A Hierarchal Planning Framework for AUV Mission Management in a Spatio-Temporal Varying Ocean

The purpose of this paper is to provide a hierarchical dynamic mission planning framework for a single autonomous underwater vehicle (AUV) to accomplish task-assign process in a limited time interval while operating in an uncertain undersea environment, where spatio-temporal variability of the operating field is taken into account. To this end, a high level reactive mission planner and a low level motion planning system are constructed. The high level system is responsible for task priority assignment and guiding the vehicle toward a target of interest considering on-time termination of the mission. The lower layer is in charge of generating optimal trajectories based on sequence of tasks and dynamicity of operating terrain. The mission planner is able to reactively re-arrange the tasks based on mission/terrain updates while the low level planner is capable of coping unexpected changes of the terrain by correcting the old path and re-generating a new trajectory. As a result, the vehicle is able to undertake the maximum number of tasks with certain degree of maneuverability having situational awareness of the operating field. The computational engine of the mentioned framework is based on the biogeography based optimization (BBO) algorithm that is capable of providing efficient solutions. To evaluate the performance of the proposed framework, firstly, a realistic model of undersea environment is provided based on realistic map data, and then several scenarios, treated as real experiments, are designed through the simulation study. Additionally, to show the robustness and reliability of the framework, Monte-Carlo simulation is carried out and statistical analysis is performed. The results of simulations indicate the significant potential of the two-level hierarchical mission planning system in mission success and its applicability for real-time implementation

Task Assignment and Path Planning for Multiple Autonomous Underwater Vehicles using 3D Dubins Curves

This paper investigates the task assignment and path planning problem for multiple AUVs in three dimensional (3D) underwater wireless sensor networks where nonholonomic motion constraints of underwater AUVs in 3D space are considered. The multi-target task assignment and path planning problem is modeled by the Multiple Traveling Sales Person (MTSP) problem and the Genetic Algorithm (GA) is used to solve the MTSP problem with Euclidean distance as the cost function and the Tour Hop Balance (THB) or Tour Length Balance (TLB) constraints as the stop criterion. The resulting tour sequences are mapped to 2D Dubins curves in the X − Y plane, and then interpolated linearly to obtain the Z coordinates. We demonstrate that the linear interpolation fails to achieve G1 continuity in the 3D Dubins path for multiple targets. Therefore, the interpolated 3D Dubins curves are checked against the AUV dynamics constraint and the ones satisfying the constraint are accepted to finalize the 3D Dubins curve selection. Simulation results demonstrate that the integration of the 3D Dubins curve with the MTSP model is successful and effective for solving the 3D target assignment and path planning problem

3D Dubins curves for multi-vehicle path planning

This thesis proposes a unified algorithm for target assignment and path planning in 3D space for multiple Autonomous Underwater Vehicles (AUVs) to visit multiple targets. The multi-target assignment and path planning problem is modeled as a multiple Traveling Salesmen Problem (mTSP) and is usually solved by two separate algorithms: the multiple task assignment problem is first solved by the Genetic Algorithm (GA) using Euclidean distances between the targets; then the 3D path planning problem is solved for each assignment by selecting Dubins curves or other continuity curves. In contrast, this paper embeds the 3D Dubins curve selection into the target assignment step and uses the true path lengths rather than Euclidean distances as the fitness value of the GA. The unified algorithm is implemented by three functions: Function 1 designs a 3D Dubins path for a given target assignment sequence and given incoming-outgoing angles by an innovative rotation method extended from the well-known 2D Dubins curves; Function 2 uses the back-propagation algorithm to choose the shortest path among all possible incoming-outgoing angle combinations for a given target assignment sequence; Function 3 uses the true lengths of the 3D Dubins curves in the Genetic Algorithm (GA) to assign target sequence to multiple AUVs. Computer simulations demonstrate that the proposed algorithm provides better G2 continuity in 3D space than the existing linear or spline interpolation methods. The unified algorithm solves the NP-hard integer programming problem with an affordable computational complexity --Abstract, page iii

Multiple Region Coverage Path Planning for Autonomous Underwater Vehicle.

Coverage path planning methodology for an autonomous underwater vehicle to search multiple non-overlapping regions has been proposed in the paper. The proposed methodology is based on the genetic algorithm (GA). The GA used in the proposed methodology has been tuned for the specific problem, using design of experiment on an equivalent travelling salesman problem benchmark instance. Optimality of the generated paths was analysed through simulation studies. Results indicated that the proposed methodology generated shorter paths in comparison to conventional methods

Haptic-GeoZui3D: Exploring the Use of Haptics in AUV Path Planning

We have developed a desktop virtual reality system that we call Haptic-GeoZui3D, which brings together 3D user interaction and visualization to provide a compelling environment for AUV path planning. A key component in our system is the PHANTOM haptic device (SensAble Technologies, Inc.), which affords a sense of touch and force feedback – haptics – to provide cues and constraints to guide the user’s interaction. This paper describes our system, and how we use haptics to significantly augment our ability to lay out a vehicle path. We show how our system works well for quickly defining simple waypoint-towaypoint (e.g. transit) path segments, and illustrate how it could be used in specifying more complex, highly segmented (e.g. lawnmower survey) paths