating with milliseconds of latency at high speeds amidst cluttered environments. The UAVs demonstrated the capability to plan and execute swift yet safe trajectories, showcasing the robustness of the algorithmic advancements in real-world simulations.

Implications and Future Directions

The implications of this research extend to a variety of UAV applications requiring rapid navigation, such as search and rescue operations, environmental monitoring, and industrial inspections. From a theoretical standpoint, the integration of efficient spatial mapping with optimal trajectory planning represents a significant step forward in autonomous UAV technology.

Future developments may explore the scalability of HEPP in even more dynamic environments, including aerial vehicular traffic systems where real-time adaptability is paramount. Moreover, integrating additional sensory modalities or improved computational methods might further refine trajectory safety and reduce latency.

Conclusion

The HEPP framework presented in this paper effectively demonstrates a comprehensive solution for high-speed UAV navigation in cluttered environments by significantly enhancing perception and planning capabilities. The technical contributions represent notable advancements in both mapping efficiency and trajectory optimization, positioning this research as a resourceful foundation for future high-speed UAV flight innovations.