A Receding Horizon Multi-Objective Planner for Autonomous Surface Vehicles in Urban Waterways
This work addresses navigation challenges for autonomous surface vehicles in constrained urban waterways, representing an incremental improvement in multi-objective motion planning.
The authors tackled path planning for autonomous surface vehicles in urban waterways by developing a receding horizon planner with multi-objective optimization, using lexicographic optimization to hierarchically penalize resources like collision risk and distance traveled. They validated the planner in simulated and real-world environments, demonstrating its capability for navigation in complex settings.
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.