Path Planning of Mobile Robot Based on Adaptive Ant Colony Optimization

Yang, Xiuqing; Xiong, Ni; Xiang, Yong; Du, Mingqian; Zhou, Xinzhi; Liu, Yong

doi:10.1109/iecon48115.2021.9589808

Cited by 7 publications

(5 citation statements)

References 10 publications

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“…Assuming the algorithm has Z groups of product ant colonies, the global pheromone update rule of the PACO algorithm, shown in Equation (22), is derived according to Equation (18).…”

Section: Multi-pheromone Of Product Ant Colonymentioning

confidence: 99%

“…Zhang et al [20] studied the production scheduling problem in a flexible manufacturing system with two adjacent working areas. Miao et al [21] and Yang et al [22] solved the problems related to the path planning of mobile robots with an improved adaptive ACO algorithm. Jia et al [23] proposed a novel bilevel ACO algorithm to deal with the vehicle routing problem.…”

Section: Introductionmentioning

confidence: 99%

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Textile Flexible Job-Shop Scheduling Based on a Modified Ant Colony Optimization Algorithm

Chen,

Xie,

et al. 2024

Applied Sciences

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To improve the workshop production efficiency of textile enterprises and balance the total operating time of all machines in each operation, this paper proposes a modified algorithm based on the combination of the ant colony optimization (ACO) algorithm and production products, which we call the product ant colony optimization (PACO) algorithm. The local pheromone update rule in the ACO algorithm is modified through the close relationship between textile machinery and production products in the textile workshop; the pheromone is then introduced into production products based on the constraints of the textile machine. A heuristic function is designed to improve the utilization rate of textile machines to increase the heuristic value of machines that are less frequently used in the algorithm iteration process. In addition, this paper combines the convergence speed and the global search ability of the algorithm with a designed variable pheromone evaporate parameter. The comparison among the initially designed PACO algorithm, the separately modified PACO algorithm, and the integratively modified PACO algorithm demonstrates that the proposed enhancement effectively addresses scheduling issues in textile flexible workshops and various workshops with similar constraint conditions.

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“…Assuming the algorithm has Z groups of product ant colonies, the global pheromone update rule of the PACO algorithm, shown in Equation (22), is derived according to Equation (18).…”

Section: Multi-pheromone Of Product Ant Colonymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Textile Flexible Job-Shop Scheduling Based on a Modified Ant Colony Optimization Algorithm

Chen,

Xie,

et al. 2024

Applied Sciences

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“…The foundational exploration of wheeled mobile robots (WMRs) to assess the mechanical system's dynamics involves a kinematic inquiry, which is imperative for ensuring the seamless execution of predefined trajectories [12]. This analytical process yields insights into devising control software for the WMR hardware, as well as for the design of robots tailored to tasks demanding a comprehensive grasp of the mechanical conduct of the robot [13].…”

Section: Kinematic Analysis Of Wmrmentioning

confidence: 99%

Stability Analysis and Navigational Techniques of Wheeled Mobile Robot: A Review

Borkar,

Aljrees,

Pandey

et al. 2023

Processes

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Wheeled mobile robots (WMRs) have been a focus of research for several decades, particularly concerning navigation strategies in static and dynamic environments. This review article carefully examines the extensive academic efforts spanning several decades addressing navigational complexities in the context of WMR route analysis. Several approaches have been explored by various researchers, with a notable emphasis on the inclusion of stability and intelligent capabilities in WMR controllers attracting the attention of the academic community. This study traces historical and contemporary WMR research, including the establishment of kinetic stability and the construction of intelligent WMR controllers. WMRs have gained prominence in various applications, with precise navigation and efficient control forming the basic prerequisites for their effective performance. The review presents a comprehensive overview of stability analysis and navigation techniques tailored for WMRs. Initially, the exposition covers the basic principles of WMR dynamics and kinematics, explaining the different wheel types and their associated constraints. Subsequently, various stability analysis approaches, such as Lyapunov stability analysis and passivation-based control, are discussed in depth in the context of WMRs. Starting an exploration of navigation techniques, the review highlights important aspects including path planning and obstacle avoidance, localization and mapping, and trajectory tracking. These techniques are carefully examined in both indoor and outdoor settings, revealing their benefits and limitations. Finally, the review ends with a comprehensive discussion of the current challenges and possible routes in the field of WMR. The discourse includes the fusion of advanced sensors and state-of-the-art control algorithms, the cultivation of more robust and reliable navigation strategies, and the continued exploration of novel WMR applications. This article also looks at the progress of mobile robotics during the previous three decades. Motion planning and path analysis techniques that work with single and multiple mobile robots have been discussed extensively. One common theme in this research is the use of soft computing methods to give mobile robot controllers cognitive behaviors, such as artificial neural networks (ANNs), fuzzy logic control (FLC), and genetic algorithms (GAs). Nevertheless, there is still a dearth of applications for mobile robot navigation that leverage nature-inspired algorithms, such as firefly and ant colony algorithms. Remarkably, most studies have focused on kinematics analysis, with a small number also addressing dynamics analysis.

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“…ACO is a meta-heuristic probabilistic technique focusing on unbalancing load [28]. The ACO offers minimum completion time and optimality to a large number of jobs but have stagnation issue and suffer from low convergence speed [21]. The Crow Search Algorithm (CSO) imitates crows' comportment by storing extra food or resources and retrieving it when necessary.…”

Section: Max-minmentioning

confidence: 99%

GEMM, a Genetic Engineering-Based Mutual Model for Resource Allocation of Grid Computing

Sharma,

Chaurasia,

Sharma

et al. 2023

IEEE Access

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Resource sharing, selection, and aggregation are vital functions of grid computing. However, managing resources in a grid-based environment is a stimulating task. It is necessary to update the topographical dispersal of resources possessed by various organisations with various usage guidelines, financial frameworks, load, and availability patterns. Users and servers have different objectives, methods, and needs. This article suggests a cost-effective framework for resource management in grid computing to look at and address these resource management difficulties. The proposed framework has three main functions, which help in grid construction, load balancing, and resource allocation. A Genetic engineering approach has been implemented to establish a relationship between the resource pool and the jobs of the nodes that improve the resource utilization. The proposed methodology also optimizes the overall cost by minimizing turnaround time. The results of proposed research are compared with commonly used algorithms and claiming 1.5 to 10% better results.

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Path Planning of Mobile Robot Based on Adaptive Ant Colony Optimization

Cited by 7 publications

References 10 publications

Textile Flexible Job-Shop Scheduling Based on a Modified Ant Colony Optimization Algorithm

Textile Flexible Job-Shop Scheduling Based on a Modified Ant Colony Optimization Algorithm

Stability Analysis and Navigational Techniques of Wheeled Mobile Robot: A Review

GEMM, a Genetic Engineering-Based Mutual Model for Resource Allocation of Grid Computing

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