Simulation and Implementation of a Mobile Robot Trajectory Planning Solution by Using a Genetic Micro-Algorithm

Abstract

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.

Non-Parametric Calibration of the Inverse Kinematic Matrix of a Three-Wheeled Omnidirectional Mobile Robot Based on Genetic Algorithms

“…Genetic algorithm optimization has many practical applications in robotics [18][19][20][21][22]. In this paper, genetic optimization is applied in an offline procedure that recomputes the odometry from the mobile robot data registered in 36 representative straight and curved trajectories.…”