Identification and Avoidance of Static and Dynamic Obstacles on Point Cloud for UAVs Navigation
This work addresses the challenge of efficient and real-time obstacle avoidance for UAVs in unknown scenarios, which is incremental as it builds on existing methods like the relative velocity approach.
The paper tackled the problem of UAV navigation in unknown environments with both static and dynamic obstacles by introducing a technique to distinguish them using only point cloud input and proposing a computationally efficient motion planning approach that integrates collision checks and motion constraints for both types of obstacles into one framework, achieving an average single step calculating time of less than 20 ms.
Avoiding hybrid obstacles in unknown scenarios with an efficient flight strategy is a key challenge for unmanned aerial vehicle applications. In this paper, we introduce a technique to distinguish dynamic obstacles from static ones with only point cloud input. Then, a computationally efficient obstacle avoidance motion planning approach is proposed and it is in line with an improved relative velocity method. The approach is able to avoid both static obstacles and dynamic ones in the same framework. For static and dynamic obstacles, the collision check and motion constraints are different, and they are integrated into one framework efficiently. In addition, we present several techniques to improve the algorithm performance and deal with the time gap between different submodules. The proposed approach is implemented to run onboard in real-time and validated extensively in simulation and hardware tests. Our average single step calculating time is less than 20 ms.