Path planning of a mobile robot using genetic heuristics (original) (raw)
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Path Optimization for Mobile Robot Using Genetic Algorithm
This study presents the concept of using the Genetic Algorithm approach to resolve the mobile robot path planning problem in a static environment with predictable terrain. We present our initial Idea for using genetic algorithms to assist a controllable mobile robot to hunt out an optimal path between a starting and ending point in an exceedingly grid environment. The two problems that occur in an exceedingly very mobile robot's path planning are finding the shortest path and a collision-free path. These should be achieved during a static environment of obstacles. This work is different from other works within the aspect of stationing the obstacles and running the genetic algorithm with user input iterations. This work ensures that the goal position is the global minimum path of the total potential. During this work, anytime the obstacles keep changing the positions. This work is finished in an environment that's modeled in space-time and collision-free path by a variation of the Genetic algorithm. A genetic algorithm is capable of competing with every other learning algorithm in terms of accuracy and high performance. Because the number of iterations keeps on increasing, the algorithm draws the shortest and collision-free path. When the obstacles, the initial and destination points, so the number of iterations are set the ultimate word result that's the shortest and collision-free path is acquired.
Mobile robot path planning using genetic algorithms
Lecture Notes in Computer Science, 1999
In this study we present our initial idea for using genetic algorithms to help a controllable mobile robot to find an optimal path between a starting and ending point in a grid environment. The mobile robot has to find the optimal path which reduces the number of steps to be taken between the starting point and the target ending point. GAs can overcome many problems encountered by traditional search techniques such as the gradient based methods. The proposed controlling algorithm allows four-neighbor movements, so that path-planning can adapt with complicated search spaces with low complexities. The results are promising.
A mobile robot path planning using genetic algorithm in static environment
Journal of Computer Science, 2008
In this study we present our initial idea for using genetic algorithms to help a controllable mobile robot to find an optimal path between a starting and ending point in a grid environment. The mobile robot has to find the optimal path which reduces the number of steps to be taken between the starting point and the target ending point. GAs can overcome many problems encountered by traditional search techniques such as the gradient based methods. The proposed controlling algorithm allows four-neighbor movements, so that path-planning can adapt with complicated search spaces with low complexities. The results are promising.
Enhanced Genetic Algorithm for Mobile Robot Path Planning in Static and Dynamic Environment
2016
Path planning is an important component for a mobile robot to be able to do its job in different types of environments. Furthermore, determining the safest and shortest path from the start location to a desired destination, intelligently and in quickly, is a major challenge, especially in a dynamic environment. Therefore, various optimisation methods are recommended to solve the problem, one of these being a genetic algorithm (GA). This paper investigates the capabilities of GA for solving the path planning problem for mobile robots in static and dynamic environments. First, it studies the different GA approaches. Then, it carefully designs a new GA with intelligent crossover to optimise the search process in static and dynamic environments. It also conducts a comprehensive statistical evaluation of the proposed GA approach in terms of solution quality and execution time, comparing it against the well-known A* algorithm and MGA in a static scenario, and against the Improved GA in a ...
Genetic algorithm for path planning of mobiles robots in dynamic environment
Genetic algorithms are commonly used to solve mobile robots path planning problems. Several techniques have been proposed by researchers to improve the efficiency of the genetic algorithm. In this paper, we propose an improvement of the genetic mutation operator for dynamic environments. We compared our method with conventional genetic algorithm and improved genetics algorithms provided by the literature. The comparison was made by an application of these different genetics algorithms in two dynamics environments. Experimental results demonstrate the superiority of our method as compared to what is already known.
Autonomous local path planning for a mobile robot using a genetic algorithm
2004
This work presents results of our work in development of a genetic algorithm based path-planning algorithm for local obstacle avoidance (local feasible path) of a mobile robot in a given search space. The method tries to find not only a valid path but also an optimal one. The objectives are to minimize the length of the path and the number of turns. The proposed path-planning method allows a free movement of the robot in any direction so that the path-planner can handle complicated search spaces.
Path Optimization for Mobile Robots using Genetic Algorithms
International Journal of Advanced Computer Science and Applications, 2022
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 obstacles presented in the environment and low-medium resolution images.
2018
In this study, an improved crossover operator is suggested, for solving path planning problems using genetic algorithms (GA) in static environment. GA has been widely applied in path optimization problem which consists in finding a valid and feasible path between two positions while avoiding obstacles and optimizing some criteria such as distance (length of the path), safety (the path must be as far as possible from the obstacles) ...etc. Several researches have provided new approaches used GA to produce an optimal path. Crossover operators existing in the literature can generate infeasible paths, most of these methods dont take into account the variable length chromosomes. The proposed crossover operator avoids premature convergence and offers feasible paths with better fitness value than its parents, thus the algorithm converges more rapidly. A new fitness function which takes into account the distance, the safety and the energy, is also suggested. In order to prove the validity o...
Mobile Robot Path Planning Using Hybrid Genetic Algorithm and Traversability Vectors Method
2004
The shortest/optimal path generation is essential for the efficient operation of a mobile robot. Recent advances in robotics and machine intelligence have led to the application of modern optimization method such as the genetic algorithm (GA), to solve the path-planning problem. However, the genetic algorithm path planning approach in the previous works requires a preprocessing step that captures the connectivity of the free-space in a concise representation. In this paper, GA path-planning approach is enhanced with feasible path detection mechanism based on traversability vectors method. This novel idea eliminates the need of free-space connectivity representation. The feasible path detection is performed concurrently while the GA performs the search for the shortest path. The performance of the proposed GA approach is tested on three different environments consisting of polygonal obstacles with increasing complexity. In all experiments, the GA has successfully detected the near-optimal feasible traveling path for mobile.