Obstacle Avoidance and Trajectory Optimization for an Autonomous Vessel Utilizing MILP Path Planning, Computer Vision based Perception and Feedback Control

Obstacle Avoidance and Trajectory Optimization for an Autonomous Vessel Utilizing MILP Path Planning, Computer Vision based Perception and Feedback Control

Garofano, V. (TU Delft Transport Engineering and Logistics)
Hepworth, M. (TU Delft Transport Engineering and Logistics)
Shahin, R. (Student TU Delft)

2022

The framework of an autonomous vessel is typically composed of three distinct and independent blocks known as the Guidance, Navigation and Control (GNC) system. This paper presents 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.

GNC scheme
Mixed Linear-Integer Programming
Obstacle Avoidance strategy
Computer Vision
Artificial Intelligence
Feedback Control
Model-based Design

http://resolver.tudelft.nl/uuid:42d7fbed-5018-4486-b0d5-16c22522a3b3

https://doi.org/10.24868/10713

2631-8741

Proceedings of the International Ship Control Systems Symposium, 16

16th International Naval Engineering Conference and Exhibition incorporating the International Ship Control Systems Symposium, INEC/iSCSS 2022, 2022-11-08 → 2022-11-10, Aula Congress Centre, Delft University of Technology, The Netherlands, Delft, Netherlands

Institutional Repository

journal article

© 2022 V. Garofano, M. Hepworth, R. Shahin