英语翻译AbstractWe propose real-time path planning schemes employing limited information for fully autonomous unmanned air vehicles (UAVs) in a hostileenvironment.Two main algorithms are proposed under different assumptions on the information use

英语翻译AbstractWe propose real-time path planning schemes employing limited information for fully autonomous unmanned air vehicles (UAVs) in a hostileenvironment.Two main algorithms are proposed under different assumptions on the information use
英语翻译
Abstract
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.
\3 2007 Elsevier Ltd.All rights reserved.
Keywords:Autonomous vehicles; Path planning; Limited data; Uncertainty; Static and dynamic threats
我的分不多 翻译的号再加分吧··

英语翻译AbstractWe propose real-time path planning schemes employing limited information for fully autonomous unmanned air vehicles (UAVs) in a hostileenvironment.Two main algorithms are proposed under different assumptions on the information use
抽象
我们建议实时路径规划计划雇用完全自主无人驾驶飞行器（无人机）在充满敌意的信息有限
环境.两个主要算法,提出根据所使用的信息和所面临的威胁不同的假设.它们包括
几个简单（计算驯服）的实时应用确定性规则.第一个算法使用极为有限的信息
（仅在周边地区就无人机钬檚当前位置概率风险）和内存,第二个利用更多
知识（位置和强度的威胁在无人机钬檚感官范围）和内存.可证明这两种算法收敛到一个给定的
目标点和生产出的风险几乎是一个给定的阈值比更不安全航点系列.特别是,我们的特点1
一类动态威胁（所谓的静态依赖的威胁）,这样可以有效地处理第二个算法等动态威胁,同时
保证其收敛到一个给定的目标.具有挑战性的情况下,试验使用该算法.
2007年爱思唯尔版权所有.
关键词：自主车辆;路径规划,有限的数据,不确定性,静态和动态威胁