Cooperative Formation Control of Autonomous Underwater Vehicles:An Overview

Formation control is a cooperative control concept in which multiple autonomous underwater mobile robots are deployed for a group motion and/or control mission. This paper presents a brief review on various cooperative search and formation control strategies for multiple autonomous underwater vehicles （AUV） based on literature reported till date. Various cooperative and formation control schemes for collecting huge amount of data based on formation regulation control and formation tracking control are discussed. To address the challenge of detecting AUV failure in the fleet, communication issues, collision and obstacle avoidance are also taken into attention. Stability analysis of the feasible formation is also presented. This paper may be intended to serve as a convenient reference for the further research on formation control of multiple underwater mobile robots.

A Comprehensive Review of Path Planning Algorithms for Autonomous Underwater Vehicles

The underwater path planning problem deals with finding an optimal or sub-optimal route between an origin point and a termination point in marine environments.The underwater environment is still considered as a great challenge for the path planning of autonomous underwater vehicles(AUVs)because of its hostile and dynamic nature.The major constraints for path planning are limited data transmission capability,power and sensing technology available for underwater operations.The sea environment is subjected to a large set of challenging factors classified as atmospheric,coastal and gravitational.Based on whether the impact of these factors can be approximated or not,the underwater environment can be characterized as predictable and unpredictable respectively.The classical path planning algorithms based on artificial intelligence assume that environmental conditions are known apriori to the path planner.But the current path planning algorithms involve continual interaction with the environment considering the environment as dynamic and its effect cannot be predicted.Path planning is necessary for many applications involving AUVs.These are based upon planning safety routes with minimum energy cost and computation overheads.This review is intended to summarize various path planning strategies for AUVs on the basis of characterization of underwater environments as predictable and unpredictable.The algorithms employed in path planning of single AUV and multiple AUVs are reviewed in the light of predictable and unpredictable environments.

Multiuser Detection for MIMO-OFDM system in Underwater Communication Using a Hybrid Bionic Binary Spotted Hyena Optimizer

Multi Access Interference (MAI) is the main source limiting the capacity and quality of the Multiple-Input Multiple-Output Orthogonal Frequency Division Multiplexing (MIMO-OFDM) system which fulfills the demand of high-speed transmission rate and high quality of service for future underwater acoustic (UWA) communication. Multi User Detection (MUD) is needed to overcome the performance degradation caused by MAI. In this research, both local and global optimal solutions are obtained in Bionic Binary Spotted Hyena Optimizer (BBSHO) algorithm using the Position Coordinate Vectors (PCVs) of the social behavior of spotted hyenas to achieve MUD. Further, Extremal Optimization (EO) is introduced in BBSHO algorithm to improve the local search ability within the search space. Hence, a hybrid BBSHO algorithm is proposed for achieving MUD at the receiver of the MIMO-OFDM system whose transceiver model in underwater is implemented using BELLHOP simulation system. By MATLAB simulation, it is shown that the Bit Error Rate (BER) performance of the proposed hybrid algorithm outperforms with best optimal solution within the search space towards MUD for Interference to Noise Ratio (INR) at 10 dB, 20 dB, and 40 dB over conventional detectors and metaheuristic approaches such as Binary Spotted Hyena Optimizer (BSHO), Binary Particle Swarm Optimization (BPSO) in the UWA network.