Using Hill Climb Modular Assembler Encoding and Differential Evolution to evolve modular neuro-controllers of an autonomous underwater vehicle acting as a Magnetic Anomaly Detector

In order for any construction to arise on the sea bottom, the area around has to be carefully cleaned from all dangerous objects including ferromagnetic objects like ammunition or the remains of sea mines. Magnetic anomaly detectors (MAD) which are composed of a set of magnetometers are applied to detect such objects. The combined signal of all the magnetometers moving near the sea bottom is a base for detecting anomalies in the Earth magnetic field produced by ferromagnetic objects. To carry the magnetometers, underwater vehicles can be used. However, in order for the vehicles to be able to fulfill the role of MAD, they have to move near the sea bottom in a tight swarm formation. In the paper, a modular neuro-evolutionary controller is presented which leads each swarm member along a desired trajectory, with the time synchronization, and at the certain distance from the sea bottom. In order to design the controller, two neuro-evolutionary algorithms were applied, i.e. Hill Climb Modular Assembler Encoding (HCMAE) and Differential Evolution (DE). During tests carried out in simulation conditions, initially in 2D and then also in a 3D environment, under the influence of sea current, neuro-controllers evolved by HCMAE and DE were compared with algorithmic and PID-like controllers. The tests showed that the solutions proposed in the paper are more effective than the reference solutions and proved that even in unfavorable underwater conditions it is possible to precisely keep the trajectory specified both spatially and temporally. (C) 2022 The Author(s). Published by Elsevier B.V.

Automated summary

This paper presents a modular neuro-evolutionary controller for guiding underwater vehicles to move along a desired trajectory and maintain a certain distance from the sea bottom in an underwater environment. The controller outperforms traditional controllers in simulation tests.