Task Allocation and Path Planning for Collaborative Autonomous Underwater Vehicles Operating through an Underwater Acoustic Network

Abstract

Dynamic and unstructured multiple cooperative autonomous underwater vehicle (AUV) missions are highly complex operations, and task allocation and path planning are made significantly more challenging under realistic underwater acoustic communication constraints. This paper presents a solution for the task allocation and path planning for multiple AUVs under marginal acoustic communication conditions: a location-aided task allocation framework (LAAF) algorithm for multitarget task assignment and the grid-based multiobjective optimal programming (GMOOP) mathematical model for finding an optimal vehicle command decision given a set of objectives and constraints. Both the LAAF and GMOOP algorithms are well suited in poor acoustic network condition and dynamic environment. Our research is based on an existing mobile ad hoc network underwater acoustic simulator and blind flooding routing protocol. Simulation results demonstrate that the location-aided auction strategy performs significantly better than the well-accepted auction algorithm developed by Bertsekas in terms of task-allocation time and network bandwidth consumption. We also demonstrate that the GMOOP path-planning technique provides an efficient method for executing multiobjective tasks by cooperative agents with limited communication capabilities. This is in contrast to existing multiobjective action selection methods that are limited to networks where constant, reliable communication is assumed to be available.

A Comprehensive Review of Path Planning Algorithms for Autonomous Underwater Vehicles

“…Recently, a collaborative path planning method has been presented by Liu et al [199] for multiple-AUV in the face of dynamic oceanic currents based on the DP. In the proposed methodology, each AUV coordinated with the AUV with the longest estimated time of travel.…”

“…Algorithm 6. DE pseudocode [199] for each i do for each j do for each k do The application of deterministic algorithms over a stochastic algorithm for the PP and task assignment for a multiple AUV system has been advocated in the literature due to its algorithmic completeness and predictability. The algorithm outputs will remain similar, if the traffic scenario is evaluated on a different AUV, as the overall traffic configuration remains unchanged.…”

“…•Can solve task allocation problem of multiple AUVs avail with minimal acoustic communications •Deterministic in nature •Requires more computation time and cost Unpredictable RRT [198,199] Time B. Garau, M. Bonet, A. Alvarez, S. Ruiz, A. Pascual. Path planning for autonomous underwater vehicles in realistic oceanic current fields: Application to gliders in the western mediterranean sea.…”

Meta-Heuristic Ant Colony Algorithm for Multi-Tasking Assignment on Collaborative AUVs

“…Collaborative planning task allocation algorithm can undertake the task to complete decomposition and allocation for tasks require collaborative execution, the task can be broken down into several sub-tasks, and according to the agreement AUV developed jointly with other implementation plans [7][8][9].…”

AUV Search Target Research Based meta-heuristic algorithm

“…In multiple AUVs in many complex tasks require multiple AUV synergy to complete. Collaborative planning task allocation algorithm can undertake the task to complete decomposition and allocation for tasks require collaborative execution, the task can be broken down into several sub-tasks, and according to the agreement AUV developed jointly with other implementation plans [7][8][9]. Comparison of experimental results prove that the use of meta-heuristic ant colony optimization algorithm for solving multi-tasking aspect AUVs , solving performance than the basic ant colony algorithm has higher stability.…”