P-CAL: Pre-computed alternative lanes for aggressive aerial collision avoidance,”
The 12th International Conference on Field and Service Robotics (FSR), Tokyo, Japan(2019)
摘要
We here address the issue of air vehicles flying autonomously at a high speed in complex environments. Typically, autonomous navigation through a complex environment requires a continuous heuristic search on a graph generated by a k-connected grid or a probabilistic scheme. The process is expensive especially if the paths must be kino-dynamically feasible. Aimed at tackling the problem from a different angle, we consider the case that the environment is mostly known from a prior map. The proposed method suggests the computation needed to find safe paths during fast flight can be greatly reduced if we pre-compute and carefully arrange a set of alternative paths before the flight. During the navigation, the vehicle selects a pre-computed path to navigate without the need to generate a new path. The result is that majority of the processing is migrated to offline path generation. Effectively, the onboard computation is significantly reduced, taking< 3% of a CPU thread on a modern embedded computer. In experiments, it enables a lightweight aerial vehicle to maneuver aggressively through a cluttered forest environment at 10m/s.
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要