Abstract
This article proposes a path planning strategy for mobile robots based on image processing, the visibility graphs technique, and genetic algorithms as searching/optimization tool. This proposal pretends to improve the overall execution time of the path planning strategy against other ones that use visibility graphs with other searching algorithms. The global algorithm starts from a binary image of the robot environment, where the obstacles are represented in white over a black background. After that four keypoints are calculated for each obstacle by applying some image processing algorithms and geometric measurements. Based on the obtained keypoints, a visibility graph is generated, connecting all of these along with the starting point and the ending point, as well as avoiding collisions with the obstacles taking into account a safety distance calculated by means of using an image dilation operation. Finally, a genetic algorithm is used to optimize a valid path from the start to the end passing through the navigation network created by the visibility graph. This implementation was developed using Python programming language and some modules for working with image processing ang genetic algorithms. After several tests, the proposed strategy shows execution times similar to other tested algorithms, which validates its use on applications with a limited number of ob-stacles presented in the environment and low-medium resolution images.
Highlights
Today more than ever, robotics is part of the daily life in most of the world specially in big cities where the automation and smart stuff is everywhere
These visibility graphs generates a high dense network of possible paths through the some navigation keypoints obtained from the obstacles image that is related with the navigation scene to solve by the mobile robot
The path planning strategy for mobile robots proposed in this article is based on image capture of the navigating environment in which the robot is involved [28], [29], where the obstacles are perfectly differentiated from the void room
Summary
Robotics is part of the daily life in most of the world specially in big cities where the automation and smart stuff is everywhere. Researching areas like UAVs (Unmanned Aerial Vehicles) and self-driving or autonomous vehicles have maintained the interest on one of the most important issues for mobile robots, the path planning. In this area, one of the most used algorithm has been the visibility graphs [9] supported by image processing algorithms [10]. One of the most used algorithm has been the visibility graphs [9] supported by image processing algorithms [10] These visibility graphs generates a high dense network of possible paths through the some navigation keypoints obtained from the obstacles image that is related with the navigation scene to solve by the mobile robot. A lot of different optimization algorithms has been used for the path planning issue such as ant colony optimization [12], [13], particle swarm for mobile robots [14], [15], [16], [17], chaotic particle swarm [18] particle swarm for manipulators [19], brain storm optimization [20], Fuzzy-Wind Driven algorithm [21], rapidly-exploring trees [22], gray wolf algorithm [23] among others
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