一类基于质心Voronoi图的多机器人部署与搜索策略的性能
Performance of a Class of Multi-Robot Deploy and Search Strategies based on Centroidal Voronoi Configurations
摘要：
本文考虑了一类多机器人系统的部署和搜索策略，并评估其性能。所使用的应用程序框架是一个系统的自主移动机器人配备所需的传感器和通信设备部署在搜索空间收集信息。关于搜索空间的信息的缺乏被建模为搜索空间上的不确定性密度分布。本文提出了一种{ \em combined deploy and search}（CDS）策略，它是对我们以前提出的{ \em sequential deploy and search}（SDS）策略的改进。使用Voronoi划分的最优部署策略形成了这两种搜索策略的基础。在机器人速度约束和传感器范围限制的情况下，分析了该策略，以使轨迹收敛，并给出了相应的机器人运动控制律。SDS和CDS战略相比，标准的贪婪和随机搜索策略的基础上所花费的时间，以实现减少的不确定性密度低于所需的水平。仿真实验揭示了几个重要的问题，如机器人的数量，机器人的速度，和它们的传感器范围限制的相对性能的策略的依赖。
英文摘要：
This paper considers a class of deploy and search strategies for multi-robot systems and evaluates their performance. The application framework used is a system of autonomous mobile robots equipped with required sensors and communication equipment deployed in a search space to gather information. The lack of information about the search space is modeled as an uncertainty density distribution over the search space. A {\em combined deploy and search} (CDS) strategy has been formulated as a modification to {\em sequential deploy and search} (SDS) strategy presented in our previous work. The optimal deployment strategy using Voronoi partition forms the basis for these two search strategies. The strategies are analyzed in presence of constraints on robot speed and limit on sensor range for convergence of trajectories with corresponding control laws responsible for the motion of robots. SDS and CDS strategies are compared with standard greedy and random search strategies on the basis of time taken to achieve reduction in the uncertainty density below a desired level. The simulation experiments reveal several important issues related to the dependence of the relative performances of the strategies on parameters such as number of robots, speed of robots, and their sensor range limits.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
