Abstract
Extensive studies have been conducted on the Dijkstra algorithm owing to its bright prospect. However, few of them have studied the surface path planning of mobile robots. Currently, some application fields (e.g., wild ground, planet ground, and game scene) need to solve the optimal surface path. This paper proposes an extended Dijkstra algorithm. We utilize the Delaunay triangulation to model the surface environment. Based on keeping the triangle side length unchanged, the triangle mesh on the surface is equivalently converted into a triangle on the two-dimensional plane. Through this transformation, we set up the two-dimensional developable passable channel of the surface and solve the optimal route on this channel. Traversing all the two-dimensional developable and passable paths of the surface, we can get the shortest route among all the optimal paths. Then the inverse transformation from the two-dimensional plane coordinates to the corresponding surface coordinates obtains the surface optimal path. The simulation results show that, compared with the traditional Dijkstra algorithm, this method improves the accuracy of the surface optimization path in single-robot single-target and multi-robot multi-target path planning tasks.
Highlights
Dijkstra algorithm is a classical well-known shortest path routing algorithm in 2D mobile robots’ path planning researches
The results show that the improved Dijkstra algorithm introduced in this paper can provide smoother and shorter paths in 1-robot 1-target and multi-robot multitarget path planning tasks compared with the traditional Dijkstra algorithm
To improve the error of the traditional Dijkstra algorithm when studying the surface optimal path task, we introduce the extended Dijkstra algorithm
Summary
Dijkstra algorithm is a classical well-known shortest path routing algorithm in 2D mobile robots’ path planning researches. The paper introduced a progressive path search algorithm to settle this problem, taking into account the number of transmissions and travel time This method obtained a trip planning system by integrating route and timetable information from different transportation agencies. Because the optimal path obtained by calculating the Euclidean distance between nodes may not be on the surface, it is one of the most fundamental reasons for the optimal path error of the surface All of these represent that new tools and methods are required to improve the surface optimal path planning process based on the traditional Dijkstra algorithm. At the end of this paper, a conclusion is given (Section 5)
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