A Receding Horizon Multi-Objective Planner for Autonomous Surface Vehicles in Urban Waterways
A Receding Horizon Multi-Objective Planner for Autonomous Surface Vehicles in Urban Waterways
复制标题
城市水道自主水面车辆的后退多目标规划器
DOI：
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发表时间：
2020
期刊：
影响因子：
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通讯作者：
D. Rus
中科院分区：
文献类型：
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作者：
Tixiao Shan;Wei Wang;Brendan Englot;C. Ratti;D. Rus
我们提出了一种新颖的后退地平线规划器，用于在城市水道中执行路径规划的自动水面车辆（ASV）。通过重复生成和搜索反映传感器视野中观察到的障碍物的图表来找到可行的路径。我们还提出了一种在图上进行多目标运动规划的新方法，通过利用词典优化的范例并将其应用于我们的后退地平线规划器中的图搜索。在搜索过程中，感兴趣的竞争资源会受到分级惩罚。排名较高的资源会导致机器人在行驶路径上产生非负成本，这些成本有时为零。该框架旨在解决机器人必须管理资源（例如碰撞风险）的问题。这为优先级较低的资源提供了打破平局的自由；层次结构底部是机器人消耗的严格正数，例如行驶的距离、消耗的能量或经过的时间。我们在模拟和现实环境中进行了实验，以验证所提出的规划器并展示其在复杂环境中实现 ASV 导航的能力。
We propose a novel receding horizon planner for an autonomous surface vehicle (ASV) performing path planning in urban waterways. Feasible paths are found by repeatedly generating and searching a graph reflecting the obstacles observed in the sensor field-of-view. We also propose a novel method for multi-objective motion planning over the graph by leveraging the paradigm of lexicographic optimization and applying it to graph search within our receding horizon planner. The competing resources of interest are penalized hierarchically during the search. Higher-ranked resources cause a robot to incur non-negative costs over the paths traveled, which are occasionally zero-valued. The framework is intended to capture problems in which a robot must manage resources such as risk of collision. This leaves freedom for tie-breaking with respect to lower-priority resources; at the bottom of the hierarchy is a strictly positive quantity consumed by the robot, such as distance traveled, energy expended or time elapsed. We conduct experiments in both simulated and real-world environments to validate the proposed planner and demonstrate its capability for enabling ASV navigation in complex environments.