Modelling, trajectory control, path planning, and enhanced navigation of a biomimetic underwater vehicle for a novel propulsion system

Abstract

The present research focuses on enhancing the navigation performance of a tail-driven biomimetic autonomous underwater vehicle (BAUV) through a novel parallel robotic propulsion system. A detailed methodological proposal for deriving mathematical models of the vehicle’s hydrodynamics and propeller dynamics is detailed. To build the BAUV’s improved navigation framework, guidance systems, path tracking controllers, and path planners were designed. The vehicle's conceptual development and innovative propeller are shown using computer-aided designs. For the proposed propulsion system, existing theories to explain parallel robotic mechanisms were employed and developed to mathematically define the propeller’s kinematics, workspace, and dynamics. Moreover, the estimation of the hydrodynamics components of the BAUV is also one of the main contributions developed through this work. Forces, drag, added mass, and hydrostatic effects acting on the vehicle while swimming were also estimated to define the proposed design's expected performance properly. The control stage involves a two-phase process. First, the swimming performance of the vehicle is directly corrected by the proper regulation of the flapping of the caudal fin. This is done by incorporating classic and intelligent controllers to attain vector thrust and turning moment. Second, the vehicle is complementarily guided by a waypoint tracking controller that adapts the propeller’s expected performance based on desired trajectories. For the path planning stage, an adapted D* Lite algorithm was built based on the swimming locomotion of the vehicle. The designed methodology guides the BAUV into safer and collision-free paths inside unknown environments until it reaches a goal. The development of planning theories and their main advantages are also duly detailed. Finally, the results of this work demonstrated that enhanced swimming performances are attained by improving the way robotic fish propel itself and by designing adequate intelligent path-tracking controllers along with efficient path planners.