This paper describes the functioning of a manufacturing cell, in which several complex systems have been joined, such as: a robot, a computer vision system, and a lathe. To allow communication between these systems in an organised manner, diverse technologies have been used, varying from a field bus to the intranet of the company. The robot takes the role of master, coordinating the functioning of the rest of subsystems. The computer vision takes charge calculating the position and orientation of the pieces of injected plastic and communicating them to the robot. The robot gathers the pieces and takes them up to the lathe. Once the process of the lathe has finished, the robot gathers again the piece and deposits it in the packaging system. The cell is endowed with the possibility of remote adjustment and monitoring across the company's intranet. The cell's functioning conditions can be modified from the laboratory, to assure that the pieces made are within the quality limits specified by the clients.

This paper presents a new method for online path planning for robotic arms in dynamic environments. Most online path planning methods are based on local algorithms that end up being inefficient due to their lack of global information (local minima problems). The method presented in this paper avoids local minima by using a two stage framework. The robots react to dynamic environments using a local and reactive planning method restricted to a subset of its configuration space. Since the subset has few degrees of freedom the computational cost of the online stage is very low. An off-line stage chooses the subset of the configuration space that minimizes the probability of blockades and inefficient motions.

Describes a neural network model for the reactive behavioral navigation of a mobile robot. From the information received through the sensors the robot can elicit one of several behaviors (e.g., stop, avoid, stroll, wall following), through a competitive neural network. The robot is able to develop a control strategy depending on sensor information and learning operation. Reinforcement learning improves the navigation of the robot by adapting the eligibility of the behaviors and determining the linear and angular robot velocities.

This work describes a path planning method applied to a three robot system. Trajectories are optimized to allow minimum time performance of the overall system. Mathematical programming methods with a global approach are used.