Juan David Hernandez

Online path planning for autonomous underwater vehicles in unknown environments

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.

Autonomous Underwater Navigation and Optical Mapping in Unknown Natural Environments

We present an approach for navigating in unknown environments while, simultaneously, gathering information for inspecting underwater structures using an autonomous underwater vehicle (AUV). To accomplish this, we first use our pipeline for mapping and planning collision-free paths online, which endows an AUV with the capability to autonomously acquire optical data in close proximity. With that information, we then propose a reconstruction pipeline to create a photo-realistic textured 3D model of the inspected area. These 3D models are also of particular interest to other fields of study in marine sciences, since they can serve as base maps for environmental monitoring, thus allowing change detection of biological communities and their environment over time. Finally, we evaluate our approach using the Sparus II, a torpedo-shaped AUV, conducting inspection missions in a challenging, real-world and natural scenario.

Planning feasible and safe paths online for autonomous underwater vehicles in unknown environments

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 vehicles 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 frameworks new features by solving tasks in both simulation and real-world in-water trials and comparing results with our previous approach.

AUV online mission replanning for gap filling and target inspection

In most of the current operational autonomous underwater vehicles (AUVs), a survey mission is generally composed of two main stages. The first one conducts an exhaustive coverage over an area of interest, while gathering data of the sea bottom. Then, and after processing the collected data, a second mission is programmed to obtain more detailed information of potential targets, and to cover the gaps that resulted from the first exploration. However, this two-survey strategy can be inefficient, since it requires establishing a communication link between the AUV and its operator for retrieving the data and reprogramming the second mission. To cope with this situation, we present a mission planner that endows an AUV with the capability of extending its missions online. With our approach, the vehicle is also required to conduct an initial and predefined survey of an area of interest, but it processes the gathered data onboard to plan 3D feasible paths to complement the initial exploration. To validate our approach, we present real-world results with the AsterX AUV.

Online View Planning for Inspecting Unexplored Underwater Structures

In this letter, we propose a method to automate the exploration of unknown underwater structures for autonomous underwater vehicles (AUVs). The proposed algorithm iteratively incorporates exteroceptive sensor data and replans the next-best-view in order to fully map an underwater structure. This approach does not require prior environment information. However, a safe exploration depth and the exploration area (defined by a bounding box, parameterized by its size, location, and resolution) must be provided by the user. The algorithm operates online by iteratively conducting the following three tasks: 1) Profiling sonar data are first incorporated into a 2-D grid map, where voxels are labeled according to their state (a voxel can be labeled as empty, unseen, occluded, occplane, occupied, or viewed). 2) Useful viewpoints to continue exploration are generated according to the map. 3) A safe path is generated to guide the robot toward the next viewpoint location. Two sensors are used in this approach: a scanning profiling sonar, which is used to build an occupancy map of the surroundings, and an optical camera, which acquires optical data of the scene. Finally, in order to demonstrate the feasibility of our approach, we provide real-world results using the Sparus II AUV.

Online motion planning for underwater inspection

This paper proposes the use of path planning algorithms for AUVs in applications where the robot needs to adapt online its trajectory for inspection or safety purposes. These algorithms generate trajectories under motion constraints, which can be followed without deviations, to ensure the safety even when passing close to obstacles. View planning algorithms are also combined to decide the movements to be executed to discover the unexplored seabed or target and to cover it with a camera or sonar. Online mapping with profiling sonars and online planning with fast sampling-based algorithms, allows the execution of missions without any previous knowledge of the 3D shape of the environment. Real 2D results with a torpedo-shaped AUV with hovering capabilities in an artificial harbour structure and natural rocky canyon demonstrate the feasibility of the approach for avoiding or inspecting the underwater environment. Simulated 3D results also show the capability of the approach to be extended in more real and challenging environments.

Autonomous Seabed Inspection for Environmental Monitoring

We present an approach for navigating in unknown environments, while gathering information for inspecting underwater structures using an autonomous underwater vehicle (AUV). To accomplish this, we first use our framework for mapping and planning collision-free paths online, which endows an AUV with the capability to autonomously acquire optical data in close proximity. With that information, we then propose a reconstruction framework to create a 3-dimensional (3D) geo-referenced photo-mosaic of the inspected area. These 3D mosaics are also of particular interest to other fields of study in marine sciences, since they can serve as base maps for environmental monitoring, thus allowing change detection of biological communities and their environment in the temporal scale. Finally, we evaluate our frameworks, independently, using the SPARUS-II, a torpedo-shaped AUV, conducting missions in real-world scenarios. We also assess our approach in a virtual environment that emulates a natural underwater milieu that requires the aforementioned capabilities.