An Approach to Multi-Objective Path Planning Optimization for Underwater Gliders

Abstract

Underwater gliders are energy-efficient vehicles that rely on changes in buoyancy in order to convert up and down movement into forward displacement. These vehicles are conceived as multi-sensor platforms, and can be used to collect ocean data for long periods in wide range areas. This endurance is achieved at the cost of low speed, which requires extensive planning to ensure vehicle safety and mission success, particularly when dealing with strong ocean currents. As gliders are often involved on missions that pursue multiple objectives (track events, reach a target point, avoid obstacles, sample specified areas, save energy), path planning requires a way to deal with several constraints at the same time; this makes glider path planning a multi-objective (MO) optimization problem. In this work, we analyse the usage of the non-dominated sorting genetic algorithm II (NSGA-II) to tackle a MO glider path planning application on a complex environment integrating 3D and time varying ocean currents. Multiple experiments using a glider kinematic simulator coupled with NSGA-II, combining different control parameters were carried out, to find the best parameter configuration that provided suitable paths for the desired mission. Ultimately, the system described in this work was able to optimize multi-objective trajectories, providing non dominated solutions. Such a planning tool could be of great interest in real mission planning, to assist glider pilots in selecting the most convenient paths for the vehicle, taking into account ocean forecasts and particular characteristics of the deployment location.

A Consolidated Review of Path Planning and Optimization Techniques: Technical Perspectives and Future Directions

“…Carlos Miguel et al [197] worked on underwater glider (UWG) vehicles to ensure their mission success and safety. UWG is considered energy-efficient vehicles, and for performing journeys, they are equipped with sensors that collect data from their surroundings.…”

Overview of a new Ocean Glider Navigation System: OceanGNS

“…Petillo et al (2015) presented a way of adjusting the vehicle depth to track the ocean's thermocline. Lucas et al (2019) took a different approach applying a sorting genetic algorithm. This technique has been expanded for tracking ocean fronts (Petillo et al, 2015).…”

Measuring Smoothness as a Factor for Efficient and Socially Accepted Robot Motion

“…Typically, these algorithms address also minimizing the path length, and maximizing the path safety. Hence, Reference [9,13] propose the use of the elitist NSGA-II (non-dominated sorting genetic algorithm) [14] to simultaneously compute different solutions to PP. To obtain these paths, a set of factors in a fitness function are modified, and these modifications produce paths with different features.…”