Multi-AUV cooperative target search and tracking in unknown underwater environment

Cao, Xiang; Sun, Hong; Jan, Gene Eu

doi:10.1016/j.oceaneng.2017.12.037

Cited by 101 publications

(31 citation statements)

References 26 publications

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“…The single-AUV is limited by the energy consumption, the work efficiency is not high enough, and the multi-AUV [14][15] cooperative work will greatly improve the efficiency. This section will introduce the specific implementation of collision avoidance of the algorithm in the multi-AUV path planning.…”

Section: Path Planning For Multi-auvmentioning

confidence: 99%

Based on Improved Bio-Inspired Model for Path Planning by Multi-AUV

Lian

Liu

2018

Proceedings of the 2018 International Conference on Electronics and Electrical Engineering Technology

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Aiming at path planning and collision avoidance of multiple autonomous underwater vehicle (AUV) system under complex environment, an improved neural network algorithm based on biological inspired model is proposed. Firstly, establishing an improved bio-inspired neural network model, the two-dimensional working area is rasterized, and each grid and neuron are one-toone correspondence, stipulating that the interest area and the obstacle area of the grid correspond to the excitatory and inhibition of neurons respectively, affecting the neurons activity in the whole working area by the transversal function of the adjacent neurons each other. Secondly, AUV plans a safe and collision-free path by comparing the size of the activity of neighbor neurons. Then, Aiming at the problem that AUV moves clinging to the edge of obstacles, adding lateral inhibitory effects of the obstacles on the neural network and greatly improving the safety and rationality of the path planning. Finally, changing the property of grid positions of each AUV in real time to realize collision avoidance between multi-AUV. Simulation experiments prove that the improved algorithm is valid about the path planning in this thesis and the large allowance collision avoidance problem in a complex environment with single-AUV and multi-AUV.

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Section: Path Planning For Multi-auvmentioning

confidence: 99%

Based on Improved Bio-Inspired Model for Path Planning by Multi-AUV

Lian

Liu

2018

Proceedings of the 2018 International Conference on Electronics and Electrical Engineering Technology

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“…The goal is to maximize environmental coverage while minimizing overlaps. Search pattern, random walk, search map, digital pheromone [4], Glasius bio-inspired neural network (GBNN) [5,6], and optimization algorithms are the typical algorithms. The search pattern, such as zigzag and spiral [7], can effectively cover a given domain with fewer time t. r i d (t) is the decision radius of glowworm i at time t. The update equation of position is shown in (7).…”

Section: Introductionmentioning

confidence: 99%

“…In this paper, a random component was introduced to the GSO to help robots to explore the area when there is no neighbor. l i (t + 1) = (1 − ρ)l i (t) + γJ(x i (t)) (5) p ij (t) = l j (t) − l i (t) k∈ U i (t) l k (t) − l i (t) (6) x i (t + 1) = x i (t) + s · ( x j (t) − x i (t)…”

Section: Introductionmentioning

confidence: 99%

A Distributed Strategy for Cooperative Autonomous Robots Using Pedestrian Behavior for Multi-Target Search in the Unknown Environment

Shi

2020

Sensors

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Searching multiple targets with swarm robots is a realistic and significant problem. The goal is to search the targets in the minimum time while avoiding collisions with other robots. In this paper, inspired by pedestrian behavior, swarm robotic pedestrian behavior (SRPB) was proposed. It considered many realistic constraints in the multi-target search problem, including limited communication range, limited working time, unknown sources, unknown extrema, the arbitrary initial location of robots, non-oriented search, and no central coordination. The performance of different cooperative strategies was evaluated in terms of average time to find the first, the half, and the last source, the number of located sources and the collision rate. Several experiments with different target signals, fixed initial location, arbitrary initial location, different population sizes, and the different number of targets were implemented. It was demonstrated by numerous experiments that SRPB had excellent stability, quick source seeking, a high number of located sources, and a low collision rate in various search strategies.

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“…e UUV and AUV are autonomous, which means they perform the assigned mission without any human intercession [1]. Recently, the cooperative control of the multi-UUVs has been becoming a hot topic in various applications, including ocean exploration, submarine rescue, minesweeping, and other fields [2][3][4]. Compared to the individual UUV which has the limited processing power and operational capability [5] to complete a single task, the multi-UUVs as a whole can perform the complex task, thus having more superiority in the harsh ocean environment, such as enhancing reliability and reducing cost.…”

Section: Introductionmentioning

confidence: 99%

Leader-Following Multiple Unmanned Underwater Vehicles Consensus Control under the Fixed and Switching Topologies with Unmeasurable Disturbances

Yan

Liu³

et al. 2020

Complexity

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We propose a consensus control strategy for multiple unmanned underwater vehicles (multi-UUVs) with unmeasurable disturbances under the fixed and switching topologies. The current methods presented in the literature mainly solve tracking consensus problems with the disturbances under the time-invariant and time-varying topologies, respectively. In the paper, considering the complex nonlinear and couple model of the UUV, the technique of the feedback linearization is employed to transform the nonlinear UUV model into a second-order integral UUV model. For unmeasurable disturbances consisting of unknown model uncertainties and external disturbance for each UUV, we design the distributed extended state observer (DESO) to estimate the disturbances using the UUV position information relative to its neighbours. Moreover, leader-following multi-UUVs consensus control algorithm that enables all following UUVs to track the leader UUV state information based on the estimation state information from the DESO is proposed for two types of topologies, the fixed and switching topologies. Finally, simulation results are shown to demonstrate the effectiveness of the algorithm proposed in the paper.

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Multi-AUV cooperative target search and tracking in unknown underwater environment

Cited by 101 publications

References 26 publications

Based on Improved Bio-Inspired Model for Path Planning by Multi-AUV

Based on Improved Bio-Inspired Model for Path Planning by Multi-AUV

A Distributed Strategy for Cooperative Autonomous Robots Using Pedestrian Behavior for Multi-Target Search in the Unknown Environment

Leader-Following Multiple Unmanned Underwater Vehicles Consensus Control under the Fixed and Switching Topologies with Unmeasurable Disturbances

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