A Receding Horizon Multi-Objective Planner for Autonomous Surface Vehicles in Urban Waterways
Name
2007.08362.pdf
Description
Submitted version
Size
2.75 MB
Format
Adobe PDF
Checksum (MD5)
81612b90e0d3cd81eff20c66cb10d5a3
Author(s)
Shan, Tixiao
Wang, Wei
Englot, Brendan
Ratti, Carlo
Rus, Daniela
Date Issued
2020
Journal
Proceedings of the IEEE Conference on Decision and Control
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Shan, Tixiao, Wang, Wei, Englot, Brendan, Ratti, Carlo and Rus, Daniela. 2020. "A Receding Horizon Multi-Objective Planner for Autonomous Surface Vehicles in Urban Waterways." Proceedings of the IEEE Conference on Decision and Control, 2020-December.
Version
Original manuscript
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Department of Urban Studies and Planning
Massachusetts Institute of Technology. Department of Mechanical Engineering
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/CDC42340.2020.9304298
