Navigation underwater traditionally is done by keeping a safe distance from obstacles, resulting in "fly-overs" of the area of interest. An Autonomous Underwater Vehicle (AUV) moving through a cluttered space, such as a shipwreck, or a decorated cave is an extremely challenging problem and has not been addressed in the past. This paper proposed a novel navigation framework utilizing an enhanced version of Trajopt for fast 3D path-optimization with near-optimal guarantees for AUVs. A sampling based correction procedure ensures that the planning is not limited by local minima, enabling navigation through narrow spaces. The method is shown, both on simulation and in-pool experiments, to be fast enough to enable real-time autonomous navigation for an Aqua2 AUV with strong safety guarantees.
We present a synthesis of techniques for rotorcraft UAV navigation through unknown environments which may contain obstacles. D* Lite and probabilistic roadmaps are combined for path planning, together with stereo vision for obstacle detection and dynamic path updating. A 3D occupancy map is used to represent the environment, and is updated online using stereo data. The target application is autonomous helicopter-based structure inspections, which require the UAV to fly safely close to the structures it is inspecting. Results are presented from simulation and with real flight hardware mounted onboard a cable array robot, demonstrating successful navigation through unknown environments containing obstacles. Abstract The main goal of this research effort is to find a flyable collision-free path for an unmanned aerial vehicle (UAV) in a dynamic environment. Given that the UAV path planning needs to adapt in near real-time to the dynamic nature of the operational scenario, and to react rapidly to updates in the situational awareness, a modified artificial potential field (MAPF) approach is utilized to provide collision avoidance in view of pop-up threats and a random set of moving obstacles. To ensure a practically reachable trajectory, this paper proposes a constraint reference frame to develop MAPF so that the decomposed forces from MAPF can be matched with the physical constraints of the UAV for online adjustment. Simulations and experimental results provide promising validation in terms of the efficiency and scalability of the proposed approach.  Abstract Future Naval operations necessitate the incorporation of autonomous underwater vehicles into a collaborative network. In future complex missions, a forward look capability will also be required to map and avoid obstacles such as sunken ships. This work examines obstacle avoidance behaviors using a hypothetical forward-looking sonar for the autonomous underwater vehicle REMUS. Hydrodynamic coefficients are used to develop steering equations that model REMUS through a track of specified points similar to a real-world mission track. A two-dimensional forward-looking sonar model with a 120° horizontal scan and a 110 meter radial range is modeled for obstacle detection. Sonar mappings from geographic range-bearing coordinates are developed for implementation in MATLAB simulations. The product of bearing and range weighting functions form the gain factor for a dynamic obstacle avoidance behavior. The overall vehicle heading error incorporates this obstacle avoidance term to develop a path around detected objects. REMUS is a highly responsive vehicle in the model and is capable of avoiding multiple objects in proximity along its track path. Abstract This paper presents a reactive local level of an Obstacle Avoidance System for an Autonomous Underwater Vehicle (AUV). The specific requirements of the underwater world, the computational capacity and the sensors of the vehicle as well as its manoeuvrability were considered by the choice and the development of strategies used. Such requirements include the sea current information, the consideration of moving objects and the constrained view of sonar.