论文标题
城市水道上自动表面车辆的后退视野多目标计划者
A Receding Horizon Multi-Objective Planner for Autonomous Surface Vehicles in Urban Waterways
论文作者
论文摘要
我们为在城市水道中执行路径规划的自动表面车辆（ASV）的新型水平计划者提出了新颖的水平计划。可行的路径是通过反复生成和搜索图表来反映传感器视野中观察到的障碍的图形来找到的。我们还通过利用词典优化的范式并将其应用于我们回收的地平线计划者中的图形搜索，从而在图上提出了一种用于多目标运动计划的新方法。搜索过程中，竞争资源在层次上受到惩罚。较高的资源导致机器人在行进的道路上产生非负成本，这有时是零值。该框架旨在捕获机器人必须管理资源（例如碰撞风险）的问题。这为较低优先级的资源留下了抢七的自由；在层次结构的底部是机器人消耗的严格正数，例如行进的距离，消耗的能量或经过的时间。我们在模拟和现实世界环境中进行实验，以验证提出的计划者，并证明其在复杂环境中启用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.
