arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1602.04762cs.RO

Optimized and Trusted Collision Avoidance for Unmanned Aerial Vehicles using Approximate Dynamic Programming (Technical Report)

Stanford University(斯坦福大学)

Zachary N. Sunberg, Mykel J. Kochenderfer, Marco Pavone

英文摘要:

Safely integrating unmanned aerial vehicles into civil airspace is contingent upon development of a trustworthy collision avoidance system. This paper proposes an approach whereby a parameterized resolution logic that is considered trusted for a given range of its parameters is adaptively tuned online. Specifically, to address the potential conservatism of the resolution logic with static parameters, we present a dynamic programming approach for adapting the parameters dynamically based on the encounter state. We compute the adaptation policy offline using a simulation-based approximate dynamic programming method that accommodates the high dimensionality of the problem. Numerical experiments show that this approach improves safety and operational performance compared to the baseline resolution logic, while retaining trustworthiness.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑