Multi objective Energy Optimization Of Locomotions For Underwater Snake Robot
Biologically inspired Autonomous Underwater Vehicles (AUVs) have been developed in the recent decades. is thesis focuses on the AUVs that are biologically inspired by snakes, called Underwater Snake Robots (USRs) A well known issue of the USRs, or any AUVs, is the longterm autonomy. To achieve this, energy ecient approaches are required. Many studies have considered single objective optimization problems regarding the energy eciency of the USR, but almost none with Multi Objective Optimization Problems (MOPs) is thesis presents MOPs of dierent locomotions of the USR. e presented MOPs consider the energy ecient optimization of maximizing the forward velocity, while minimizing the power consumption of the USR. For computing the ecient motion paerns, two Multi Objective Evolutionary Algorithms (MOEAs) called Non dominated Sort Genetic Algorithm II (NSGA II) and Hypervolume Estimation Algorithm for Multi objective Optimization (HypE) are applied. A challenging topic of the USR, is their adaptability of dierent locomotions. Dierent locomotions of the USR give rise to dierent search spaces for optimization. We present simulation studies of the two most common snake locomotions: (i) lateral undulation and (ii) eel like motion. Furthermore, we also present and investigate three altered motion paern of the USR. e aim of the altered locomotions is to let the MOEAs generate ecient locomotions through evolutionary, which we do not know the gait of. From the simulation results, it turns out that one of the altered motion paern approximates a motion similar to the lateral undulation. is motion paern is generated based on Fourier series. e obtained simulation results are based on optimization with optimal Genetic Algorithm (GA) parameters, found by numerous presimulations of the MOPs. Since this is multi objective optimization problems, the end results will be in the form of Pareto fronts. ese Pareto fronts can be used as trade os for selecting the forward velocity and power consumption of the USR. Additional to the optimization results of the MOPs, we present multivariate analysis of the simulation results using Principal Component Analysis (PCA) for nding relationships between the motion paerns. Furthermore, through the analysis, some assumptions on the shape of the altered locomotion can be given. Based on the results from the PCA, we also present regression models computed by Partial Least Square Regression (PLSR) for predicting the optimal gait parameters using the objective values from the Pareto fronts.