Stochastic algorithm for automatic path planning of a humanoid robot

Introduction- The incorporation of an autonomous learning system in robotics will allow the resolution of a large number of problems. One is the autonomous march of the humanoid robots due to its complexity in the great number of variables regarding this process. Objective- Develop algorithms that generate autonomous paths in a humanoid robot with various degrees of freedom. Methodology- The study begins with the development of stochastic algorithms with few dimensions. Then, it will be extended to n-dimensional situations. Afterwards, simulation tests will be carried out. And finally, the experimental tests are performed. Results- An algorithm was generated based on the physical model of the robot to create walking paths stochastically. A simulator that contemplates the kinematic constraints, including collisions, was implemented to verify the results. In addition, one hundred experimental tests were done. With these tests, the correct operation of the trajectories was verified. Conclusions- It was verified that it is possible to create a stochastic algorithm that mixes determinant and random rules to automatically generate paths in humanoid robots, hence, extending concepts generated in two-dimensional and three-dimensional spaces to n-dimensional articulated coordinates.