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We propose a novel receding horizon planner for an autonomous surface vehicle(ASV) performing path planning in urban waterways. Feasible paths are found byrepeatedly generating and searching a graph reflecting the obstacles observedin the sensor field-of-view. We also propose a novel method for multi-objectivemotion planning over the graph by leveraging the paradigm of lexicographicoptimization and applying it to graph search within our receding horizonplanner. The competing resources of interest are penalized hierarchicallyduring the search. Higher-ranked resources cause a robot to incur non-negativecosts over the paths traveled, which are occasionally zero-valued. Theframework is intended to capture problems in which a robot must manageresources such as risk of collision. This leaves freedom for tie-breaking withrespect to lower-priority resources; at the bottom of the hierarchy is astrictly positive quantity consumed by the robot, such as distance traveled,energy expended or time elapsed. We conduct experiments in both simulated andreal-world environments to validate the proposed planner and demonstrate itscapability for enabling ASV navigation in complex environments.

A Receding Horizon Multi-Objective Planner for Autonomous Surface Vehicles in Urban Waterways