Path planning algorithms for Autonomous underwater Vehicles

Abstract

Autonomous underwater vehicles (AUVs) are self-powered underwater robots, used in newlineseveral marine applications including military missions, surveillance, oceanographic newlinesurveys, and bathymetric data collection in underwater environment. Path planning newlinedeals with finding out an optimal or sub-optimal path between an initial position and the newlinedestination under specific environmental constraints. Path planning is greatly influenced newlineby the oceanic environment. The path planning can be classified as local, global, and newlinehybrid. The diversity of the underwater environment and unavailability of GPS signal newlinemakes the path planning of AUV a complicated task. Furthermore, various control newlinecomplexities are also encountered for path planning control owing to communication newlineconstraints in underwater medium, uncertainties in parameters and dynamics in newlinehydrodynamic ocean currents and disturbances. Thus, there is a great challenge to newlinedevelop efficient optimized control algorithms in presence of communication to achieve newlinepath planning effectively. The information of the desired path of the AUV should be newlineknown to ensure safety in all stages of a mission. The underwater environment is newlineclassified as predictable and unpredictable based on the availability of information newlineabout the environment. A review on reported path planning methods in the literature newlineenvisages opportunities for applying several control schemes to address the challenges. newlineSliding mode controller is a very successful robust control approach to achieve the newlinedesired performance of a system with uncertainties. Therefore, the thesis first develops newlinean adaptive local path planning control algorithm for resolving uncertainties in local newlinepath planning using sliding mode control. The adaptive sliding mode control (AFSMC) newlineis proposed to achieve the coordinated motion of path planning with variable added newlinemass in presence of communication constraints. Further to resolve the constraints due to newlinethe quantizers and computational complexity, a global path planning approach