Somaiyeh MahmoudZadeh

A novel efficient task-assign route planning method for AUV guidance in a dynamic cluttered environment

Increasing the level of autonomy facilitates a vehicle in performing long-range operations with minimum supervision. This paper shows that the ability of Autonomous Underwater Vehicles (AUVs) to fulfill mission objectives is directly influenced by route planning and task assignment system performance. This paper proposes an efficient task-assign route-planning model in a semi-dynamic network, where the location of some waypoints can change over time within a target area. Two popular meta-heuristic algorithms, biogeography-based optimization (BBO) and particle swarm optimization (PSO), are adapted to provide real-time optimal solutions for task sequence selection and mission time management. To examine the performance of the method in a context of mission productivity, mission time management and vehicle safety, a series of Monte Carlo simulation trials are undertaken. The results of simulations demonstrate that the proposed methods are reliable and robust, particularly in dealing with uncertainties and changes in the operations network topology. As a result, they can significantly enhance the level of vehicles autonomy, enhancing its reactive nature through its capacity to provide fast feasible solutions.

Optimal route planning with prioritized task scheduling for AUV missions

This paper presents a solution to Autonomous Underwater Vehicles (AUVs) large scale route planning and task assignment joint problem. Given a set of constraints (e.g., time) and a set of task priority values, the goal is to find the optimal route for underwater mission that maximizes the sum of the priorities and minimizes the total risk percentage while meeting the given constraints. Making use of the heuristic nature of genetic and swarm intelligence algorithms in solving NP-hard graph problems, Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) are employed to find the optimum solution, where each individual in the population is a candidate solution (route). To evaluate the robustness of the proposed methods, the performance of the all PS and GA algorithms are examined and compared for a number of Monte Carlo runs. Simulation results suggest that the routes generated by both algorithms are feasible and reliable enough, and applicable for underwater motion planning. However, the GA-based route planner produces superior results comparing to the results obtained from the PSO based route planner.

Toward efficient task assignment and motion planning for large-scale underwater missions

An autonomous underwater vehicle needs to possess a certain degree of autonomy for any particular underwater mission to fulfil the mission objectives successfully and ensure its safety in all stages of the mission in a large-scale operating field. In this article, a novel combinatorial conflict-free task assignment strategy, consisting of an interactive engagement of a local path planner and an adaptive global route planner, is introduced. The method takes advantage of the heuristic search potency of the particle swarm optimization algorithm to address the discrete nature of routing-task assignment approach and the complexity of nondeterministic polynomial-time-hard path planning problem. The proposed hybrid method is highly efficient as a consequence of its reactive guidance framework that guarantees successful completion of missions particularly in cluttered environments. To examine the performance of the method in a context of mission productivity, mission time management, and vehicle safety, a series of simulation studies are undertaken. The results of simulations declare that the proposed method is reliable and robust, particularly in dealing with uncertainties, and it can significantly enhance the level of a vehicle’s autonomy by relying on its reactive nature and capability of providing fast feasible solutions.

A hierarchal planning framework for AUV mission management in a spatiotemporal 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.

Online path planning for AUV rendezvous in dynamic cluttered undersea environment using evolutionary algorithms

Abstract In this study, a single autonomous underwater vehicle (AUV) aims to rendezvous with a submerged leader recovery vehicle through a cluttered and variable operating field. The rendezvous problem is transformed into a Nonlinear Optimal Control Problem (NOCP) and then numerical solutions are provided. A penalty function method is utilized to combine the boundary conditions, vehicular and environmental constraints with the performance index that is final rendezvous time. Four evolutionary based path planning methods namely Particle Swarm Optimization (PSO), Biogeography-Based Optimization (BBO), Differential Evolution (DE), and Firefly Algorithm (FA) are employed to establish a reactive planner module and provide a numerical solution for the proposed NOCP. The objective is to synthesize and analyze the performance and capability of the mentioned methods for guiding an AUV from an initial loitering point toward the rendezvous through a comprehensive simulation study. The proposed planner module entails a heuristic for refining the path considering situational awareness of environment, encompassing static and dynamic obstacles within a spatiotemporal current fields. The planner thus needs to accommodate the unforeseen changes in the operating field such as emergence of unpredicted obstacles or variability of current field and turbulent regions. The simulation results demonstrate the inherent robustness and efficiency of the proposed planner for enhancing a vehicle’s autonomy so as to enable it to reach the desired rendezvous. The advantages and shortcoming of all utilized methods are also presented based on the obtained results.

Hybrid Motion Planning Task Allocation Model for AUV’s Safe Maneuvering in a Realistic Ocean Environment

This paper presents a hybrid route-path planning model for an Autonomous Underwater Vehicle’s task assignment and management while the AUV is operating through the variable littoral waters. Several prioritized tasks distributed in a large scale terrain is defined first; then, considering the limitations over the mission time, vehicle’s battery, uncertainty and variability of the underlying operating field, appropriate mission timing and energy management is undertaken. The proposed objective is fulfilled by incorporating a route-planner that is in charge of prioritizing the list of available tasks according to available battery and a path-planer that acts in a smaller scale to provide vehicle’s safe deployment against environmental sudden changes. The synchronous process of the task assign-route and path planning is simulated using a specific composition of Differential Evolution and Firefly Optimization (DEFO) Algorithms. The simulation results indicate that the proposed hybrid model offers efficient performance in terms of completion of maximum number of assigned tasks while perfectly expending the minimum energy, provided by using the favorable current flow, and controlling the associated mission time. The Monte-Carlo test is also performed for further analysis. The corresponding results show the significant robustness of the model against uncertainties of the operating field and variations of mission conditions.

Time and energy efficient trajectory generator for autonomous underwater vehicle docking operations

This paper presents a novel real-time quasi-optimal trajectory generator, based on the inverse dynamics in the virtual domain (IDVD) method, to produce a reliable and efficient guidance system for autonomous underwater vehicle (AUV) docking operations. To this end, a challenging docking scenario is defined in a cluttered operating field, encompassing ocean currents and no-fly zones. Using the IDVD method, a trajectory that takes into consideration the hydrodynamic model of the vehicle is generated and the optimality of this trajectory, in regards to mission time and energy expenditure of the vehicle, is considered. Computer simulations demonstrate that the IDVD-based strategy enables the guiding of a vehicle into the dock by satisfying the final boundary conditions of the docks position and orientation. Generated trajectories are feasible, smooth and realizable using for the vehicles low-level auto-pilot module. In terms of computation, the solution is suitable for real-time implementation that incorporates uncertainty handling of the operating environment. For further analysis, the generated trajectory is evaluated on a high fidelity AUV simulator. The result of this latter test also demonstrates applicability of the utilized IDVD method for optimization of docking trajectories.