Underwater robots are subject to complex hydro-dynamic forces. These forces define how the vehicle moves, so it is important to consider them when planning trajectories. However, performing motion planning considering the dynamics on the robot's onboard computer is challenging due to the limited computational resources available. In this paper an efficient motion planning framework for autonomous underwater vehicles (AUVs) is presented. By introducing a loosely coupled multilayered planning design, our framework is able to generate dynamically feasible trajectories while keeping the planning time low enough for online planning. First, a fast path planner operating in a lower-dimensional projected space computes a lead path from the start to the goal configuration. Then, the lead path is used to bias the sampling of a second motion planner, which takes into account all the dynamic constraints. Furthermore, we propose a strategy for online planning that saves computational resources by generating the final trajectory only up to a finite horizon. By using the finite horizon strategy together with the multilayered approach, the sampling of the second planner focuses on regions where good quality solutions are more likely to be found, significantly reducing the planning time. To provide strong safety guarantees our framework also incorporates the conservative approximations of inevitable collision states (icss). finally, we present simulations and experiments using a real underwater robot to demonstrate the capabilities of our framework.

Achieving full autonomous robotic environment exploration in the underwater domain is very challenging, mainly due to noisy acoustic sensors, high localization error, control disturbances of the water and lack of accurate underwater maps. In this work we present a robotic exploration algorithm for underwater vehicles that does not rely on prior information about the environment. Our method has been greatly influenced by many robotic exploration, view planning and path planning algorithms. The proposed method constitutes a significant improvement over our previous work [1]: Firstly, we refine our exploration approach to improve robustness; Secondly, we propose an alternative map representation based on the quadtree data structure that allows different relevant queries to be performed efficiently, reducing the computational cost of the viewpoint generation process; Thirdly, we present an algorithm that is capable of generating consistent maps even when noisy sonar data is used. The aforementioned contributions have increased the reliability of the algorithm, allowing new real experiments performed in artificial structures but also in more challenging natural environments, from which we provide a 3D reconstruction to show that with this algorithm full optical coverage is obtained.

We present a framework for planning collision-free and safe paths online for autonomous underwater vehicles (AUVs) in unknown environments. We build up on our previous work and propose an improved approach. While preserving its main modules (mapping, planning and mission handler), the framework now considers motion constraints to plan feasible paths, i.e., those that meet vehicle's motion capabilities. The new framework also incorporates a risk function to avoid navigating close to nearby obstacles, and reuses the last best known solution to eliminate time-consuming pruning routines. To evaluate this approach, we use the Sparus II AUV, a torpedo-shaped vehicle performing autonomous missions in a 2-dimensional workspace. We validate the framework's new features by solving tasks in both simulation and real-world in-water trials and comparing results with our previous approach.

We present a framework for planning collision-free paths online for autonomous underwater vehicles (AUVs) in unknown environments. It is composed of three main modules (mapping, planning and mission handler) that incrementally explore the environment while solving start-to-goal queries. We use an octree-based representation of the environment and we extend the optimal rapidly-exploring random tree (RRT*) using concepts of anytime algorithms and lazy collision evaluation, thus including the capability to replan paths according to nearby obstacles perceived during the execution of the mission. To validate our approach, we plan paths for the SPARUS-II AUV, a torpedo-shaped vehicle performing autonomous missions in a 2-dimensional workspace. We demonstrate its feasibility with the SPARUS-II AUV in both simulation and real-world in-water trials.