Teaching–Learning-Based Optimization Algorithm for Path Planning and Task Allocation in Multi-robot Plant Inspection System

Majumder, Arindam; Majumder, Abhishek; Bhaumik, Rahul

doi:10.1007/s13369-021-05710-8

Cited by 13 publications

(4 citation statements)

References 67 publications

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“…e inspection robot should also combine multisensor information fusion technology, wireless communication, and wireless transmission technology. Last but not least, the substation inspection robot has special requirements in the overall structural design and functional hardware selection [10].…”

Section: Comprehensive Design Concept Of Substation Inspectionmentioning

confidence: 99%

Application of PID Control and Improved Ant Colony Algorithm in Path Planning of Substation Inspection Robot

Kang

Wang

2022

Mathematical Problems in Engineering

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The purpose is to improve the effect of substation inspection and ensure the safety of power consumption in human society. First, this work discusses the current substation inspection-oriented robot path planning situation. Then, the proportional integration differentiation (PID) control algorithm is introduced and optimized. Ant colony algorithm (ACA) is improved. The substation inspection-oriented RPP model is designed based on the PID algorithm and optimized ACA (the proposed model is denoted as the Ant-PID algorithm). Afterward, the Ant-PID algorithm is compared with the PID control algorithm and ACA. The results show that the longest robot path of the proposed Ant-PID algorithm in different data sets is about 28 m. The shortest is about 26 m, and the number of optimal solutions is maintained at about 45–49. By comparison, the average response time of the PID algorithm is about 25 s to 28 s. The shortest response time of ACA is about 24 s, the shortest average response time is about 27 s, and the longest is about 30 s. The average response time of the proposed ant PID model is about 17 s to 20 s. Therefore, the Ant-PID algorithm can improve the substation inspection robots’ path planning effect. The research results provide technical support for improving the effect of substation inspection and contribute to social power transmission.

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Section: Comprehensive Design Concept Of Substation Inspectionmentioning

confidence: 99%

Application of PID Control and Improved Ant Colony Algorithm in Path Planning of Substation Inspection Robot

Kang

Wang

2022

Mathematical Problems in Engineering

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“…As the complexity of problems increases, traditional methods often cannot solve NP problems such as 3D path planning, so heuristic algorithms have gradually become the mainstream approach for solving such problems. Heuristic algorithms mainly include Differential Evolution (DE) [6,7], Ant Colony Optimization (ACO) [8,9], Particle Swarm Optimization (PSO) [10][11][12], the Genetic Algorithm (GA) [13,14], the Artificial Bee Colony (ABC) algorithm [15], the Firefly Algorithm (FA) [16], the Teaching-Learning-Based Optimization (TLBO) algorithm [17], and other heuristic algorithms that are currently widely used in multi-UAV path planning. Based on different principles and strategies, these algorithms can find effective flight paths in complex environments.…”

Section: Introductionmentioning

confidence: 99%

PPSwarm: Multi-UAV Path Planning Based on Hybrid PSO in Complex Scenarios

Meng,

Chen,

2024

Drones

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Evolutionary algorithms exhibit flexibility and global search advantages in multi-UAV path planning, effectively addressing complex constraints. However, when there are numerous obstacles in the environment, especially narrow passageways, the algorithm often struggles to quickly find a viable path. Additionally, collaborative constraints among multiple UAVs complicate the search space, making algorithm convergence challenging. To address these issues, we propose a novel hybrid particle swarm optimization algorithm called PPSwarm. This approach initially employs the RRT* algorithm to generate an initial path, rapidly identifying a feasible solution in complex environments. Subsequently, we adopt a priority planning method to assign priorities to UAVs, simplifying collaboration among them. Furthermore, by introducing a path randomization strategy, we enhance the diversity of the particle swarm, thereby avoiding local optimum solutions. The experimental results show that, in comparison to algorithms such as DE, PSO, ABC, GWO, and SPSO, the PPSwarm algorithm demonstrates significant advantages in terms of path quality, convergence speed, and runtime when addressing path planning issues for 40 UAVs across four different scenarios. In larger-scale experiments involving 500 UAVs, the proposed algorithm also exhibits excellent processing capability and scalability.

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“…In recent years, a large number of bioinspired algorithms have been proposed, such as the marine predator algorithm ( Faramarzi et al, 2020 ), chimp optimization algorithm ( Khishe and Mosavi, 2020 ), arithmetic optimization algorithm ( Abualigah et al, 2021 ), bald eagle search algorithm ( Alsattar et al, 2020 ), Harris hawks optimization algorithm ( Heidari et al, 2019 ), squirrel search algorithm ( Jain et al, 2018 ), pathfinder algorithm ( Yapici and Cetinkaya, 2019 ), equilibrium optimizer ( Faramarzi et al, 2019 ). The swarm intelligence algorithm has been widely used in various optimization problems and achieved good results, for example, path planning problems solved by the central force optimization algorithm ( Chen et al, 2016 ), teaching–learning-based optimization algorithm ( Majumder et al, 2021 ), water wave optimization algorithm ( Yan et al, 2021 ), chicken swarm optimization algorithm ( Liang et al, 2020 ), etc. Location problems are solved by the genetic algorithm ( Li et al, 2021 ), particle swarm optimization ( Yue et al, 2019 ), flower pollination algorithm ( Singh and Mittal, 2021 ), etc.…”

Section: Introductionmentioning

confidence: 99%

Bioinspired Bare Bones Mayfly Algorithm for Large-Scale Spherical Minimum Spanning Tree

Zhang

Zhou

et al. 2022

Front. Bioeng. Biotechnol.

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Mayfly algorithm (MA) is a bioinspired algorithm based on population proposed in recent years and has been applied to many engineering problems successfully. However, it has too many parameters, which makes it difficult to set and adjust a set of appropriate parameters for different problems. In order to avoid adjusting parameters, a bioinspired bare bones mayfly algorithm (BBMA) is proposed. The BBMA adopts Gaussian distribution and Lévy flight, which improves the convergence speed and accuracy of the algorithm and makes better exploration and exploitation of the search region. The minimum spanning tree (MST) problem is a classic combinatorial optimization problem. This study provides a mathematical model for solving a variant of the MST problem, in which all points and solutions are on a sphere. Finally, the BBMA is used to solve the large-scale spherical MST problems. By comparing and analyzing the results of BBMA and other swarm intelligence algorithms in sixteen scales, the experimental results illustrate that the proposed algorithm is superior to other algorithms for the MST problems on a sphere.

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Teaching–Learning-Based Optimization Algorithm for Path Planning and Task Allocation in Multi-robot Plant Inspection System

Cited by 13 publications

References 67 publications

Application of PID Control and Improved Ant Colony Algorithm in Path Planning of Substation Inspection Robot

Application of PID Control and Improved Ant Colony Algorithm in Path Planning of Substation Inspection Robot

PPSwarm: Multi-UAV Path Planning Based on Hybrid PSO in Complex Scenarios

Bioinspired Bare Bones Mayfly Algorithm for Large-Scale Spherical Minimum Spanning Tree

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