论文标题
最小曝光DUBINS定向急救问题
Minimal Exposure Dubins Orienteering Problem
论文作者
论文摘要
不同的应用程序，例如环境监测和军事行动，要求观察预定义的目标位置，并且自动移动机器人可以协助这些任务。在这种情况下，定向问题（OP）是一个众所周知的路由问题，其中的目标是通过访问最有意义的位置来最大化目标函数，但是，尊重有限的旅行预算（例如，长度，时间，能源）。但是，用于路由问题的传统配方通常忽略了某些环境特殊性，例如障碍或威胁区域。在本文中，我们考虑了在有已知部署的传感器领域的情况下考虑Dubins车辆的OP。我们提出了一种新型的多目标配方，称为最小暴露于DUBINS定向急救问题（MEDOP），其主要目标是：（i）最大化收集的奖励，（ii）最大程度地减少剂的暴露，即被检测到的概率。该解决方案基于一种进化算法，该算法迭代地改变了要访问的位置的子集和序列，每个位置的方向以及用于确定路径的转弯半径。结果表明，我们的方法可以有效地找到一套可以同时优化两个目标的解决方案。
Different applications, such as environmental monitoring and military operations, demand the observation of predefined target locations, and an autonomous mobile robot can assist in these tasks. In this context, the Orienteering Problem (OP) is a well-known routing problem, in which the goal is to maximize the objective function by visiting the most rewarding locations, however, respecting a limited travel budget (e.g., length, time, energy). However, traditional formulations for routing problems generally neglect some environment peculiarities, such as obstacles or threatening zones. In this paper, we tackle the OP considering Dubins vehicles in the presence of a known deployed sensor field. We propose a novel multi-objective formulation called Minimal Exposure Dubins Orienteering Problem (MEDOP), whose main objectives are: (i) maximize the collected reward, and (ii) minimize the exposure of the agent, i.e., the probability of being detected. The solution is based on an evolutionary algorithm that iteratively varies the subset and sequence of locations to be visited, the orientations on each location, and the turning radius used to determine the paths. Results show that our approach can efficiently find a diverse set of solutions that simultaneously optimize both objectives.
