随机动态环境下车辆路径的分布式自适应算法
Distributed and Adaptive Algorithms for Vehicle Routing in a Stochastic and Dynamic Environment
摘要：
在本文中，我们提出了分布式和自适应算法的运动协调的一组m自主车辆。车辆运行在一个凸的环境中有界的速度，必须服务的需求，其到达时间，位置和现场服务是随机的，目标是最小化期望的系统时间（等待加服务）的需求。一般的问题被称为m-车辆动态旅行修理工问题（m-DTRP）。先前已知的最佳控制算法依赖于集中式先验任务分配，并且对于环境的变化（例如，负载条件的变化）不鲁棒;因此，它们在涉及在时变环境中操作的自主车辆的自组织网络的场景中具有有限的适用性。首先，我们提出了一类新的1-DTRP问题的政策：（i）是可证明的最佳轻，重负荷条件下，和（ii）是自适应的，特别是，他们对负载条件的变化是强大的。其次，我们表明，分区政策，即环境之间的车辆和每个车辆在自己的区域遵循一定的规则集，在重载条件下是最佳的。最后，通过结合新的一类算法的1-DTRP与适当的分区政策，我们设计的分布式算法的m-DTRP问题，（i）是空间分布的，可扩展到大型网络，并适应网络的变化，（ii）是在一个恒定的因素，在重载条件下的最佳和稳定的系统在任何负载条件下。仿真结果进行了介绍和讨论。
英文摘要：
In this paper we present distributed and adaptive algorithms for motion coordination of a group of m autonomous vehicles. The vehicles operate in a convex environment with bounded velocity and must service demands whose time of arrival, location and on-site service are stochastic; the objective is to minimize the expected system time (wait plus service) of the demands. The general problem is known as the m-vehicle Dynamic Traveling Repairman Problem (m-DTRP). The best previously known control algorithms rely on centralized a-priori task assignment and are not robust against changes in the environment, e.g. changes in load conditions; therefore, they are of limited applicability in scenarios involving ad-hoc networks of autonomous vehicles operating in a time-varying environment. First, we present a new class of policies for the 1-DTRP problem that: (i) are provably optimal both in light- and heavy-load condition, and (ii) are adaptive, in particular, they are robust against changes in load conditions. Second, we show that partitioning policies, whereby the environment is partitioned among the vehicles and each vehicle follows a certain set of rules in its own region, are optimal in heavy-load conditions. Finally, by combining the new class of algorithms for the 1-DTRP with suitable partitioning policies, we design distributed algorithms for the m-DTRP problem that (i) are spatially distributed, scalable to large networks, and adaptive to network changes, (ii) are within a constant-factor of optimal in heavy-load conditions and stabilize the system in any load condition. Simulation results are presented and discussed.
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。
