Genetic Algorithm Approach for Obstacle Avoidance and Path Optimization of Mobile Robot

Mane, Sunil B.; Vhanale, Sharan

doi:10.1007/978-981-13-1513-8_66

Cited by 9 publications

(3 citation statements)

References 4 publications

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“…Evolutionary algorithms were used in three papers: GA applied to mobile robots in 2D (Mane & Vhanale, 2019) and UAVs in 3D (Hildemann & Verstegen, 2023), and GP applied to USVs in 2D (Jing et al, 2022). Thus the literature review suggests that potential exists for GP applications in 3-dimensional space with obstacle avoidance.…”

Section: Prisma Discussionmentioning

confidence: 99%

Genetic Programming to Optimise 3d Trajectories

Kotze¹,

Hildemann

Santos³

et al. 2023

Preprint

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Section: Prisma Discussionmentioning

confidence: 99%

Genetic Programming to Optimise 3d Trajectories

Kotze¹,

Hildemann

Santos³

et al. 2023

Preprint

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“…For selecting the shortest possible path in the generated visibility graph, it is possible to use a lot of different selection or optimization algorithms such as the A * algorithm [34]. In this proposal, Genetic Algorithms (GAs) are used as optimization tool [35] in order to find the shortest (and then the most efficient) path in the dense navigating network generated by the visibility graph.…”

Section: Path Optimization-selectionmentioning

confidence: 99%

Path Optimization for Mobile Robots using Genetic Algorithms

Santa¹,

Sarmiento²,

Ariza³

2022

IJACSA

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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.

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“…Autonomous robots need to be able to navigate through and around obstacles to be able to carry out tasks efficiently. This can be split into two tasks, path planning and obstacle avoidance [1,2]. Path planning relies on global information and knowledge of the area that needs to be explored.…”

Section: Introductionmentioning

confidence: 99%

On the Performance Analyses of a Modified Force Field Algorithm and Neural Network Approach for Obstacle Avoidance in Swarm Robotics

2021

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Obstacle avoidance is a major hurdle when implementing mobile robots and swarm robots. Swarm robots work in groups and therefore require an efficient and functional obstacle avoidance algorithm to stay collision free between themselves and their surroundings. This paper reviews previous research in obstacle avoidance implementation using the force field method (FFM), also known as potential field method (PFM) and a neutral network approach. Moreover, this paper aims to execute simulations using a modified force field algorithm and a neural network approach and compare them. The obtained results are analyzed to identify the performance characteristics and the time taken to perform tasks using a singular mobile robot against a swarm robot environment consisting of four and ten robots, respectively, in both simulation cases. Simulations showed that the algorithm was successful in navigating obstacles for both single and swarm robot environments. A single robot was found to take up to 340% longer to arrive at the required location compared to the first robot in the experiment. Moreover, it was found that the neural network approach showed ~ 27% improvement over the modified force field algorithm when it comes to cases where more than four robots are being used.

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Genetic Algorithm Approach for Obstacle Avoidance and Path Optimization of Mobile Robot

Cited by 9 publications

References 4 publications

Genetic Programming to Optimise 3d Trajectories

Genetic Programming to Optimise 3d Trajectories

Path Optimization for Mobile Robots using Genetic Algorithms

On the Performance Analyses of a Modified Force Field Algorithm and Neural Network Approach for Obstacle Avoidance in Swarm Robotics

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