Complete Coverage Autonomous Underwater Vehicles Path Planning Based on Glasius Bio-Inspired Neural Network Algorithm for Discrete and Centralized Programming

Sun, Bing; Zhu, Daqi; Tian, Chen; Luo, Chaomin

doi:10.1109/tcds.2018.2810235

Cited by 105 publications

(50 citation statements)

References 22 publications

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“…By constructing hypothetical targets and introducing them into the NN as stimulating sources of excitation, the AUVs are guided to quickly search for areas where the target is likely to exist and they can efficiently complete the search task. Sun et al [138] designed a new strategy for collaborative search with a GBNN algorithm. In the algorithm, a grid map is set up to represent the working environment and NN are constructed where each AUV corresponds to a NN.…”

Section: Search Missionsmentioning

confidence: 99%

Autonomous Underwater Vehicles: Localization, Navigation, and Communication for Collaborative Missions

et al. 2020

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Development of Autonomous Underwater Vehicles (AUVs) has permitted the automatization of many tasks originally achieved with manned vehicles in underwater environments. Teams of AUVs designed to work within a common mission are opening the possibilities for new and more complex applications. In underwater environments, communication, localization, and navigation of AUVs are considered challenges due to the impossibility of relying on radio communications and global positioning systems. For a long time, acoustic systems have been the main approach for solving these challenges. However, they present their own shortcomings, which are more relevant for AUV teams. As a result, researchers have explored different alternatives. To summarize and analyze these alternatives, a review of the literature is presented in this paper. Finally, a summary of collaborative AUV teams and missions is also included, with the aim of analyzing their applicability, advantages, and limitations.

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Section: Search Missionsmentioning

confidence: 99%

Autonomous Underwater Vehicles: Localization, Navigation, and Communication for Collaborative Missions

et al. 2020

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“…With respect to the other methods using the grid map, Kapanoglu et al [43] developed the MRCPP method using the grid map combined with a genetic algorithm (GA). Sun et al [44] and Zhu et al [30] used a neural network based on a grid map in the MRCPP method. Gautam et al [45] proposed the MRCPP method using the grid map combined with the cluster.…”

Section: Related Workmentioning

confidence: 99%

Multi-Cleaning Robots Using Cleaning Distribution Method Based on Map Decomposition in Large Environments

Lee

Kang

2020

IEEE Access

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Most cleaning robots have a good cleaning performance for small environments such as houses but require a longer cleaning time due to problems such as slow cleaning progress and low battery capacity, making the robots unsuitable for large environments such as libraries and airports. Cleaning large environments with multiple robots is faster than cleaning them with a single robot. Multi-cleaning robots can utilize several robots to simultaneously clean and share the task of cleaning among the robots. However, as the number of robots increases and the effective distribution of cleaning is not efficient during the cleaning process, the cleaning time will consequently be longer due to the frequent collisions between the robots. Therefore, to shorten the cleaning time, multi-cleaning robots require a coverage path planning that uses an effective cleaning distribution method in the cleaning process. In this paper, a coverage path planning using a cleaning distribution method based on map decomposition is proposed to reduce the cleaning time of multi-cleaning robots. The experimental results demonstrated that the proposed multi-cleaning robots' coverage path planning could be used in large environments in the presence of several types of obstacles. Furthermore, the cleaning time was found to be shorter than that of the previous methods in the case of multi-cleaning robots. INDEX TERMS Coverage path planning, cleaning robot, computational efficiency, multi-robots system, robot path planning.

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“…However, considering that the choice of a fuzzy boundary is subjective, the generated path could not be guaranteed to be optimal. The following year, they [5] proposed a new discrete centralized planning strategy based on glasius bio-inspired neural network for AUVs full coverage motion planning. The algorithm had low computational cost and high efficiency.…”

Section: Introductionmentioning

confidence: 99%

Hierarchical Motion Planning of AUVs in Three Typical Marine Environments

et al. 2021

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Broad waters, harbor waters, and waterway waters make up more than 90% of autonomous underwater vehicles (AUV) navigation area, and each of them has its typical environmental characteristics. In this paper, a three-layer AUV motion planning architecture was designed to improve the planning logic of an AUV when completing complex underwater tasks. The AUV motion planning ability was trained by the improved deep deterministic policy gradient (DDPG) combined with the experience pool of classification. Compared with the traditional DDPG algorithm, the proposed algorithm is more efficient. Using the strategy obtained from the training and the motion planning architecture proposed in the paper, the tasks of AUVs searching in broad waters, crossing in waterway waters and patrolling in harbor waters were realized in the simulation experiment. The reliability of the planning system was verified in field tests.

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Complete Coverage Autonomous Underwater Vehicles Path Planning Based on Glasius Bio-Inspired Neural Network Algorithm for Discrete and Centralized Programming

Cited by 105 publications

References 22 publications

Autonomous Underwater Vehicles: Localization, Navigation, and Communication for Collaborative Missions

Autonomous Underwater Vehicles: Localization, Navigation, and Communication for Collaborative Missions

Multi-Cleaning Robots Using Cleaning Distribution Method Based on Map Decomposition in Large Environments

Hierarchical Motion Planning of AUVs in Three Typical Marine Environments

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