Dhanika Mahipala | Searcharxiv

arXiv2025

AUTOBargeSim: MATLAB(R) toolbox for the design and analysis of the guidance and control system for autonomous inland vessels

This paper introduces AUTOBargeSim, a simulation toolbox for autonomous inland vessel guidance and control system design. AUTOBargeSim is developed using MATLAB and provides an easy-to-use introduction to various aspects of autonomous inland navigation, including mapping, modelling, control design, and collision avoidance, through examples and extensively documented code. Applying modular design principles in the simulator structure allows it to be easily modified according to the user's requirements. Furthermore, a GUI interface facilitates a simple and quick execution. Key performance indices for evaluating the performance of the controller and collision avoidance method in confined space are also provided. The current version of AUTOBargeSim attempts to improve reproducibility in the design and simulation of marine systems while serving as a foundation for simulating and evaluating vessel behaviour considering operational, system, and environmental constraints.

eess.SY↗

arXiv2025

Design and Validation of an Intention-Aware Probabilistic Framework for Trajectory Prediction: Integrating COLREGS, Grounding Hazards, and Planned Routes

Collision avoidance capability is an essential component in an autonomous vessel navigation system. To this end, an accurate prediction of dynamic obstacle trajectories is vital. Traditional approaches to trajectory prediction face limitations in generalizability and often fail to account for the intentions of other vessels. While recent research has considered incorporating the intentions of dynamic obstacles, these efforts are typically based on the own-ship's interpretation of the situation. The current state-of-the-art in this area is a Dynamic Bayesian Network (DBN) model, which infers target vessel intentions by considering multiple underlying causes and allowing for different interpretations of the situation by different vessels. However, since its inception, there have not been any significant structural improvements to this model. In this paper, we propose enhancing the DBN model by incorporating considerations for grounding hazards and vessel waypoint information. The proposed model is validated using real vessel encounters extracted from historical Automatic Identification System (AIS) data.

cs.RO↗