Improved prim algorithm and its application in unmanned aerial vehicle cruise system

Zhou, Funa; Hu, Po; Feng, Xiaoliang; Song, Yansui

doi:10.1109/ccdc.2017.7978249

Cited by 1 publication

(1 citation statement)

References 11 publications

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“…Some good two-dimensional path planning modeling methods are still in use now, such as the grid method [11], topological method [12], visibility graph method [13], and so on. There are some typical path planning algorithms, such as the Dijkstra algorithm [14], Prim algorithm [15], simulated annealing algorithm [16], A* algorithm [17], artificial potential field method [18], and the fuzzy logic-control algorithm [19], which are widely used in engineering practice.…”

Section: Introductionmentioning

confidence: 99%

3D Path Planning for the Ground Robot with Improved Ant Colony Optimization

Wang

Kan

Guo

et al. 2019

Sensors

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Path planning is a fundamental issue in the aspect of robot navigation. As robots work in 3D environments, it is meaningful to study 3D path planning. To solve general problems of easily falling into local optimum and long search times in 3D path planning based on the ant colony algorithm, we proposed an improved the pheromone update and a heuristic function by introducing a safety value. We also designed two methods to calculate safety values. Concerning the path search, we designed a search mode combining the plane and visual fields and limited the search range of the robot. With regard to the deadlock problem, we adopted a 3D deadlock-free mechanism to enable ants to get out of the predicaments. With respect to simulations, we used a number of 3D terrains to carry out simulations and set different starting and end points in each terrain under the same external settings. According to the results of the improved ant colony algorithm and the basic ant colony algorithm, paths planned by the improved ant colony algorithm can effectively avoid obstacles, and their trajectories are smoother than that of the basic ant colony algorithm. The shortest path length is reduced by 8.164%, on average, compared with the results of the basic ant colony algorithm. We also compared the results of two methods for calculating safety values under the same terrain and external settings. Results show that by calculating the safety value in the environmental modeling stage in advance, and invoking the safety value directly in the path planning stage, the average running time is reduced by 91.56%, compared with calculating the safety value while path planning.

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