This research proposes a unified guidance and control framework for Autonomous Underwater Vehicles (AUVs) based on the task priority control approach, incorporating various behaviors such as path following, terrain following, obstacle avoidance, as well as homing and docking to stationary and moving stations. The integration of homing and docking maneuvers into the task priority framework is thus a novel contribution of this paper. This integration allows, for example, to execute homing maneuvers close to uneven seafloor or obstacles, ensuring the safety of the AUV, as safety tasks can be given the highest priority. Furthermore, another contribution shown in the paper is that the proposed approach tackles a wide range of scenarios without ad hoc solutions. Indeed, the proposed approach is well suited for both the emerging trend of resident AUVs, which stay underwater for a long period inside garage stations, exiting to perform inspection and maintenance missions and homing back to them, and for AUVs that are required to dock to moving stations such as surface vehicles, or towed docking stations. The proposed techniques are studied in a simulation setting, taking into account the rich number of aforementioned scenarios.

The operation of drones in cluttered environments like forests and hilly areas is extremely difficult; it is impossible to use drones autonomously without having built-in information to detect and avoid obstacles. The vision based obstacle avoidance algorithm is presented in this paper, with extensions to UAV navigation. The proposed method is incorporated on a stereo vison multi copter using a block matching algorithm. The stereo vision baseline is based on horizontal configuration and computes the depth using a sum of absolute difference algorithm. The image processing node (LabVIEW vi) and the controller node are run on a remote laptop. This vi computes the distance between the multirotor and an obstacle and transmits depth data to an onboard flight controller through the MAVLink protocol. The algorithm efficiency was tested using the software in the loop on Gazebo simulator to analyze the performance of the UAV. The hardware in loop results are also shown in this paper after the successful flight test.

This report presents the analysis of a system which includes a maneuvering ship towing an underwater vehicle at the end of a long flexible cable. The equations of motion for both the cable and the underwater vehicle are also presented.The cable is imagined to consist of many interconnected short rigid segments. The equations of motion for the system are formulated twice on the basis of two hypotheses: first for a simple hypothesis regarding the inertia of an accelerated body in a fluid, and secondly, for a more complete and a more accurate hydrodynamic hypothesis.

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