Combining a fuzzy inference system with an A* algorithm for the automated generation of roadmaps for Automated Guided Vehicles

Uttendorf, Sarah; Eilert, Björn; Overmeyer, Ludger

doi:10.1515/auto-2016-0081

Cited by 9 publications

(5 citation statements)

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“…Liu et al Proposed an improved A* algorithm to solve the combination of normal channel and berthing channel 16 . Uttendorf et al combined the fuzzy inference system with the A* algorithm to generate a path map for automatically guided vehicles 17 . Das et al proposed an online path planning method based on an improved real-time A* algorithm, which plans the optimal path by avoiding obstructions and minimizing time, energy, and distance as the cost 18 .…”

Section: Introductionmentioning

confidence: 99%

Path planning of scenic spots based on improved A* algorithm

Wang

Zhang

Liu

et al. 2022

Sci Rep

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Traditional scenic route planning only considers the shortest path, which ignores the information of scenic road conditions. As the most effective direct search method to solve the shortest path in static road network, A* algorithm can plan the optimal scenic route by comprehensively evaluating the weights of each expanded node in the gridded scenic area. However, A* algorithm has the problem of traversing more nodes and ignoring the cost of road in the route planning. In order to bring better travel experience to the travelers, the above factors are taken into account. This paper presents a path planning method based on the improved A* algorithm. Firstly, the heuristic function of the A* algorithm is weighted by exponential decay to improve the calculation efficiency of the algorithm. Secondly, in order to increase the practicality of the A* algorithm, the impact factors that road conditions is introduced to the evaluation function. Finally, the feasibility of the improved A* algorithm is verified through simulation experiments. Experimental results show that the improved A* algorithm can effectively reduce the calculation time and road cost.

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Section: Introductionmentioning

confidence: 99%

Path planning of scenic spots based on improved A* algorithm

Wang

Zhang

Liu

et al. 2022

Sci Rep

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“…The A* algorithm was first proposed in 1968 by Hart et al [14], it is a heuristic search algorithm proposed by combining the advantages of the bestfirst search algorithm and Dijkstra's algorithm [37], [42]. The A* algorithm uses heuristic information to guide the search direction, which has been widely used to find the optimal solution in a short time, and has strong expansibility and adaptability to different scenarios.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Center Constraint Weighted A* Algorithm for Path Planning of Petrochemical Inspection Robot

Lai

Chambers

2021

J Intell Robot Syst

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In many practical applications of robot path planning, finding the shortest path is critical, while the response time is often overlooked but important. To address the problems of search node divergence and long calculation time in the A* routing algorithm in the large scenario, this paper presents a novel center constraint weighted A* algorithm (CCWA*). The heuristic function is modified to give different dynamic weights to nodes in different positions, and the node weights are changed in the specified direction during the expansion process, thereby reducing the number of search nodes. An adaptive threshold is further added to the heuristic function to enhance the adaptiveness of the algorithm. To verify the effectiveness of the CCWA* algorithm, simulations are performed on 2-dimensional grid maps of different sizes. The results show that the proposed algorithm speeds up the search process and reduces the planning time in the process of path planning in a multi-obstacle environment compared with the conventional A* algorithm and weighted A* algorithm.

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“…However, existing approaches that prioritize maximizing redundancy can generate numerous unnecessary paths, resulting in low road utilization. The computation time, although not explicitly mentioned in any of the works, is also assumed to be relatively high [16]. Furthermore, introducing potential improvements by the experts can be difficult as the developed system is hard to understand.…”

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confidence: 99%

“…These drawbacks have been addressed to some extent by [16], who combined advantages of mathematical optimization with human expertise using fuzzy logic. The proposed expert system enables automated generation of roadmaps that are not only mathematically efficient, but also applicable to real production systems.…”

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confidence: 99%

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Simulation-Based Approach for Automatic Roadmap Design in Multi-AGV Systems

Žužek,

Vrabič,

Zdešar

et al. 2024

IEEE Trans. Automat. Sci. Eng.

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This paper addresses the problem of establishing efficient intralogistic systems, focusing on the generation of roadmaps on a given layout and the coordination of multiple Automated Guided Vehicles (AGVs). A simulation-based approach for automatic roadmap design is proposed. An eventbased simulator is developed that uses ant-colony inspired optimization to generate roadmaps tailored to the specific characteristics of a given intralogistic problem, i.e., the plant layout, fleet size, statistical description of tasks, dispatching algorithm, etc. The generated solutions are evaluated with a Multi-Agent Path Finding (MAPF) simulator that uses a Safe Interval Path Planning (SIPP) algorithm. By analysing the system throughput, the optimal fleet size for the system is proposed. The approach is validated through various examples and benchmarked against existing methods in the literature.Note to Practitioners-Creating efficient roadmaps plays a key role in improving the efficiency of multi-AGV systems. Usually, roadmaps are designed by experts, which is time consuming and may lead to suboptimal solutions due to the complexity of the problem. The main objective of this work is to develop a methodology that allows the automatic generation of efficient roadmaps taking into account the specific characteristics of an intralogistic problem. The main advantage is the fast generation of solutions and their validation in terms of throughput. At the same time, the presented methodology allows a quick correction of the solution if one of the problem characteristics changes, without the need to change the layout itself. This provides industry practitioners with an effective tool that facilitates their work in setting up multi-AGV systems, thus saving time and costs.

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Combining a fuzzy inference system with an A* algorithm for the automated generation of roadmaps for Automated Guided Vehicles

Cited by 9 publications

References 0 publications

Path planning of scenic spots based on improved A* algorithm

Path planning of scenic spots based on improved A* algorithm

Enhanced Center Constraint Weighted A* Algorithm for Path Planning of Petrochemical Inspection Robot

Simulation-Based Approach for Automatic Roadmap Design in Multi-AGV Systems

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