Navigation in the Presence of Obstacles for an Agile Autonomous Underwater Vehicle
This addresses the challenge of agile AUV navigation in obstacle-rich environments, such as underwater exploration, but it is incremental as it builds on existing path optimization methods.
The paper tackles the problem of enabling an autonomous underwater vehicle (AUV) to navigate through cluttered spaces like shipwrecks, which was previously unaddressed, by proposing a novel navigation framework that uses an enhanced Trajopt for fast 3D path planning and a sampling-based correction to avoid local minima, resulting in safe real-time navigation proven in simulations and pool experiments.
Navigation underwater traditionally is done by keeping a safe distance from obstacles, resulting in "fly-overs" of the area of interest. Movement of an autonomous underwater vehicle (AUV) through a cluttered space, such as a shipwreck or a decorated cave, is an extremely challenging problem that has not been addressed in the past. This paper proposes a novel navigation framework utilizing an enhanced version of Trajopt for fast 3D path-optimization planning for AUVs. A sampling-based correction procedure ensures that the planning is not constrained by local minima, enabling navigation through narrow spaces. Two different modalities are proposed: planning with a known map results in efficient trajectories through cluttered spaces; operating in an unknown environment utilizes the point cloud from the visual features detected to navigate efficiently while avoiding the detected obstacles. The proposed approach is rigorously tested, both on simulation and in-pool experiments, proven to be fast enough to enable safe real-time 3D autonomous navigation for an AUV.