Real-time path planning with limited information for autonomous unmanned air vehicles
Real-time path planning with limited information for autonomous unmanned air vehicles
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DOI：
10.1016/j.automatica.2007.07.023
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发表时间：
2008-03
期刊：
影响因子：
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通讯作者：
Yoonsoo Kim;D. Gu;I. Postlethwaite
中科院分区：
文献类型：
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作者：
Yoonsoo Kim;D. Gu;I. Postlethwaite
我们提出了基于有限信息的全自主无人飞行器（uav）在敌对环境中的实时路径规划方案。在对所使用的信息和所涉及的威胁的不同假设下，提出了两种主要算法。它们由实时应用程序的几个简单的（计算上可处理的）确定性规则组成。第一种算法使用极其有限的信息（仅相对于无人机当前位置的周围区域的概率风险）和记忆，第二种算法利用更多的知识（无人机感知范围内威胁的位置和强度）和记忆。两种算法均可证明收敛于给定目标点，并产生一系列风险几乎小于给定阈值的安全路径点。特别是，我们描述了一类动态威胁（所谓的静态依赖威胁），以便第二种算法可以有效地处理此类动态威胁，同时保证其收敛到给定目标。使用具有挑战性的场景来测试所提出的算法。
We propose real-time path planning schemes employing limited information for fully autonomous unmanned air vehicles (UAVs) in a hostile environment. Two main algorithms are proposed under different assumptions on the information used and the threats involved. They consist of several simple (computationally tractable) deterministic rules for real-time applications. The first algorithm uses extremely limited information (only the probabilistic risk in the surrounding area with respect to the UAV's current position) and memory, and the second utilizes more knowledge (the location and strength of threats within the UAV's sensory range) and memory. Both algorithms provably converge to a given target point and produce a series of safe waypoints whose risk is almost less than a given threshold value. In particular, we characterize a class of dynamic threats (so-called, static-dependent threats) so that the second algorithm can efficiently handle such dynamic threats while guaranteeing its convergence to a given target. Challenging scenarios are used to test the proposed algorithms.