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 Cooperative Dynamic Task Assignment Framework for COTSBot AUVs

This paper presents a cooperative dynamic task assignment framework for a certain class of Autonomous Underwater Vehicles (AUVs) employed to control outbreak of Crown-Of-Thorns Starfish (COTS) in Australia's Great Barrier Reef. The problem of monitoring and controlling the COTS is transcribed into a constrained task assignment problem in which eradicating clusters of COTS, by the injection system of COTSbot AUVs, is considered as a task. A probabilistic map of the operating environment including seabed terrain, clusters of COTS, and coastlines is constructed. Then, a novel heuristic algorithm called Heuristic Fleet Cooperation (HFC) is developed to provide a cooperative injection of the COTSbot AUVs to the maximum possible COTS in an assigned mission time. Extensive simulation studies together with quantitative performance analysis are conducted to demonstrate the effectiveness and robustness of the proposed cooperative task assignment algorithm in eradicating the COTS in the Great Barrier Reef

Uninterrupted path planning system for Multi-USV sampling mission in a cluttered ocean environment

This paper presents an uninterrupted collision-free path planning system that facilitates the operational performance of multiple unmanned surface vehicles (USVs) in an ocean sampling mission. The proposed uninterrupted path planning system is developed based on the integration of a novel B-Spline data frame and particle swarm optimization (PSO)-based solver engine. The new B-spline data framing structure provides smart sampling of the candidate spots without needing full stop for completing the sampling tasks. This enables the USVs to encircle the area smoothly while simultaneously correcting the heading angle toward the next spot and preventing sharp changes in the vehicle's heading. Then, the optimization engine generates optimal, smooth, and constraint-aware path curves for multiple USVs to conduct the sampling mission from start point to the rendezvous point. The path generated incorporates controllability over the vehicles' velocity profile to prevent experiencing zero velocity and frequent stop/start switching of the controller. To achieve faster convergence of the optimization routine, a suitable search space decomposition scheme is proposed. Extensive simulation studies emulating a realistic ocean sampling mission are conducted to examine the feasibility and effectiveness of the proposed path planning system. This encapsulates modelling a realistic maritime environment of Indonesian Archipelago in Banda Sea including ocean waves, obstacles, and no-fly zones and introducing several performance indices to benchmark the path planning system performance. This process is accompanied by a comparative study of the proposed path planning system with a well-known state-of-the art piecewise, rapidly exploring random tree (RRT), and differential evolution-based path planning algorithms. The results of the simulation confirm the suitability and robustness of the proposed path planning system for the uninterrupted ocean sampling missions

Exploiting a fleet of UAVs for monitoring and data acquisition of a distributed sensor network

This study proposes an efficient data collection strategy exploiting a team of unmanned aerial vehicles (UAVs) to monitor and collect the data of a large distributed sensor network usually used for environmental monitoring, meteorology, agriculture, and renewable energy applications. The study develops a collaborative mission planning system that enables a team of UAVs to conduct and complete the mission of sensors’ data collection collaboratively while considering existing constrains of the UAV payload and battery capacity. The proposed mission planner system employs the differential evolution optimization algorithm enabling UAVs to maximize the number of visited sensor nodes given the priority of the sensors and avoiding redundant collection of sensors’ data. The proposed mission planner is evaluated through extensive simulation and comparative analysis. The simulation results confirm the effectiveness and fidelity of the proposed mission planner to be used for the distributed sensor network monitoring and data collection