This paper studies a multi-robot visibility-based pursuit-evasion problem in which a group of pursuer robots are tasked with detecting an evader within a two dimensional polygonal environment. The primary contribution is a novel formulation of the pursuit-evasion problem that modifies the pursuers' objective by requiring that the evader still be detected, even in spite of the failure of any single pursuer robot. This novel constraint, whereby two pursuers are required to detect an evader, has the benefit of providing redundancy to the search, should any member of the team become unresponsive, suffer temporary sensor disruption/failure, or otherwise become incapacitated. Existing methods, even those that are designed to respond to failures, rely on the pursuers to replan and update their search pattern to handle such occurrences. In contrast, the proposed formulation produces plans that are inherently tolerant of some level of disturbance. Building upon this new formulation, we introduce an augmented data structure for encoding the problem state and a novel sampling technique to ensure that the generated plans are robust to failures of any single pursuer robot. An implementation and simulation results illustrating the effectiveness of this approach are described.
翻译:本文研究一个多机器人可见度反射问题,在这个问题上,一组追追者机器人的任务是在两维多边形环境中发现逃避者,其主要贡献是,对追追者目标作出新颖的回避问题提出新的表述,要求即使单个追追者机器人失败,也仍然能够发现逃避者。这种新的制约要求两名追追者发现逃避者,因此,如果该团队的任何成员不作出反应,遭受临时传感器干扰/故障,或丧失工作能力,则会为搜寻工作提供冗余。现有方法,即使是那些旨在应对失败的方法,也依赖追追追者重新规划和更新搜索模式,以处理这类事件。相反,拟议的提法提出了一些计划,对某种程度的干扰具有内在的容忍性。在这一新表述的基础上,我们引入了一种强化的数据结构,将问题状态编码,并采用了一种新型的取样技术,以确保生成的计划能够应对任何单一追寻者机器人的失败。介绍了用以说明这一方法有效性的实施和模拟结果。