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

Abstract

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.

A Performance Review of Collision-Free Path Planning Algorithms

“…In Table 6, additional information from previous research related to PSO is shown. Ant colony optimization (ACO) is usually studied under varying network environments, such as grid network and Voronoi diagram [107][108][109][110][111][112][113][114][115][116][117][118][119]. Few papers, likewise, researched GA with ACO and coordinate system [120].…”

Efficient Deployment with Throughput Maximization for UAVs Communication Networks

“…ACO-based coordinated path planning for multi-UAV networks under threat conditions is discussed in [ 42 ]. Three-dimensional path planning for robots to avoid local maxima is elaborated in [ 43 ]. Parallel genetic algorithm-based multi-UAV path and trajectory is discussed in [ 44 ].…”

A Multi-Scale Map Method Based on Bioinspired Neural Network Algorithm for Robot Path Planning

“…Map representation is a cartographic method to express the geographical height, topography and surface features on the plane in various ways by means of graphics with the aid of natural language. Extensive research has implemented a variety of 3D environment modeling map representation methods, including digital point cloud method [23], [24], triangular grid map method [25], [26], traditional node map method [27], [28], and square grid map method [29]- [31] and so on. The square grid method is a kind of map representation method with grid as cartographic unit, which reflects the characteristics of cartographic objects.…”