A jump point search improved ant colony hybrid optimization algorithm for path planning of mobile robot

Chen, Tao; Chen, Suifan; Zhang, Kuoran; Qiu, Guoting; Li, Qipeng; Chen, Xinmin

doi:10.1177/17298806221127953

Cited by 13 publications

(3 citation statements)

References 19 publications

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“…Furthermore, Chaudhry et al designed a proprietary genetic algorithm for AGV assignment and production scheduling in flexible job shop environments [29]. Chen et al introduced an improved hybrid ant colony algorithm, enhancing the precision in pathfinding and reducing the frequency of path selection decisions [30]. Emde et al designed a taboo search algorithm, addressing the material supply issue of delivering parts efficiently and in a timely manner to automatic assembly lines using tow trains within the factory, achieving a zero-defect state in assembly lines while minimizing work-inprocess inventory to meet just-in-time targets [31].…”

Section: Materials Distribution Scheduling Problems and Intelligent A...mentioning

confidence: 99%

Optimization Model and Strategy for Dynamic Material Distribution Scheduling Based on Digital Twin

Zhang,

Qu,

Zhao

et al. 2023

Preprint

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. Manufacturers are increasingly adopting mixed-flow manufacturing models to meet the growing customized, diverse, and dynamic customer demand. In such an environment, material distribution scheduling optimization is vital for smooth operations and is integral to production management. However, manufacturers frequently encounter problems like disordered and delayed material distribution. Traditional scheduling methods suffer from problems like inadequate transparency, delayed decision directives, and suboptimal results, impacting performance. To this end, this study proposes a dynamic material distribution scheduling optimization model and strategy based on digital twin (DT) to address these problems. Firstly, we introduce workstation satisfaction and establish a material distribution path optimization model minimizing total distribution cost while maximizing workstation satisfaction. Subsequently, we present a cloud-edge computing-based decision framework and explain the DT-based material distribution system's components and operation. Furthermore, a dynamic material distribution scheduling optimization mechanism based on DT is designed. By incorporating a savings method and incentive, penalty strategies, improvements are made to the path node selection probabilities and the information pheromone update rules of the traditional ant colony optimization (ACO) algorithm. Finally, a numerical case study, using real data from collaborating enterprises, validates the proposed algorithm and strategy. This research offers valuable insights into logistics management and algorithm design in smart manufacturing environments.

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Section: Materials Distribution Scheduling Problems and Intelligent A...mentioning

confidence: 99%

Optimization Model and Strategy for Dynamic Material Distribution Scheduling Based on Digital Twin

Zhang,

Qu,

Zhao

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…The initial pheromone concentration distribution obtained by the jump point is introduced to guide the algorithm to find the path, which accelerates the early iteration speed of the algorithm. It shows good performance in path finding accuracy and reducing the number of turns [12]. Yang Dong, Wu Yaohua, and Huo Dengya [13] advocated for an effective method to evaluate the picking efficiency and time of the system under the single instruction operation cycle by analyzing the crosschannel shuttle access system.…”

Section: Introductionmentioning

confidence: 99%

Cognitive Enhancement of Robot Path Planning and Environmental Perception Based on Gmapping Algorithm Optimization

Liu,

Gong,

et al. 2024

Electronics

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In the logistics warehouse environment, the autonomous navigation and environment perception of the logistics sorting robot are two key challenges. To deal with the complex obstacles and cargo layout in a warehouse, this study focuses on improving the robot perception and navigation system to achieve efficient path planning and safe motion control. For this purpose, a scheme based on an improved Gmapping algorithm is proposed to construct a high-precision map inside a warehouse through the efficient scanning and processing of environmental data by robots. While the improved algorithm effectively integrates sensor data with robot position information to realize the real-time modeling and analysis of warehouse environments. Consequently, the precise mapping results provide a reliable navigation basis for the robot, enabling it to make intelligent path planning and obstacle avoidance decisions in unknown or dynamic environments. The experimental results show that the robot using the improved Gmapping algorithm has high accuracy and robustness in identifying obstacles and an effectively reduced navigation error, thus improving the intelligence level and efficiency of logistics operations. The improved algorithm significantly enhances obstacle detection rates, increasing them by 4.05%. Simultaneously, it successfully reduces map size accuracy errors by 1.4% and angle accuracy errors by 0.5%. Additionally, the accuracy of the robot’s travel distance improves by 2.4%, and the mapping time is reduced by nine seconds. Significant progress has been made in achieving high-precision environmental perception and intelligent navigation, providing reliable technical support and solutions for autonomous operations in logistics warehouses.

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“…Commonly used algorithms in map rasterization and robot path planning are the A* algorithm [ 2 ] and the Dijkstra algorithm [ 3 ]. Intelligent path planning algorithms encompass techniques such as the ant colony algorithm [ 4 ], genetic algorithm [ 5 ], neural network algorithm [ 6 ], and others. In the realm of intelligent planning algorithms, path planning can be further divided into global path planning and local path planning.…”

Section: Introductionmentioning

confidence: 99%

Application of the Improved Rapidly Exploring Random Tree Algorithm to an Insect-like Mobile Robot in a Narrow Environment

Wang,

Yang,

Chen

et al. 2023

Biomimetics

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When intelligent mobile robots perform global path planning in complex and narrow environments, several issues often arise, including low search efficiency, node redundancy, non-smooth paths, and high costs. This paper proposes an improved path planning algorithm based on the rapidly exploring random tree (RRT) approach. Firstly, the target bias sampling method is employed to screen and eliminate redundant sampling points. Secondly, the adaptive step size strategy is introduced to address the limitations of the traditional RRT algorithm. The mobile robot is then modeled and analyzed to ensure that the path adheres to angle and collision constraints during movement. Finally, the initial path is pruned, and the path is smoothed using a cubic B-spline curve, resulting in a smoother path with reduced costs. The evaluation metrics employed include search time, path length, and the number of sampling nodes. To evaluate the effectiveness of the proposed algorithm, simulations of the RRT algorithm, RRT-connect algorithm, RRT* algorithm, and the improved RRT algorithm are conducted in various environments. The results demonstrate that the improved RRT algorithm reduces the generated path length by 25.32% compared to the RRT algorithm, 26.42% compared to the RRT-connect algorithm, and 4.99% compared to the RRT* algorithm. Moreover, the improved RRT algorithm significantly improves the demand for reducing path costs. The planning time of the improved RRT algorithm is reduced by 64.96% compared to that of the RRT algorithm, 40.83% compared to that of the RRT-connect algorithm, and 27.34% compared to that of the RRT* algorithm, leading to improved speed. These findings indicate that the proposed method exhibits a notable improvement in the three crucial evaluation metrics: sampling time, number of nodes, and path length. Additionally, the algorithm performed well after undergoing physical verification with an insect-like mobile robot in a real environment featuring narrow elevator entrances.

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A jump point search improved ant colony hybrid optimization algorithm for path planning of mobile robot

Cited by 13 publications

References 19 publications

Optimization Model and Strategy for Dynamic Material Distribution Scheduling Based on Digital Twin

Optimization Model and Strategy for Dynamic Material Distribution Scheduling Based on Digital Twin

Cognitive Enhancement of Robot Path Planning and Environmental Perception Based on Gmapping Algorithm Optimization

Application of the Improved Rapidly Exploring Random Tree Algorithm to an Insect-like Mobile Robot in a Narrow Environment

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