@article{ObstacleAvoidanceandTrajectoryOptimizationforanAutonomousVesselUtilizingMILPPathPlanning:10713,
      recid = {10713},
      author = {Garofano, V and Hepworth, M and Shahin, R},
      title = {Obstacle Avoidance and Trajectory Optimization for an  Autonomous Vessel Utilizing MILP Path Planning, Computer  Vision based Perception and Feedback Control},
      journal = {Conference Proceedings of iSCSS},
      address = {2022-09-30},
      abstract = {The 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.},
      url = {http://library.imarest.org/record/10713},
      doi = {https://doi.org/10.24868/10713},
}