An Improved SOM-Based Method for Multi-Robot Task Assignment and Cooperative Search in Unknown Dynamic Environments

Tang, Hongwei; Lin, Anping; Sun, Wei; Shi, Shuqi

doi:10.3390/en13123296

Cited by 9 publications

(4 citation statements)

References 25 publications

(46 reference statements)

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“…Step 4: Calculate the convergence factor a using Equations ( 16) and ( 17), and determine the values of A and C. Update the positions of the individual gray wolves in the population using Equation (22), and compute the updated population's objective function value.…”

Section: Steps Of De-gwomentioning

confidence: 99%

“…Dong and colleagues utilized a velocity synthesis algorithm and the SOM to address task assignment for a Multi-AUV system in a 3D time-varying current environment. The target point functioned as the input layer, while the output layer consisted of the position of each AUV [22]. Then, Zhu et al proposed an improved self-organizing algorithm for grid confidence to solve the task-allocation problem for multiple AUV in an obstacle environment [23].…”

Section: Introductionmentioning

confidence: 99%

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Hybrid Form of Differential Evolutionary and Gray Wolf Algorithm for Multi-AUV Task Allocation in Target Search

Chen,

Zhang,

Wang

et al. 2023

Electronics

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For underwater target exploration, multiple Autonomous Underwater Vehicles (AUVs) have shown significant advantages over single AUVs. Aiming at Multi-AUV task allocation, which is an important issue for collaborative work in underwater environments, this paper proposes a Multi-AUV task allocation method based on the Differential Evolutionary Gray Wolf Optimization (DE-GWO) algorithm. Firstly, the working process of the Multi-AUV system was analyzed, and the allocation model and objective function were established. Then, we combined the advantages of the strong global search capability of the Differential Evolutionary (DE) algorithm and the excellent convergence performance of Gray Wolf Optimization (GWO) to solve the task assignment of the Multi-AUV system. Finally, a reassignment mechanism was used to solve the problem of AUV failures during the task’s execution. In the simulation comparison experiments, the DE-GWO, GWO, DE, and Particle Swarm Optimization (PSO) algorithms were carried out for different AUV execution capabilities, respectively.

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Section: Steps Of De-gwomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hybrid Form of Differential Evolutionary and Gray Wolf Algorithm for Multi-AUV Task Allocation in Target Search

Chen,

Zhang,

Wang

et al. 2023

Electronics

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show abstract

“…In addition, due to the structural problems of the robot itself, the planning path may also lead to contact between the robot and the obstacles during the moving process. To address these issues and improve the standardization level of non-standardized map grid processing, we conducted more in-depth research [41], [42].…”

Section: Description Of the Problemmentioning

confidence: 99%

Research on Path Planning for Robot Based on Improved Design of Non-Standard Environment Map With Ant Colony Algorithm

Li,

Kim,

Lyu

et al. 2023

IEEE Access

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The maps used for the path planning of mobile robots are mostly grid maps, which are generated through independent design or sensor measurement to obtain relevant information and then modeling. To obtain the robot motion planning path more quickly, this paper proposes a method of robot motion path planning through the ant colony algorithm under a non-standard environment map. The non-standard environment map is used for standard grid design, and the grid map is optimized by the method of no safety distance added obstacle box selected and safety distance added obstacle box selected, then the path planning is carried out through the ant colony algorithm. In addition, the mutual correspondence between grid maps and real environment maps was solved by adding calibration objects. The experimental results show that this method can not only effectively solve the problem of ant colony algorithms under a non-standard real environment map, show the planning path and pose on the non-standard real environment map, moreover, the safety degree of the planning path in the real environment is also increased by 29.51%, ensuring the safety degree of the whole planning path, which improves the intelligent degree of robot motion path planning.

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“…In dynamic environments such as IoT, adaptive TA is accomplished thanks to reinforcement learning [80][81][82] [83]. Neural networks, and in particular Self-Organizing Maps (SOM) are mostly used in MRS [67] [84].…”

Section: B Approaches Typically Included In Task Allocation Algorithmsmentioning

confidence: 99%

Task Allocation Among Connected Devices: Requirements, Approaches, and Challenges

Pilloni

Ning

Atzori

2022

IEEE Internet Things J.

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Task allocation (TA) is essential when deploying application tasks to systems of connected devices with dissimilar and time-varying characteristics. The challenge of an efficient TA is to assign the tasks to the best devices, according to the context and task requirements. The main purpose of this paper is to study the different connotations of the concept of TA efficiency, and the key factors that most impact on it, so that relevant design guidelines can be defined. The paper first analyzes the domains of connected devices where TA has an important role, which brings to this classification: Internet of Things (IoT), Sensor and Actuator Networks (SAN), Multi-Robot Systems (MRS), Mobile Crowdsensing (MCS), and Unmanned Aerial Vehicles (UAV). The paper then demonstrates that the impact of the key factors on the domains actually affects the design choices of the state-ofthe-art TA solutions. It results that resource management has most significantly driven the design of TA algorithms in all domains, especially IoT and SAN. The fulfillment of coverage requirements is important for the definition of TA solutions in MCS and UAV. Quality of Information requirements are mostly included in MCS TA strategies, similar to the design of appropriate incentives. The paper also discusses the issues that need to be addressed by future research activities, i.e.: allowing interoperability of platforms in the implementation of TA functionalities; introducing appropriate trust evaluation algorithms; extending the list of tasks performed by objects; designing TA strategies where network service providers have a role in TA functionalities' provisioning.

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An Improved SOM-Based Method for Multi-Robot Task Assignment and Cooperative Search in Unknown Dynamic Environments

Cited by 9 publications

References 25 publications

Hybrid Form of Differential Evolutionary and Gray Wolf Algorithm for Multi-AUV Task Allocation in Target Search

Hybrid Form of Differential Evolutionary and Gray Wolf Algorithm for Multi-AUV Task Allocation in Target Search

Research on Path Planning for Robot Based on Improved Design of Non-Standard Environment Map With Ant Colony Algorithm

Task Allocation Among Connected Devices: Requirements, Approaches, and Challenges

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