Abstract
In this research article, the path planning of a mobile robot done by using ant colony optimization (ACO) with guidance factor. A two-dimensional (2-D) threat map structure design for path planning strategy, in which threat points are fixed in the path of moving robot. The two main objective of this research; to reach the mobile robot at the target position by using optimal route using ACO strategy. Secondly, by using guidance factor all the ants of ACO arrive at the fixed targeted area. Moreover, the results of the proposed algorithm compares with the classical ant system methodology. The simulated results show that the design method has short path planning and less steady state error to reach the designated path robustly.
Highlights
The mobile robot track or path planning is the most basic and the most important part of mission planning
In order to ensure that the ant can reach the target node, unlike the general Ant Colony Optimization (ACO) algorithm, the state transition probability of this study introduces the guidance factor δj(n), so that the ant search has a certain direction, even if the ant searches for the track in the direction of the target node, in which α, β and γ represent the importance parameters of pheromone, heuristic factor and guidance factor respectively
In this paper the ACO algorithm is applied for the pathplanning problem of mobile robot
Summary
The mobile robot track or path planning is the most basic and the most important part of mission planning. The robot path-planning problem is a combinatorial optimization problem and an important branch of the optimization field It is mainly through the study of mathematical methods to find the optimal arrangement, grouping or screening of discrete events. Select the appropriate path through the intelligent optimization algorithm The disadvantage of this method is that the determination of the location and number of navigation nodes often requires repeated consideration. Whenever the threat field model changes the navigation nodes and VORONOI graphs need to be reconstructed [4]. This method is not adaptable for sudden new threats. Without the need to set navigation nodes and construct a VORONOI map it can automatically search for the minimum cost track in free space and has strong adaptive ability
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