2 research outputs found
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
Autonomous Reactive Mission Scheduling and Task-Path Planning Architecture for Autonomous Underwater Vehicle
An Autonomous Underwater Vehicle (AUV) should carry out complex tasks in a
limited time interval. Since existing AUVs have limited battery capacity and
restricted endurance, they should autonomously manage mission time and the
resources to perform effective persistent deployment in longer missions. Task
assignment requires making decisions subject to resource constraints, while
tasks are assigned with costs and/or values that are budgeted in advance. Tasks
are distributed in a particular operation zone and mapped by a waypoint covered
network. Thus, design an efficient routing-task priority assign framework
considering vehicle's availabilities and properties is essential for increasing
mission productivity and on-time mission completion. This depends strongly on
the order and priority of the tasks that are located between node-like
waypoints in an operation network. On the other hand, autonomous operation of
AUVs in an unfamiliar dynamic underwater and performing quick response to
sudden environmental changes is a complicated process. Water current
instabilities can deflect the vehicle to an undesired direction and perturb
AUVs safety. The vehicle's robustness to strong environmental variations is
extremely crucial for its safe and optimum operations in an uncertain and
dynamic environment. To this end, the AUV needs to have a general overview of
the environment in top level to perform an autonomous action selection (task
selection) and a lower level local motion planner to operate successfully in
dealing with continuously changing situations. This research deals with
developing a novel reactive control architecture to provide a higher level of
decision autonomy for the AUV operation that enables a single vehicle to
accomplish multiple tasks in a single mission in the face of periodic
disturbances in a turbulent and highly uncertain environment.Comment: Thesis of PhD completed at Flinders University of South Australia,
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