# Research on the A* Algorithm for Automatic Guided Vehicles in Large-Scale Maps
## 3 Improved A* Algorithm Based on Local Obstacle Rate and Dynamic Neighbourhood
### Modifications to the Evaluation Function
#### 3.4.3 Adding Direction Change Penalty
Specifically, by introducing the weighting consideration of local obstacle rate, optimising the definition of dynamic two-layer neighbourhood structure, and incorporating the idea of simulated annealing algorithm into the node selection strategy, the algorithm successfully achieved a significant improvement in its running efficiency, with the average running time reduced by 84%, while the number of expansion nodes was sharply reduced by 94%, and the length of paths was optimised by 2.3%. This improvement is especially significant when dealing with large-scale maps. In the future research outlook, the core principle of the A* algorithm and its realisation mechanism will remain a key direction for improvement. Researchers may aim to train the heuristic function by introducing neural network technology, with a view of enabling it to more accurately simulate and reflect the cost situation in the real environment, thus significantly improving the accuracy and efficiency of path planning. Meanwhile, more in-depth optimisation explorations will be carried out for the data structure of the algorithm, such as adopting efficient data organisation forms such as binary heap and ordered chain list, aiming to further accelerate the key operations of node insertion, deletion, and lookup, so as to comprehensively improve the performance of the algorithm. Or, depending on different graph environments, different scales of path searching are adopted to improve the search efficiency. In addition, applying A* algorithms to specific domains, such as intelligent transportation systems, will also become an important trend for future research. By combining the specific needs and constraints in the domain, the algorithm is customised and optimised, which is expected to provide more relevant, efficient, and reliable path planning solutions. It is worth noting that future research will also actively explore the path of combining the A* algorithm with local obstacle avoidance algorithms. For example, the improved A* algorithm is organically integrated with local obstacle avoidance algorithms such as the dynamic window approach (DWA) in order to construct a composite navigation system that possesses both global path planning capability and flexible response to local obstacles. This combination is expected to significantly improve the autonomous navigation ability of robots in complex environments. Meanwhile, the integration of multi-objective optimisation strategies in the A* algorithm framework will also become an important direction for future research. Through the introduction of multi-objective optimisation ideas, the path search process is more finely regulated and optimised to achieve the diversity and flexibility of path search. This will not only meet the needs of more diversified application scenarios but also further expand the application scope of the A* algorithm and enhance its application value. In summary, the future research of the A* algorithm will carry out in-depth exploration in multiple dimensions, such as principle optimisation, data structure improvement, domain application, algorithm fusion, and multi-objective optimisation, in order to continuously improve the performance and universality of the algorithm, as well as to provide powerful support for the intelligent development of more fields. Conceptualization, Y.C. and D.C.; Methodology, Y.C., J.P. and D.C.; Software, Y.G. and S.Z. ; Validation, J.P. and S.Z. ; Formal analysis, Z.L. and S.Z. ; Resources, Z.L. ; Data curation, J.P., Y.G. and Z.L. ; Writing--original draft, Y.C. and J.P.; Visualization, Y.G. ; Supervision, D.C.; Funding acquisition, Y.C. and D.C. All authors have read and agreed to the published version of the manuscript.