Low-Computational-Load Real-time Path Planning and Trajectory Control based on Artificial Potential Fields

The presented project shows the theoretical development, design and subsequent implementation of a low-computational-load path planning system and the corresponding movement control for differential type robots. The model contemplates the use of Artificial Potential Fields for both aspects, control and planning. The theoretical development is based on the combination of the Local Minimum Method with a kinematic motion control model. The model simulations are performed in a Python programming environment (for the generation of the optimal path based on image processing) and in Matlab (for initial trajectory tracking simulation). The subsequent implementation is carried out by means of the GLADIUS ME32A robot in a controlled environment. Likewise, the system is powered by peripheral artificial vision and Wi-Fi communication.