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@article{shah2014,
title = {Trajectory Planning with Adaptive Control Primitives for Autonomous Surface
Vehicles Operating in Congested Civilian Traffic},
author = {Brual C. Shah},
journal = {IEEE International Conference on Intelligent Robots and Systems (IROS)},
year = {2014},
abstract = {abstract---We introduce a model-predictive trajectory planning algorithm for
unmanned surface vehicles (USVs) operating in congested civilian traffic. The
planner reasons about the availability of contingency maneuvers needed in case
of any of the civilian vessels breaches the International Regulations for the
Prevention of Collisions at Sea (COLREGs). Our exploratory study indicated
that implementing the envisioned planner requires significant speed up of
trajectory planning to cope with the dynamics of the scene, and evaluation of
collision risk. We describe a new method for efficiently searching 5D state
space for a dynamically feasible trajectory using adaptive control action
primitives. The algorithm estimates the congestion of the state space regions
to evaluate collision risk, and then dynamically scales action primitives used
during the search while preserving their dynamical feasibility. Our simulation
experiments demonstrate that this leads to a substantial increase in the
search efficiency and a decrease in the number of collisions, especially in
complex scenarios with a higher number of civilian vessels.}}