Genetic Algorithms for Path Planning in a Room with Obstacles (original) (raw)

Autonomous Robot Navigation Using a Genetic Algorithm with an Efficient Genotype Structure

2004

The goal for real-time mobile robots is to travel the shortest path in minimal time while avoiding obstacles in a navigation environment. Autonomous navigation allows robots to plan this path without the need for human intervention. The pathplanning problem has been shown to be NP-hard, thus this problem is often solved using heuristic optimization methods such as genetic algorithms. An important part of the genetic algorithm solution is the structure of the genotype that represents paths in the navigation environment. The genotype must represent a valid path, but still be simple to process by the genetic algorithm in order to reduce computational requirements. Unfortunately, many contemporary genetic path-planning algorithms use complex structures that require a significant amount of processing, which can affect the real-time response of the robot. This paper describes the development of a genotype structure that contains only the essential information for path planning, which allows for more efficient processing. A genetic algorithm using this structure was tested on a variety of simulated navigation spaces and was found to produce valid, obstacle-free paths for most cases.

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.

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.

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.

Evolutionary Approaches to Robot Path Planning

1999

The ultimate goal in robotics is to create machines which are more independent and rely less on humans to guide them in their operation. There are many subsystems which may be present in such a robot, one of which is path planning-the ability to determine a sequence of positions or configurations between an initial and goal position within a particular obstacle cluttered workspace. Many classical path planning techniques have been developed, but these tend to have drawbacks such as their computational requirements; the suitability of the plans they produce for a particular application; or how well they are able to generalise to unseen problems. In recent years, evolutionary based problem solving techniques have seen a rise in popularity, possibly coinciding with the improvement in the computational power afforded researches by successful developments in hardware. These techniques adopt some of the features of natural evolution and mimic them in a computer. The increase in the number of publications in the areas of Genetic Algorithms (GA) and Genetic Programming (GP) demonstrate the success achieved when applying these techniques to ever more problem areas. This dissertation presents research conducted to determine whether there is a place for Evolutionary Approaches, and specifically GA and GP, in the development of future path planning techniques.

Modified Genetic Algorithm-based Robot Path Planning to Avoid Static Obstacles Collision

Soft Computing Research Society eBooks, 2023

In this paper, a modified Genetic Algorithm is presented for mobile robot path planning applications in a known environment. The algorithm provides the optimal path using the modified variable-length chromosomes study uses the fitness function, which calculates the path length of the chromosomes such that the large path lengths are eliminated. The proposed algorithm uses the 8-way movement robot instead of the conventionally adopted 4-way movement commonly used in such applications. The results obtained using the proposed modified Genetic Algorithm during the study are compared with other approaches of the Genetic Algorithm. The proposed algorithm shows improvement in the convergence speed, provides better flexibility, provides shorter path length, and reduces the total time.

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.

A mobile robot path planning using genetic algorithm in static environment

Journal of Computer Science, 2008

Path planning of a mobile robot using genetic heuristics

Robotica, 1998

A genetic algorithm for the path planning problem of a mobile robot which is moving and picking up loads on its way is presented. Assuming a findpath problem in a graph, the proposed algorithm determines a near-optimal path solution using a bit-string encoding of selected graph vertices. Several simulation results of specific task-oriented variants of the basic path planning problem using the proposed genetic algorithm are provided. The results obtained are compared with ones yielded by hill-climbing and simulated annealing techniques, showing a higher or at least equally well performance for the genetic algorithm.

Genetic Algorithms for Path Planning in a Room with Obstacles (original) (raw)

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