Vinícius Menezes de Oliveira

PREDICTIVE CONTROL OF AN UNDERACTUATED BRACHIATION ROBOT

Abstract This work is focused on the position control of an underactuated brachiation mobile robot with 3 links. We present the modeling of the dynamics of the robot and introduce the application of the nonlinear model-based predictive control (NMPC) to such a system. The robot has 3 revolute joints but only one of them is actuated, i. e., the robot has less control inputs than degrees of freedom. The investigation of the NMPC to control such a system is due to the fact that NMPC is able do deal with constraints (state or input limitations) in a direct way, obtaining an optimal control law. Simulation results are shown, as well as some directions for future developments.

MPC Applied to Motion Control of an Underactuated Brachiation Robot

In this paper we present the application of the nonlinear model based predictive control to the motion control of an underactuated brachiation robot. The robot has 3 links and only one joint, the second one, is actuated. The aim of this work is to develop a control input sequence that moves continuously forward the robot over an horizontal line. We investigate the use of MPC to control the underactuated brachiation robot due to its advantages, mainly the construction of an optimal stabilizing control law and due to its ability to consider in a direct way the constraints into the optimization problem. We present computational simulations with their respective results and we highlight some important considerations.

TATUBOT Robotic System for Inspection of Undergrounded Cable System

In this paper, it is presented the development of an innovative and low-cost robotic mobile system to be employed in inspection of underground electrical distribution lines and pipes. The system is composed of a robot with different sensors (infrared, acoustic, and FEF) which permit to move inside pipes and detect overheated points, partial electrical discharges and the occurrence of cable treeing. It is showed the mechanical structure of the robot, the overall architecture of the system and preliminary results.

Control of a brachiating robot for inspection of aerial power lines

The present work addresses the problem of realtime predictive control of a brachiating robot. The robot is constrained (underactuated, limited torque) and a multivariable system, which implies a very difficult problem due to the large amount of on-line computation that is required. Previous works demonstrated that it is not possible to consider the nonlinear model-based MPC under real-time constraints. To overcome this problem we present a linearized model-based MPC, which is able to be handled under real-time constraints.

Autonomous navigation for underground energy line inspection robot

This work proposes architecture of an inspection robots navigation system, aiming at monitoring underground energy lines. This architecture is composed of two modules: i. feature extraction from environment; ii navigation approach. The feature extraction module is based on the use of the edge detector by Canny algorithm and Hough transform for identification of lines from images of environment to monitoring. The lines identified correspond to cable conformation inside the duct. This information will serve to help the navigation system. For the implementation of the navigation system two approaches were proposed: navigation based on artificial neural network and navigation based on PID control. The navigation architecture can be used in real or simulated scenarios, and it was tested in a simulated environment.

Real-time predictive control of a brachiation robot

The present work addresses the problem of real time predictive control of a brachiation robot, considering that the robot is constrained and a multivariable system, which implies a very difficult problem due to the large amount of on-line computation that is required. We show that it is not possible to consider the nonlinear model-based MPC under real-time constraints. Furthermore, to overcome this problem we present a linearized model-based MPC, which is able to be handled under real-time constraints.

Using augmented state Kalman filter to localize multi autonomous underwater vehicles

The present paper describes a system for the construction of visual maps (“mosaics”) and motion estimation for a set of AUVs (Autonomous Underwater Vehicles). Robots are equipped with down-looking camera which is used to estimate their motion with respect to the seafloor and built an online mosaic. As the mosaic increases in size, a systematic bias is introduced in its alignment, resulting in an erroneous output. The theoretical concepts associated with the use of an Augmented State Kalman Filter (ASKF) were applied to optimally estimate both visual map and the fleet position.

Navigation System for an Underground Distribution Inspection Platform Using Simulation

This work proposes an architecture of an inspection robots navigation system, aiming at monitoring of power cables in underground distribution lines. This architecture is composed of two modules: i. feature extraction from environment, ii navigation approach. The feature extraction module is based on the use of the edge detector by Canny algorithm and Hough transform for identification of lines from images of environment to monitoring. The lines identified correspond to cable conformation inside the duct. This information will serve to help the navigation system. For the implementation of the navigation system two approaches were proposed: navigation based on artificial neural network and navigation based on PID control. The navigation architecture can be used in real or simulated scenarios, and it was tested in a simulated environment.

Mobile Robot Control Over the Internet with Delay Compensation

Abstract This article presents the implementation of a real time controller for mobile robots using an IP network to receive data from sensors and to send data to actuators. In order to compensate for the time delay related to the data transmission it is proposed to estimate the sensors readings that would be taken without delay. The estimation is based on an Extended Kalman Filter. Some experimental results are presented and performance of the control architecture are analyzed.

Mobile robot control using sliding mode and neural network

Abstract The complete model of a mobile robot can be seen as the cascade of a dynamic and a kinematic susb-systems. By taking advantage from this fact, a combIned controller using a sliding mode controller to controle the kinematics sub-system and a neural network computed-torque control to control the dynamics is proposed. The proof of stability is based on Lyapunov theory. SInce the kinematIc controller conforms to the restrictions of Brocketts theorem the robot can be stabilized to desired posture. Experimental real-time results are presented. Copyright