Bjørn-Olav Holtung Eriksen

A modified dynamic window algorithm for horizontal collision avoidance for AUVs

Much research has been done on the subject of collision avoidance (COLAV). However, few results are presented that consider vehicles with second-order nonholonomic constraints, such as autonomous underwater vehicles (AUVs). This paper considers the dynamic window (DW) algorithm for reactive horizontal COLAV for AUVs, and uses the HUGIN 1000 AUV in a case study. The DW algorithm is originally developed for vehicles with first-order nonholonomic constraints and is hence not directly applicable for AUVs without resulting in degraded performance. This paper suggests further developments of the DW algorithm to make it better suited for use with AUVs. In particular, a new method for predicting AUV trajectories using a linear approximation which accounts for second-order nonholonomic constraints is developed. The new prediction method, together with a modified search space, reduces the mean square prediction error to about one percent of the original algorithm. The performance and robustness of the modified DW algorithm is evaluated through simulations using a nonlinear model of the HUGIN 1000 AUV.

AIS-based vessel trajectory prediction

In order for autonomous surface vessels (ASVs) to avoid collisions at sea it is necessary to predict the future trajectories of surrounding vessels. This paper investigate the use of historical automatic identification system (AIS) data to predict such trajectories. The availability of AIS data have steadily increased in the last years as a result of more regulations, together with wider coverage through AIS integration on satellites and more land based receivers. Several AIS-based methods for predicting vessel trajectories already exist. However, these prediction techniques tend to focus on time horizons in the level of hours. The prediction time of our interest typically ranges from a few minutes up to about 15 minutes, depending on the maneuverability of the ASV. This paper presents a novel datadriven approach which recursively use historical AIS data in the neighborhood of a predicted position to predict next position and time. Three course and speed prediction methods are compared for one time step predictions. Lastly, the algorithm is briefly tested for multiple time steps in curved environments and shows good potential.

Modeling, Identification and Control of High-Speed ASVs: Theory and Experiments

This paper considers a powerful approach to modeling, identification, and control of high-speed autonomous surface vehicles (ASVs) operating in the displacement, semi-displacement, and planing regions. The approach is successfully applied to an \(8.45\,{\text {m}}\) long ASV capable of speeds up to \(18\,{\text {m}/\text {s}}\), resulting in a high-quality control-oriented model. The identified model is used to design four different controllers for the vessel speed and yaw rate, which have been tested through full-scale experiments in the Trondheimsfjord. The controllers are compared using various performance metrics, and two controllers utilizing a model-based feedforward term are shown to achieve outstanding performance.

A ship heading and speed control concept inherently satisfying actuator constraints

Satisfying actuator constraints is often not considered in the academic literature on the design of ship heading and speed controllers. This paper considers the use of a simplified dynamic window algorithm as a way to ensure that actuator constraints are satisfied. To accomplish this, we use the simplified dynamic window algorithm as a dynamic window-based controller (DWC) to guarantee that the velocities remain within a set of feasible boundaries, while simultaneously respecting the actuator constraints. We also develop a modified nonlinear ship model on which to test the proposed concept. The DWC is compared with a more traditional ship heading and speed controller, using performance metrics which consider both control accuracy and energy use.

MPC-Based mid-level collision avoidance for asvs using nonlinear programming

In this paper, we present a mid-level collision avoidance algorithm for autonomous surface vehicles (ASVs) based on model predictive control (MPC) using nonlinear programming. The algorithm enables avoidance of both static and moving obstacles, and following of a desired nominal trajectory if there is no danger of collision. We compare two alternative objective functions, where one is a quadratic function and the other is a nonlinear function designed to produce maneuvers observable for other vessels in compliance with rule 8 of the International Regulations for Preventing Collisions at Sea (COLREGS). The algorithm is implemented in the CASADI framework and uses the IPOPT solver. The performance of the algorithm is evaluated through simulations which show promising results. Furthermore, the algorithm is considered computationally feasible to run in real time. This algorithm serves as a base algorithm for further development in order to ensure full COLREGS compliance.