Multi robot distance based formation using Parallel Genetic Algorithm

López-González, A.; Meda-Campaña, J. A.; Martínez, Eduardo; Contro, P. Paniagua

doi:10.1016/j.asoc.2019.105929

Cited by 58 publications

(18 citation statements)

References 24 publications

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“…With the continuous development of wireless communication technology, multi-robot systems have attracted widespread attention in the field of intelligent control and have been used in scenarios such as resource exploration, forest fire monitoring, map construction, and disaster rescue. Multi-robot formation control [1][2][3], as an important branch of multi-intelligence systems, requires multiple robots to maintain a certain formation and avoid collisions while performing tasks, while minimizing the impact of environmental constraints on the multi-robots and influencing them to complete a specific task. Over the years, a large number of research results have been produced for the multi-robot formation control problem, and a variety of formation control methods represented by the follower method, the behavior-based control method, the virtual structure method, and the artificial potential field method have been proposed.…”

Section: Introductionmentioning

confidence: 99%

Multi-Robot Formation Control Based on CVT Algorithm and Health Optimization Management

et al. 2022

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In view of the low formation redundancy in the traditional rigid formation algorithm and its difficulty in dynamically adapting to the external environment, this study considers the use of the CVT (centroidal Voronoi tessellation) algorithm to control multiple robots to form the desired formation. This method significantly increases the complexity of the multi-robot system, its structural redundancy, and its internal carrying capacity. First, we used the CVT algorithm to complete the Voronoi division of the global map, and then changed the centroid position of the Voronoi cell by adjusting the density function. When the algorithm converged, it could ensure that the position of the generated point was the centroid of each Voronoi cell and control the robot to track the position of the generated point to form the desired formation. The use of traditional formations requires less consideration of the impact of the actual environment on the health of robots, the overall mission performance of the formation, and the future reliability. We propose a health optimization management algorithm based on minor changes to the original framework to minimize the health loss of robots and reduce the impact of environmental restrictions on formation sites, thereby improving the robustness of the formation system. Simulation and robot formation experiments proved that the CVT algorithm could control the robots to quickly generate formations, easily switch formations dynamically, and solve the formation maintenance problem in obstacle scenarios. Furthermore, the health optimization management algorithm could maximize the life of unhealthy robots, making the formation more robust when performing tasks in different scenarios.

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Section: Introductionmentioning

confidence: 99%

Multi-Robot Formation Control Based on CVT Algorithm and Health Optimization Management

et al. 2022

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“…The accuracy is not high; in addition, the genetic algorithm and the frequency sweep test cannot achieve the online real-time estimation of parameters. Therefore, some scholars tried to use some improved methods to implement their work [ 29 , 30 ]. The results show that the method can accurately identify the key parameters of the system model, and the key parameter map can accurately represent the real-time dynamic characteristics of the vehicle, which lays a good foundation for the parameter estimation and stability control of vehicles.…”

Section: Introductionmentioning

confidence: 99%

Estimation and Identification of Nonlinear Parameter of Motion Index Based on Least Squares Algorithm

Qin

2022

Computational Intelligence and Neuroscience

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Parameter identification is an important branch of automatic control. Due to its special function, it has been widely used in various fields, especially the modeling of complex systems or systems whose parameters are not easy to determine. With the development of control technology, the scale of the control object is getting larger and larger, which makes the calculation amount of the identification algorithm larger and larger. For the nonlinear system with complex structure, especially the nonlinear system containing the product of unknown parameters, the number of parameters of the over-parameterized identification method increases greatly, and the calculation amount of the identification algorithm also increases sharply. Therefore, a parameter estimation method with a small amount of calculation is explored. The results show that the proposed method can overcome the phenomenon of “data saturation”, thus improving the parameter identification results.

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“…One of the most interesting areas of study of algorithms is to solve optimization and modeling problems using artificial intelligence systems. To date, a large number of scientific works on this topic was written [1][2][3][4][5][6]. Of these, you can draw a proper understanding of the search algorithms used for a variation of the desired parameters with high accuracy.…”

Section: Introductionmentioning

confidence: 99%

Parallelization of Finding the Current Coordinates of the Lidar Based on the Genetic Algorithm and OpenMP Technology

Mochurad

Kryvinska

2021

Symmetry

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The problem of determining the position of the lidar with optimal accuracy is relevant in various fields of application. This is an important task of robotics that is widely used as a model when planning the route of vehicles, flight control systems, navigation systems, machine learning, and managing economic efficiency, a study of land degradation processes, planning and control of agricultural production stages, land inventory to evaluations of the consequences of various environmental impacts. The paper provides a detailed analysis of the proposed parallelization algorithm for solving the problem of determining the current position of the lidar. To optimize the computing process in order to accelerate and have the possibility of obtaining a real-time result, the OpenMP parallel computing technology is used. It is also possible to significantly reduce the computational complexity of the successive variant. A number of numerical experiments on the multi-core architecture of modern computers have been carried out. As a result, it was possible to accelerate the computing process about eight times and achieve an efficiency of 0.97. It is shown that a special difference in time of execution of a sequential and parallel algorithm manages to increase the number of measurements of lidar and iterations, which is relevant in simulating various problems of robotics. The obtained results can be substantially improved by selecting a computing system where the number of cores is more than eight. The main areas of application of the developed method are described, its shortcomings and prospects for further research are provided.

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Multi robot distance based formation using Parallel Genetic Algorithm

Cited by 58 publications

References 24 publications

Multi-Robot Formation Control Based on CVT Algorithm and Health Optimization Management

Multi-Robot Formation Control Based on CVT Algorithm and Health Optimization Management

Estimation and Identification of Nonlinear Parameter of Motion Index Based on Least Squares Algorithm

Parallelization of Finding the Current Coordinates of the Lidar Based on the Genetic Algorithm and OpenMP Technology

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