This work presents a three-dimensional geometric resolution method to calculate the complete inverse kinematics of a 7-degree-of-freedom articulated arm, including the hand itself. The method is classified as an analytical method with geometric solution, since it obtains a precise solution in a closed number of steps, converting the inverse kinematic problem into a three-dimensional geometric model. To simplify the problem, the kinematic decoupling method is used, so that the position of the wrist is calculated independently on one hand with information on the orientation of the hand, and the angles of the rest of the arm are calculated from the wrist.

This article describes a bipedal walking algorithm with inverse kinematics resolution based solely on geometric methods, so that all mathematical concepts are explained from the base, in order to clarify the reason for this solution. To do so, it has been necessary to simplify the problem and carry out didactic work to distribute content. In general, the articles related to this topic use matrix systems to solve both direct and inverse kinematics, using complex techniques such as decoupling or the Jacobian calculation. By simplifying the walking process, its resolution has been proposed in a simple way using only geometric techniques.

This work presents a perception system applied to robotic manipulation, that is able to assist in navigation, household waste classification and collection in outdoor environments. This system is made up of optical tactile sensors, RGBD cameras and a LiDAR. These sensors are integrated on a mobile platform with a robot manipulator and a robotic gripper. Our system is divided in three software modules, two of them are vision-based and the last one is tactile-based. The vision-based modules use CNNs to localize and recognize solid household waste, together with the grasping points estimation. The tactile-based module, which also uses CNNs and image processing, adjusts the gripper opening to control the grasping from touch data. Our proposal achieves localization errors around 6 %, a recognition accuracy of 98% and ensures the grasping stability the 91% of the attempts. The sum of runtimes of the three modules is less than 750 ms.

ABSTRACT Currently, research in robotics, artificial intelligence and drones are advancing exponentially, they are directly or indirectly related to various areas of the economy, from agriculture to industry. With this context, this project covers these topics guiding them, seeking to provide a framework that is capable of helping to develop new future researchers. For this, we use an aerial vehicle that works autonomously and is capable of mapping the scenario and providing useful information to the end user. This occurs from a communication between a simple programming language (Scratch) and one of the most important and efficient robot operating systems today (ROS). This is how we managed to develop a tool capable of generating a 3D map and detecting objects using the camera attached to the drone. Although this tool can be used in the advanced fields of industry, it is also an important advance for the research sector. The implementation of this tool in intermediate-level institutions is aspired to provide the ability to carry out high-level projects from a simple programming language.