Construção e controle inteligente de um manipulador robótico com dois graus de liberdade

The present work shows the development and construction of a robot manipulator with two rotary joints and two degrees of freedom, driven by three-phase induction motors. The positions of the arm and base are made, for comparison, by a fuzzy controller and a PID controller implemented in LabVIEW® programming environment. The robot manipulator moves in an area equivalent to a quarter of a sphere. Experimental results have shown that the fuzzy controller has superior performance to PID controller when tracking single and multiple step trajectories, for the cases of load and no load

Controle de Manipulador Redundante Utilizando Realimentação Visual

In conventional robot manipulator control, the desired path is specified in cartesian space and converted to joint space through inverse kinematics mapping. The joint references generated by this mapping are utilized for dynamic control in joint space. Thus, the end-effector position is, in fact, controlled indirectly, in open-loop, and the accuracy of grip position control directly depends on the accuracy of the available kinematic model. In this report, a new scheme for redundant manipulator kinematic control, based on visual servoing is proposed. In the proposed system, a robot image acquired through a CCD camera is processed in order to compute the position and orientation of each link of the robot arm. The robot task is specified as a temporal sequence of reference images of the robot arm. Thus, both the measured pose and the reference pose are specified in the same image space, and its difference is utilized to generate a cartesian space error for kinematic control purposes. The proposed control scheme was applied in a four degree-of-freedom planar redundant robot arm, experimental results are shown

Estratégias baseadas em aprendizado para coordenação de uma frota de robôs em tarefas cooperativas

In multi-robot systems, both control architecture and work strategy represent a challenge for researchers. It is important to have a robust architecture that can be easily adapted to requirement changes. It is also important that work strategy allows robots to complete tasks efficiently, considering that robots interact directly in environments with humans. In this context, this work explores two approaches for robot soccer team coordination for cooperative tasks development. Both approaches are based on a combination of imitation learning and reinforcement learning. Thus, in the first approach was developed a control architecture, a fuzzy inference engine for recognizing situations in robot soccer games, a software for narration of robot soccer games based on the inference engine and the implementation of learning by imitation from observation and analysis of others robotic teams. Moreover, state abstraction was efficiently implemented in reinforcement learning applied to the robot soccer standard problem. Finally, reinforcement learning was implemented in a form where actions are explored only in some states (for example, states where an specialist robot system used them) differently to the traditional form, where actions have to be tested in all states. In the second approach reinforcement learning was implemented with function approximation, for which an algorithm called RBF-Sarsa($lambda$) was created. In both approaches batch reinforcement learning algorithms were implemented and imitation learning was used as a seed for reinforcement learning. Moreover, learning from robotic teams controlled by humans was explored. The proposal in this work had revealed efficient in the robot soccer standard problem and, when implemented in other robotics systems, they will allow that these robotics systems can efficiently and effectively develop assigned tasks. These approaches will give high adaptation capabilities to requirements and environment changes.

Ambiente de desenvolvimentoweb multiplataforma configurável para robótica educacional

We propose an online configurable multiplatform development environment specifically developed for educational robotics applications. The environment, which appears as an extension of RoboEduc software, allows the programming of several programmable robots to be performed using the R-Educ language. We make it possible for the user to program in the language R-Educ and then translate the code to a language previously registered, compiled and then sent or executed by the robot. To develop this work, we conducted a bibliographic research about the main programming languages used in robotics, as well as their definitions and paradigms, from which it was possible to define a set of patterns considered important for the creation of this environment. Then, in the software development phase, we implemented the development environment, bearing in mind the requirements and functionality defined in the design phase. Finally, to validate the platform, we conducted some trials of programming languages and verificate if the complete cycle was satisfied - registration of language, programming in R-Educ, compilation for the registered language, compilation to the machine code and send the code for the robot

Desenvolvimento de solução para SLAM utilizando visão de teto

This work intends to show a new and few explored SLAM approach inside the simultaneous localization and mapping problem (SLAM). The purpose is to put a mobile robot to work in an indoor environment. The robot should map the environment and localize itself in the map. The robot used in the tests has an upward camera and encoders on the wheels. The landmarks in this built map are light splotches on the images of the camera caused by luminaries on the ceil. This work develops a solution based on Extended Kalman Filter to the SLAM problem using a developed observation model. Several developed tests and softwares to accomplish the SLAM experiments are shown in details