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.
Highlights
The robot is one of the greatest inventions of human beings in the twentieth century and has wide applications [1,2,3]
This paper studies the application of ant colony algorithm in robot path planning
We presented the heuristic function with a safety value introduced and designed two ways of introducing safety values
Summary
The robot is one of the greatest inventions of human beings in the twentieth century and has wide applications [1,2,3]. This paper studies the application of ant colony algorithm in robot path planning. For the purpose of facilitating the follow-up work and improving the efficiency of path planning, we need to do a two-step process for the robot workspace before using the 3D grid method for modeling. Z-axis can be reset based on the actual size of the robot, obstacle surmounting ability of consideration the storage environmental information and the accuracy of path planning. We set the height of the robot’s obstacle crossing to one unit of grid length (0.5 m), the coordinate difference on the Z-axis between the robot’s current position and its position is which means that robots do not have the ability to raise no more than one unit length ( Δz ≤ 0.5m ),D(0,0,l) or drop more than 0.5 m in one step. A =1 where q is a total number of discrete points traveled by the robot
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