000010713 001__ 10713
000010713 005__ 20241024114738.0
000010713 0247_ $$2doi$$a10.24868/10713
000010713 245__ $$aObstacle Avoidance and Trajectory Optimization for an Autonomous Vessel Utilizing MILP Path Planning, Computer Vision based Perception and Feedback Control
000010713 269__ $$a2022-09-30
000010713 336__ $$aConference Proceedings
000010713 520__ $$aThe framework of an autonomous vessel is typically composed of three distinct and independent blocks known as the Guidance, Navigation and Control (GNC) system. Whilst independent in their focus, the subsystems are nonetheless reliant on one another for a vessel to competently operate. This paper investigates the potential of using a combination of advanced complementary techniques in the different GNC subsystems to improve upon the current common practices/state of the art in obstacle avoidance. The novel Guidance system is based on Mixed Integer Linear Programming (MILP). This optimisation technique allows quick, robust path planning with the possibility for a variety of constraints. The feasibility of this method will be investigated with the goal of providing optimal path planning in the presence of static and dynamic obstacles during autonomous sailing operations.
Within the Navigation System, a multi-modal neural network architecture is proposed for the perception branch to provide high-level situational awareness for collision avoidance purposes. The computer vision approach allows for the vessel type, position and orientation all to be extracted for encounters with both dynamic and static
obstacles using only imaging sensors. Two Control methods are studied in the paper, an error-based PID control strategy as well as an MPC control scheme. These two techniques will be compared to evaluate the performance and reviewing the suitability for use within the specific GNC scheme and the generic application environment.
This paper details the specific implementation of each system within the overall framework, presents simulation results of the path planner and control strategy, with a performance evaluation of the navigation system using an experimental dataset. The results obtained are analysed through qualitative discussion as well as quantitative
performance indicators and key conclusions are consequently drawn.
000010713 542__ $$fCC-BY
000010713 6531_ $$aGNC scheme
000010713 6531_ $$aMixed Linear-Integer Programming
000010713 6531_ $$aObstacle Avoidance strategy
000010713 6531_ $$aComputer Vision
000010713 6531_ $$aArtificial Intelligence
000010713 6531_ $$aFeedback Control
000010713 6531_ $$aModel-based Design
000010713 7001_ $$aGarofano, V$$uDelft University of Technology
000010713 7001_ $$aHepworth, M$$uDelft University of Technology
000010713 7001_ $$aShahin, R$$uDelft University of Technology
000010713 773__ $$tConference Proceedings of iSCSS
000010713 773__ $$jiSCSS 2022
000010713 8564_ $$95fd26538-c83f-415d-b369-96fae72c1db5$$s5071769$$uhttps://library.imarest.org/record/10713/files/10713.pdf
000010713 980__ $$aConference Proceedings