A Path planning control strategy for search-classify task using multiple cooperative underwater vehicles

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

“…The output action commands generated using the GMOOP model produces the local optimal value of . The corresponding messages are designed to use as few data bytes as possible [36]. The message size is labeled Bytes for Task Allocation (BTA).…”

“…An intuitive solution is to compute all the possible combinational bids and send the bids information back to the auctioneer to select the winner(s). The problem with this method is that for concurrent targets, each bid message contains ( , 1) + ( , 2) + ⋅ ⋅ ⋅ + ( , ) combinational bids, where ( , ) is the permutation function [36]. This approach requires a large amount of data to be passed between the bidder and the auctioneer in a slow underwater acoustic network.…”

Online Determination of the Potential Benefit of Path Adaptation in Undersea Search

“…As the domain boundary becomes more complex, decomposition methods [12] are utilized to subdivide the region into simple subdomains, and optimization tools (such as genetic algorithms) that can be applied to determine optimal decompositions in terms of the net effect on search performance [13]. For multi-AUV search planning, the assignment of vehicles to various subdomains in a coverage search strategy is the current standard of practice [14]. Alternative approaches to AUV search include coordinated group behaviors [15], which effectively treat the group of vehicles as an aggregate single searcher that still performs standard coverage tasks.…”

A Behavior-Based Mission Planner for Cooperative Autonomous Underwater Vehicles†

“…In literature some works focus on the detection and classification of fixed targets, as in [21], [12] in opposition to others focused on tracking of mobile objects, as in [25]. As long as fixed targets are concerned, the cooperation might lead the team to assume an opportunistic displacement around the target, augmenting the precision level in its detection and classification [9]. Besides, for mobile targets, the fleet of AUVs might follow the object in order to track its behavior (i.e.…”

“…Applied to the underwater world, BB techniques for target detection with a single AUV have been proposed in [5]; while, in [20], authors employed behavior rules to control a predefined formation of MAUVs. A path planning control strategy for searching and classifying task using multiple cooperative Underwater Vehicles has been investigated in [9], where the authors exploit the diversity offered by the fleet, assigning different roles to different robots.…”