In recent decades, robotic locomotion has applied different techniques to emulate central pattern generators (CPGs). The theory of CPG explains the biological functions of motor control in living organisms. This paper presents an unpublished model for coupled nonlinear oscillators. This model employs a canonical nonlinear differential equation system to coordinate joint activity. The analysis, conducted under the criteria of chaos and bifurcation theory, determines that the new model is successful and without the presence of chaos. The new model is compared with other cases, including the Wilson–Cowan, Hopf, and Van Der Pol models, as well as with the operability of different robots. It highlights the new model’s advantages in terms of versatility, simplicity, and processing, as well as the comparisons of metrics of locomotion, such as support factor and symmetry index between hemibody metrics. The new model is applied to the locomotion of two quadruped robots (a crab and a dog) used in research on transitions between types of locomotion, considering both physical and computational limitations.

An industrial approach to the use of artificial vision is worked, and we are searching for the improvement of the welding process using a robotic arm. These kinds of robots in the last years have been associated to high accuracy tasks like classification, welding, object manipulation, assembly, and so on. Generally, the artificial vision is not used in works which use manipulator arm; this is normally due to the robot programmer who plans the robot task, which is executed cyclically, however, there are some approaches where different tasks using artificial vision are implemented. In this chapter, we present a retrofitting process of a manipulator welder arm Miller MR-2000, and the development of an artificial vision system, which could be used in the positioning of the machine. The developed system is able to look for areas suitable for the welding task between two pieces of material within a workspace; this process is possible using techniques of computational vision and image processing. Subsequently, the algorithm calculates the number of welding points based on the area identified previously, and finally, it sends the respective coordinates by means of G code to the robot for welding the pieces.