• Technologie
  • Équipement électrique
  • Industrie des matériaux
  • La vie numérique
  • politique de confidentialité
  • Ô nom
Emplacement: Accueil / Technologie / Planification du chemin des spots panoramiques basés sur l'amélioration de un algorithme A *

Planification du chemin des spots panoramiques basés sur l'amélioration de un algorithme A *

Plateforme de services à guichet unique |
1346

Avec le développement de l'économie sociale, le niveau de vie des gens s'est considérablement amélioré et le tourisme est devenu l'un des moyens les plus enthousiastes pour les gens dans leur temps libre.Pour les touristes, le terrain des endroits panoramiques est souvent robuste, il est donc essentiel de garder suffisamment d'énergie pour les visiter. At the same time, for scenic spots, an efficient tour path can also reduce the congestion of scenic spots and improve the resource utilization of scenic spots, which is conducive to the sustainable and healthy development of scenic spots1.Par conséquent, la planification raisonnable du chemin de la tournée est très importante pour l'expérience des touristes.

For the optimal path solving problem, researchers have proposed many classical algorithms including dijestra algorithm, Flow Direction algorithm2, etc. Dijkstra algorithm was proposed in 1959, and the algorithm is suitable for static networks, that is, when the weight in the network is fixed and there is no negative weight3.Dans l'amélioration de l'algorithme, Zhang et al. proposed a path planning method based on dijkstra, which can save driving time and oil consumption4.Rosita et al. used vector normalization technology combined with dijkstra algorithm to achieve the optimal distribution path of products5.Sabri et al. used the combination of dijkstra algorithm and ant colony algorithm to find the safest escape path in high-rise buildings6. Ant colony algorithm was proposed by Italian scientist Dorigo according to the foraging process of animals, and the algorithm was originally used to solve the traveling salesman problem7.Après cela, de nombreux chercheurs ont amélioré l'algorithme, par exemple, Zhou et al. optimized the intelligent logistics distribution path based on the improved ant colony algorithm, which was better to improve the dynamic optimization performance of the algorithm8.Yu et al. combined a special genetic operator in the ant colony algorithm, which not only avoids the local search limitations of the ant colony algorithm, but also enhances the global optimal searching ability of the ant colony algorithm9.De plus, il existe de nombreux algorithmes et améliorations pour résoudre le problème de chemin le plus court, tel que Miao et al.a proposé un algorithme de colonie de fourmis adaptatif amélioré. While improving the real-time and security of robot path planning, balance the convergence and global search ability of ant colony algorithm, and transform the path planning problem into a multi-objective optimization problem by introducing multi-objective performance index, so as to realize the global comprehensive optimization of robot path planning10.Hsieh et al. proposed a route planning algorithm which combines the two-way fast-exploring random tree algorithm and greedy algorithm to generate various route planning schemes for ice navigation, and evaluated and selected a relatively optimal route with a lower risk scheme through a risk index11.Rakita et al. proposed a new sampling-based path planning method, which quickly finds solutions to high-dimensional path planning problems by minimizing the number of collision check samples12.Pan et al. used the improved floyd algorithm to design the optimal delivery path for take-out food, so that the travel time of the vehicle after optimization was shortened and the time efficiency was improved, but the algorithm did not consider the impact of road conditions13.Wu et al. combined normal distributed random numbers with genetic algorithms, and considered traveling least costs and the traveling highest experience index to construct the optimal tourism path14.Bien que les algorithmes aient été améliorés pour leurs problèmes respectifs, certaines de leurs lacunes inhérentes sont difficiles à éradiquer.En tant que méthode de recherche directe la plus efficace pour résoudre le chemin le plus court du réseau routier statique, un algorithme * a été amélioré d'innombrables.Wang et al.A introduit le facteur de virage en algorithme A * pour résoudre le problème de chemin le plus court. At the same time, they proposed a dynamic path planning method based on the A* algorithm, which can effectively search the shortest path and avoid collision15.Liu et al. Proposed an improved A* algorithm to solve the combination of normal channel and berthing channel16.Uttendorf et al. combined the fuzzy inference system with the A* algorithm to generate a path map for automatically guided vehicles17.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 cost18.Shin et al. proposed an improved A* algorithm using Automatic Identification System (AIS) and weather data, and it finds the optimal paths by minimizing the estimated time of arrival generated by machine learning through 16-way node exploration19.Alani et al. proposed a new technique that consists of a hybridizing of A* algorithm and ant colony optimization, and the new technology can more accurately find the best parking path20.Pradhan et al. and Pardines both proposed to implement shopping guide path recommendations based on consumers' shopping lists, but they did not consider the problem of supermarket space modeling21,22.Ma et al. proposed a navigation path planning method for articulated underground scrapers based on improved A* algorithm to improve search efficiency23.Rahul et al. solved the problem of robotic path planning using a combination of A* algorithm and Fuzzy Inference, which finds the shortest path and generates the result in a finite time24.

Dans l'étude de planification de chemin ci-dessus, il existe de nombreuses méthodes Dijkstra et algorithmes d'intelligence Swarm.Bien que les algorithmes aient été améliorés pour leurs propres problèmes, l'inefficacité de l'algorithme Dijkstra lui-même et le problème de l'algorithme de colonie de fourmis qui est sensible aux paramètres de l'algorithme sont difficiles à résoudre.De plus, l'amélioration d'un algorithme * a toujours le problème d'ignorer le coût de la route.Afin de fournir une meilleure planification d'itinéraire panoramique pour les passagers, ce document présente une méthode de planification d'itinéraire basée sur l'amélioration d'un algorithme *.En pondérant de façon exponentielle la fonction heuristique de l'algorithme A *, l'efficacité de calcul de l'algorithme est améliorée et l'algorithme A * est amélioré en utilisant les informations sur la condition routière des points scéniques comme indice d'évaluation, ce qui rend l'algorithme plus applicable à laPlanification réelle de l'itinéraire pittoresque.

Path planning of scenic spots based on improved A* algorithm