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Optimal Path Planning Among Mobile Sources of Threat in Complex Environments

复杂环境下移动威胁源的最优路径规划

基本信息

批准号：
1562339
负责人：
Efstathios Bakolas
金额：
$ 27.38万
依托单位：
University of Texas at Austin
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2016
资助国家：
美国
起止时间：
2016-09-01 至 2020-01-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1562339&HistoricalAwards=false
关键词：
Optimal Path Planning Among Mobile

项目摘要

This research effort will create novel algorithms for path planning and trajectory generation by a controlled mobile system operating in an environment populated by multiple mobile threats. These threats correspond to objects moving unpredictably, or possibly maliciously. The chosen worst-case approach models the mobile threats as pursuers actively and intelligently seeking a collision. The specific objective of the algorithm is to safely steer the controlled system to its destination, while minimizing the cumulative exposure to all threat sources. Accounting for all possible actions by all pursuers quickly results in a problem too large for even the most powerful computer. To make the calculations practical, the problem is divided into two parts -- first a short-term evasion strategy considering only the single most imminent threat, and second a long-term steering strategy with the goal of safely steering the controlled system to its goal destination while accounting for all threats. As air travel and automobile traffic become more autonomous, it becomes imperative for these systems to accommodate one or more uncommunicative, malfunctioning, or malicious agents. Finally, undergraduate and under-represented students will have the opportunity to work under the supervision of the PI in research projects related to the scope of this research effort via two different research programs which are offered every summer semester at the University of Texas at Austin.Problems of capture-evasion in the presence of multiple sources of threat are generally computationally intractable. This project approaches such problems using a combination of a short-term evasion strategy and a long-term steering strategy. The short-term strategy is based on a suitably defined threat metric, which is a state-dependent metric that measures the closeness of an evader from the highest-risk threat, while accounting for the dynamic and input constraints of both parties. The long-term strategy is primarily intended to steer the evader to its goal destination, while its cumulative exposure to every pursuer along its ensuing trajectory is kept low at all times. This project approaches these problems in the following stages: 1) Formulate the differential game describing multiple, potentially adversarial, decision makers as a path planning problem considering multiple mobile sources of threat. 2) Define a computationally tractable solution approach. 3) Leverage modern techniques such as sampling-based algorithms to address the path planning problem subject to non-trivial dynamic and input constraints, with time-varying state constraints to represent mobile obstacle. 4) Validate the solution to the capture-evasion problem by comparing its performance, in terms of the likelihood of the evader safely reaching its destination, against Monte Carlo simulations and other powerful but time-consuming numerical solution techniques.

这项研究工作将创建新的算法的路径规划和轨迹生成的控制移动的系统在一个环境中的人口由多个移动的威胁。这些威胁对应于不可预测或可能恶意移动的对象。所选择的最坏情况下的方法模型的移动的威胁的追求者积极和智能地寻求碰撞。该算法的具体目标是安全地将受控系统引导到其目的地，同时最大限度地减少对所有威胁源的累积暴露。考虑到所有追踪者的所有可能行动，即使是最强大的计算机也会很快产生一个太大的问题。为了使计算实用，该问题被分为两个部分-第一个短期规避策略，只考虑单一的最紧迫的威胁，第二个长期转向策略的目标是安全地引导控制系统到其目标目的地，同时考虑所有的威胁。随着航空旅行和汽车交通变得更加自主，这些系统必须容纳一个或多个无法通信、故障或恶意的代理。最后，本科生和代表性不足的学生将有机会在PI的监督下，通过两个不同的研究项目，在德克萨斯大学奥斯汀分校每个夏季学期提供与本研究工作的范围相关的研究项目。在存在多个威胁源的情况下，捕获逃避问题通常在计算上是棘手的。该项目采用短期规避策略和长期导向策略相结合的方法来解决这些问题。短期策略是基于一个适当定义的威胁度量，这是一个依赖于状态的度量，衡量最高风险威胁的规避者的接近程度，同时考虑到双方的动态和输入约束。长期策略的主要目的是引导逃避者到达目的地，同时使其对沿着每一个追踪者的累积暴露始终保持在较低水平。该项目在以下几个阶段处理这些问题：1）将描述多个潜在敌对决策者的微分博弈公式化为考虑多个移动的威胁源的路径规划问题。2)定义一个计算上易于处理的解决方案。3)利用现代技术，如基于采样的算法，以解决路径规划问题的非平凡的动态和输入约束，与时变状态约束，以代表移动的障碍。4)通过比较其性能，在逃避者安全到达目的地的可能性方面，与蒙特卡洛模拟和其他强大但耗时的数值求解技术，来验证捕获-逃避问题的解决方案。