Simulation and Implementation of a Mobile Robot Trajectory Planning Solution by Using a Genetic Micro-Algorithm
Robots able to roll and jump are used to solve complex trajectories. These robots have a low level of autonomy, and currently, only teleoperation is available. When researching the literature about these robots, limitations were found, such as a high risk of damage by testing, lack of information, a...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/21/11284 |
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| author | Jose Eduardo Cardoza Plata Mauricio Olguín Carbajal Juan Carlos Herrera Lozada Jacobo Sandoval Gutierrez Israel Rivera Zarate Jose Felix Serrano Talamantes |
| author_facet | Jose Eduardo Cardoza Plata Mauricio Olguín Carbajal Juan Carlos Herrera Lozada Jacobo Sandoval Gutierrez Israel Rivera Zarate Jose Felix Serrano Talamantes |
| author_sort | Jose Eduardo Cardoza Plata |
| collection | DOAJ |
| description | Robots able to roll and jump are used to solve complex trajectories. These robots have a low level of autonomy, and currently, only teleoperation is available. When researching the literature about these robots, limitations were found, such as a high risk of damage by testing, lack of information, and nonexistent tools. Therefore, the present research is conducted to minimize the dangers in actual tests, increase the documentation through a platform repository, and solve the autonomous trajectory of a maze with obstacles. The methodology consisted of: replicating a scenario with the parrot robot in the gazebo simulator; then the computational resources, the mechanism, and the available commands of the robot were studied; subsequently, it was determined that the genetic micro-algorithm met the minimum requirements of the robot; in the last part, it was programmed in simulation and the solution was validated in the natural environment. The results were satisfactory and it was possible to create a parrot robot in a simulation environment analogous to the typical specifications. The genetic micro-algorithm required only 100 generations to converge; therefore, the demand for computational resources did not affect the execution of the essential tasks of the robot. Finally, the maze problem could be solved autonomously in a real environment from the simulations with an error of less than 10% and without damaging the robot. |
| first_indexed | 2024-03-09T19:16:53Z |
| format | Article |
| id | doaj.art-2f2c5c6b19234ed690d8c1f83bb2d06d |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T19:16:53Z |
| publishDate | 2022-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-2f2c5c6b19234ed690d8c1f83bb2d06d2023-11-24T03:41:04ZengMDPI AGApplied Sciences2076-34172022-11-0112211128410.3390/app122111284Simulation and Implementation of a Mobile Robot Trajectory Planning Solution by Using a Genetic Micro-AlgorithmJose Eduardo Cardoza Plata0Mauricio Olguín Carbajal1Juan Carlos Herrera Lozada2Jacobo Sandoval Gutierrez3Israel Rivera Zarate4Jose Felix Serrano Talamantes5Escuela Superior de Ingeniería Mecánica y Eléctrica (ESIME), Instituto Politécnico Nacional (IPN), Mexico City 02550, MexicoCentro de Innovación y Desarrollo Tecnológico en Cómputo (CIDETEC), Instituto Politécnico Nacional (IPN), Mexico City 07700, MexicoCentro de Innovación y Desarrollo Tecnológico en Cómputo (CIDETEC), Instituto Politécnico Nacional (IPN), Mexico City 07700, MexicoDepartamento de Procesos Productivos, Universidad Autónoma Metropolitana (UAM), Lerma de Villada 52005, MexicoCentro de Innovación y Desarrollo Tecnológico en Cómputo (CIDETEC), Instituto Politécnico Nacional (IPN), Mexico City 07700, MexicoCentro de Innovación y Desarrollo Tecnológico en Cómputo (CIDETEC), Instituto Politécnico Nacional (IPN), Mexico City 07700, MexicoRobots able to roll and jump are used to solve complex trajectories. These robots have a low level of autonomy, and currently, only teleoperation is available. When researching the literature about these robots, limitations were found, such as a high risk of damage by testing, lack of information, and nonexistent tools. Therefore, the present research is conducted to minimize the dangers in actual tests, increase the documentation through a platform repository, and solve the autonomous trajectory of a maze with obstacles. The methodology consisted of: replicating a scenario with the parrot robot in the gazebo simulator; then the computational resources, the mechanism, and the available commands of the robot were studied; subsequently, it was determined that the genetic micro-algorithm met the minimum requirements of the robot; in the last part, it was programmed in simulation and the solution was validated in the natural environment. The results were satisfactory and it was possible to create a parrot robot in a simulation environment analogous to the typical specifications. The genetic micro-algorithm required only 100 generations to converge; therefore, the demand for computational resources did not affect the execution of the essential tasks of the robot. Finally, the maze problem could be solved autonomously in a real environment from the simulations with an error of less than 10% and without damaging the robot.https://www.mdpi.com/2076-3417/12/21/11284trajectory planningmobile robotmicro-algorithmgenetic algorithmsimulation |
| spellingShingle | Jose Eduardo Cardoza Plata Mauricio Olguín Carbajal Juan Carlos Herrera Lozada Jacobo Sandoval Gutierrez Israel Rivera Zarate Jose Felix Serrano Talamantes Simulation and Implementation of a Mobile Robot Trajectory Planning Solution by Using a Genetic Micro-Algorithm Applied Sciences trajectory planning mobile robot micro-algorithm genetic algorithm simulation |
| title | Simulation and Implementation of a Mobile Robot Trajectory Planning Solution by Using a Genetic Micro-Algorithm |
| title_full | Simulation and Implementation of a Mobile Robot Trajectory Planning Solution by Using a Genetic Micro-Algorithm |
| title_fullStr | Simulation and Implementation of a Mobile Robot Trajectory Planning Solution by Using a Genetic Micro-Algorithm |
| title_full_unstemmed | Simulation and Implementation of a Mobile Robot Trajectory Planning Solution by Using a Genetic Micro-Algorithm |
| title_short | Simulation and Implementation of a Mobile Robot Trajectory Planning Solution by Using a Genetic Micro-Algorithm |
| title_sort | simulation and implementation of a mobile robot trajectory planning solution by using a genetic micro algorithm |
| topic | trajectory planning mobile robot micro-algorithm genetic algorithm simulation |
| url | https://www.mdpi.com/2076-3417/12/21/11284 |
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