16 research outputs found
Warm-Started Optimized Trajectory Planning for ASVs
We consider warm-started optimized trajectory planning for autonomous surface
vehicles (ASVs) by combining the advantages of two types of planners: an A*
implementation that quickly finds the shortest piecewise linear path, and an
optimal control-based trajectory planner. A nonlinear 3-degree-of-freedom
underactuated model of an ASV is considered, along with an objective functional
that promotes energy-efficient and readily observable maneuvers. The A*
algorithm is guaranteed to find the shortest piecewise linear path to the goal
position based on a uniformly decomposed map. Dynamic information is
constructed and added to the A*-generated path, and provides an initial guess
for warm starting the optimal control-based planner. The run time for the
optimal control planner is greatly reduced by this initial guess and outputs a
dynamically feasible and locally optimal trajectory.Comment: Accepted to the 12th IFAC Conference on Control Applications in
Marine Systems, Robotics, and Vehicles (CAMS 2019
Collision Avoidance in Tightly-Constrained Environments without Coordination: a Hierarchical Control Approach
We present a hierarchical control approach for maneuvering an autonomous
vehicle (AV) in tightly-constrained environments where other moving AVs and/or
human driven vehicles are present. A two-level hierarchy is proposed: a
high-level data-driven strategy predictor and a lower-level model-based
feedback controller. The strategy predictor maps an encoding of a dynamic
environment to a set of high-level strategies via a neural network. Depending
on the selected strategy, a set of time-varying hyperplanes in the AV's
position space is generated online and the corresponding halfspace constraints
are included in a lower-level model-based receding horizon controller. These
strategy-dependent constraints drive the vehicle towards areas where it is
likely to remain feasible. Moreover, the predicted strategy also informs
switching between a discrete set of policies, which allows for more
conservative behavior when prediction confidence is low. We demonstrate the
effectiveness of the proposed data-driven hierarchical control framework in a
two-car collision avoidance scenario through simulations and experiments on a
1/10 scale autonomous car platform where the strategy-guided approach
outperforms a model predictive control baseline in both cases.Comment: 7 pages, 7 figures, accepted at ICRA 202