A genetic-based effective approach to path-planning of autonomous underwater glider with upstream-current avoidance in variable oceans

• Published in: Soft computing (Berlin, Germany) Germany), 2017-09, Vol.21 (18), p.5369-5386
• Main Authors: Shih, Chien-Chou, Horng, Mong-Fong, Pan, Tien-Szu, Pan, Jeng-Shyang, Chen, Chun-Yu
• Format: Article
• Language: English
• Subjects: Artificial Intelligence; Autonomous underwater vehicles; Avoidance; Computational Intelligence; Control; Efficiency; Engineering; Genetic algorithms; Heuristic; Kinematics; Mathematical analysis; Mathematical Logic and Foundations; Mechatronics; Methodologies and Application; Ocean currents; Oceans; Optimization; Optimization algorithms; Optimization techniques; Path planning; Prediction models; Robotics; Sampling; Underwater vehicles; Upstream; Velocity
• ISSN: 1432-7643; 1433-7479
• DOI: 10.1007/s00500-016-2122-1

In this work, an exponential effective function (EEF) is developed as fitness function applied in a hybrid-Genetic Algorithm (hybrid-GA) to propose a genetic-based effective approach to the glider path-planning of ocean-sampling mission in variable oceans. The proposed EEF is such an objective function that is able to be implemented in optimization algorithm such as Genetic Algorithm (GA) for evaluation of the fittest path. In consideration of the glider path-planning problem (GPP), two motivations are driven by the proposed approach to the glider path-planning in discovery of: (1) a reachable path with the upstream-current avoidance (UCA) in variable oceans; (2) an efficient path for the glider ocean-sampling mission. The exponential combination of the glider motion and current effects as well as the cruising distance benefits the path in terms of reachability and efficiency. The reachability is the first motivation to discover a reachable path implemented by the scheme of UCA, while the efficiency is the second motivation to shorten the cruising distance. Meanwhile, the stabilized path solution is accomplished by hybrid-GA. In variable oceans, currents severely impact the path solution and lead the global optimum to absence. Therefore, alternative is to discover an optimal path with the minimum upstream-current sub-paths to approximate the minimal cruising distance in the condition that the discovered cruising distance should be less than the glider cruising range. To deeply analyze the path reachability, two theorems are developed to verify the conditions of the downstream-current angle (DCA). To evaluate the path-planning performances, 6 state-of-the-art fitness functions are studied and used to make a fair comparison with the EEF in hybrid-GA. First of all, 112 scenarios are created in the restricted random current variations (RRCV). Secondly, 21 scenarios are created in the near-real Kuroshio Current of east Taiwan (KCET) introducing from an ocean prediction model. These scenarios are designed to evaluate fairly the EEF in hybrid-GA. Numeric results show that whether the RRCV or the KCET, the proposed EEF indeed is able to discover the optimal path with the benefits of reachability and efficiency. Therefore, the proposed genetic-based effective approach is well developed to solve the GPP in variable oceans.