Path planning of scenic spots based on improved A* algorithm

Abstract

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.

Combined improved A* and greedy algorithm for path planning of multi-objective mobile robot

“…The path planning of AMR is a constrained optimization problem. The algorithms include Genetic algorithm 4 , Probabilistic Roadmap 5 , Rapidly-exploring-random Tree 3 , 6 , Dijkstra algorithm 7 , A* algorithm 8 – 10 , Machine learning algorithm 11 – 13 , Ant Colony algorithm 14 , Particle Swarm Optimization 15 , Artificial potential field algorithm 16 , 17 and Breath First Search algorithm 18 , and so on.…”

“…Most researchers have changed the convergence speed of A* algorithm by improving the evaluation function. Wang et al 10 proposes a path planning method for improving the A* algorithm by weighting the heuristic function to improve the computational efficiency. Shang et al 26 proposed a guideline generated by globally planning to develop heuristic functions and variable-step A* algorithms.…”

Research on Path Planning of UAV Swarm for Decapitation Strike

“…The path planning of UAV swarm is a multi-objective optimization problem [7]. At present, researchers mainly use intelligent algorithms such as artificial potential field method(APF) [8] [9], A* algorithm [11]- [13], particle swarm optimization algorithm(PSO) [14] [15], wolf swarm optimization algorithm [16] and so on. In addition to the theoretical research on path planning algorithm, scholars have also carried out a large number of experimental verification work on path planning of UAV swarms combining tasks and scenarios [17][18][19].…”

Improved On-Demand Travel Route Planning Model with Interest Fields

“…The method is used to solve a multisite vehicle path problem that simultaneously considers economic, environmental, and social dimensions. The literature [ 13 ] proposed an improved A ∗ algorithm for the scenic path planning problem, which effectively improves the computational efficiency and reduces the road cost. The literature [ 14 ] dynamically obtains the optimal path through the remaining attractions and remaining paths in the scenic area.…”

“…e literature [13] proposed an improved A * algorithm for the scenic path planning problem, which effectively improves the computational efficiency and reduces the road cost. e literature [14] dynamically obtains the optimal path through the remaining attractions and remaining paths in the scenic area.…”