Surface Optimal Path Planning Using an Extended Dijkstra Algorithm

Luo, Min; Hou, Xiaorong; Yang, Jing

doi:10.1109/access.2020.3015976

Cited by 145 publications

(52 citation statements)

References 30 publications

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“…Step 1: Set initial parameters, including maximum iteration times, population size, crossover probability, mutation probability, initial temperature, termination temperature, and cooling rate; System parameters were obtained, including order arrival rate, AGVs number, and the number and location of picking stations. Dijkstra algorithm [32] was used to calculate the shortest running path and distance between target shelves of each task and the location of each picking stations. Randomly initialize the population according to the coding scheme;…”

Section: ) Steps Of Ga-sa Algorithmmentioning

confidence: 99%

Research on Equipment Configuration Optimization of AGV Unmanned Warehouse

et al. 2021

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To improve the throughput performance of the autonomous guide vehicle (AGV) unmanned storage system, a two-stage mathematical model was established. The model also considers the equipment configuration of the AGV unmanned storage system and the AGV-picking stations dual resource coordination scheduling problem. In the equipment-task scheduling phase, the model aims at the shortest order completion time, while the equipment configuration and layout model aims at the minimum equipment configuration and operating cost. To solve the two-stage model, a two-layer genetic algorithm was designed. The inner layer algorithm was used to optimize the task scheduling order of AGVs and picking stations. The results of the inner layer algorithm are fed back to the outer model to optimize configuration of the equipment and the picking station's layout. The inner and outer loops are combined to obtain the optimal equipment configuration scheme. Through the simulation study of an enterprise AGV unmanned storage case, the optimal equipment configuration combination and picking stations layout scheme are obtained. Compared with the equipment configuration scheme based on the principle the task scheduling in operation is another key link that affects the picking efficiency of an unmanned warehouse of random task scheduling and the principle of shortest job time first; The model can improve the efficiency of warehouse retrieval and minimize the number of equipment configurations. Finally, the improved genetic algorithm is used to solve the model, and the performance is compared with that of LINGO to verify the effectiveness of the improved algorithm.

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Section: ) Steps Of Ga-sa Algorithmmentioning

confidence: 99%

Research on Equipment Configuration Optimization of AGV Unmanned Warehouse

et al. 2021

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“…In [21], the authors aimed at the rapid planning of the optimal trajectory of the intelligent aircraft, considering the error constraints and the correction probability constraints, constructing the trajectory planning model of the intelligent aircraft under multiple constraints, and proposed a global search algorithm based on Dijkstra to solve the model. The algorithm proposed by the author improves the basic Dijkstra algorithm by calculating the residual error and constrained flight distance, so that it has better adaptability when solving the trajectory planning problem under multiple constraints.…”

Section: Related Workmentioning

confidence: 99%

An Adaptive Immune Ant Colony Optimization for Reducing Energy Consumption of Automatic Inspection Path Planning in Industrial Wireless Sensor Networks

Xiao

Liu

et al. 2021

Journal of Sensors

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Industrial wireless sensor networks (IWSNs) are usually fixedly deployed in industrial environments, and various sensor nodes cooperate with each other to complete industrial production tasks. The efficient work of each sensor node of IWSNs will improve the efficiency of the entire network. Automated robots need to perform timely inspection and maintenance of IWSNs in an industrial environment. Excessive inspection distance will increase inspection costs and increase energy consumption. Therefore, shortening the inspection distance can reduce production energy consumption, which is very important for the efficient operation of the entire system. However, the optimal detection path planning of IWSNs is an N-P problem, which can usually only be solved by heuristic mathematical methods. This paper proposes a new adaptive immune ant colony optimization (AIACO) for optimizing automated inspection path planning. Moreover, novel adaptive operator and immune operator are designed to prevent the algorithm from falling into the local optimum and increase the optimization ability. In order to verify the performance of the algorithm, the algorithm is compared with genetic algorithm (GA) and immune clone algorithm (ICA). The simulation results show that the inspection distance of IWSNs using AIACO is lower than that of GA and ICA. In addition, the convergence speed of AIACO is faster than that of GA and ICA. Therefore, the AIACO proposed in this paper can effectively reduce the inspection energy consumption of the entire IWSN system.

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“…Geometric search algorithms are commonly used path search algorithm, mainly including the Dijkstra algorithm [24] and the A-star algorithm [25]. The Dijkstra algorithm obtains the shortest path by traversing all nodes.…”

Section: Introductionmentioning

confidence: 99%

Geometric A-Star Algorithm: An Improved A-Star Algorithm for AGV Path Planning in a Port Environment

et al. 2021

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This research introduces a path planning method based on the geometric A-star algorithm.The whole approach is applied to an Automated Guided Vehicle (AGV) in order to avoid the problems of many nodes, long-distance and large turning angle, and these problems usually exist in the sawtooth and cross paths produced by the traditional A-star algorithm. First, a grid method models a port environment. Second, the nodes in the close-list are filtered by the functions ( ) , P x y and ( ) , W x y and the nodes that do not meet the requirements are removed to avoid the generation of irregular paths. Simultaneously, to enhance the stability of the AGV regarding turning paths, the polyline at the turning path is replaced by a cubic B-spline curve. The path planning experimental results applied to different scenarios and different specifications showed that compared with other seven different algorithms, the geometric A-star algorithm reduces the number of nodes by 10% ~ 40%, while the number of turns is reduced by 25%, the turning angle is reduced by 33.3%, and the total distance is reduced by 25.5%. Overall, the simulation results of the path planning confirmed the effectiveness of the geometric A-star algorithm.

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Surface Optimal Path Planning Using an Extended Dijkstra Algorithm

Cited by 145 publications

References 30 publications

Research on Equipment Configuration Optimization of AGV Unmanned Warehouse

Research on Equipment Configuration Optimization of AGV Unmanned Warehouse

An Adaptive Immune Ant Colony Optimization for Reducing Energy Consumption of Automatic Inspection Path Planning in Industrial Wireless Sensor Networks

Geometric A-Star Algorithm: An Improved A-Star Algorithm for AGV Path Planning in a Port Environment

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