R. Robotin

Classification of surface electromyographic signals for control of upper limb virtual prosthesis using time-domain features

The development of a training system in the field of rehabilitation has always been a challenge for scientists. Surface electromyographical signals are widely used as input signals for upper limb prosthetic devices. The great mental effort of patients fitted with myoelectric prostheses during the training stage, can be reduced by using a simulator of such device. This paper presents an architecture of a system able to assist the patient and a classification technique of surface electromyographical signals, based on neural networks. Four movements of the upper limb have been classified and a rate of recognition of 96.67% was obtained when a reduced number of features were used as inputs for a feed-forward neural network with two hidden layers.

State estimation based on Kalman filtering techniques in navigation

This paper tackles the problem of the position measurement and estimation techniques in the robot navigation field based on Kalman filters. It presents the problem of the position estimation based on odometric, infrared and ultrasonic measurements. Further on deals with the theoretical and practical aspects of the state estimation based on Kalman filtering techniques. From the wide range of derivatives of the Kalman filtering technique there are detailed the extended Kalman filter and the one based on unscented transformation. In the second part of the paper is concluded with the results of the comparison between the different filtering algorithms and the further perspectives regarding this subject.

3D graphical simulation of an articulated serial manipulator based on kinematic models

This paper presents a portable 3D graphical simulator for a Fanuc M-6iB/2HS articulated robot. The mechanical structure is modeled using OpenGL functions implemented in Qt Framework. The robot motions are simulated based on the direct and inverse kinematics equations also presented in this paper. The simulator features include quick motion, step-by-step simulation, 3D scene control functions, objects selection functions and motion algorithms like joint interpolated motion and linear interpolated motion.

Hierarchical myoelectric control of a human upper limb prosthesis

Myolectric control is nowadays the most used approach for electrically-powered upper limb prostheses. The myoelectric controllers use electromyographic (EMG) signals as inputs. These signals can be collected from the skin surface using surface EMG sensors, or intramuscular, using needle sensors. No matter which method is used, they have to be processed before being used as controller inputs. In this paper, we present an algorithm based on an autoregressive (AR) model representation and a neural network, for EMG signal classification. The results have shown that combining a low-order AR model with a feed-forward neural network, a rate of classification of 98% can be achieved, while keeping the computational cost low. We also present a hierarchical control architecture and the implementation of the high-level controller using Finite State Machine. The solution proposed is capable of controlling three joints (i.e. six movements) of the upper limb prosthesis. The inputs of the high-level controller are obtained from the classifier, while its outputs are applied as input signals for the low-level controller. The main advantage of the proposed strategy is the reduced effort required to the patient for controlling the prosthetic device, since he only has to initiate the movement that is finalized by the low-level part of the controller.

An OpenGL Application for Industrial Robots Simulation

This paper presents a Visual C++ and OpenGL application for 3D simulation of the serial industrial robots. To develop this application we started from the forward kinematics of the robot taken into consideration. The functions implemented in the source code are able to calculate the position and orientation of each robot joint, including the position and orientation of the robot gripper. With the help of the OpenGL functions, the application is able to draw and simulate the 3D kinematic scheme of the robot. In addition, the application has a calculus module where the gripper position can be determined using particular values for the robot joints positions or orientations

Graph Search Techniques for Mobile Robot Path Planning

In a number of applications, the problem of determining the optimum path occurs. This applications range from finding the fastest path in a network, to determining the safest path for mobile vehicle, wandering on the surface of Mars. In this context, we shall limit our scope to the case of finding paths in Euclidean two-dimensional space. Moreover we shall limit the case study to movements along a surface that can be projected onto a directed graph. To be specific, we shall look at the case of finding the optimum path for a mobile robot moving along a flat surface, the robot’s configurations in the configuration space being the graph’s nodes while the graph’s arcs represent the cost of moving from one configuration to another. Researchers have tried to come with new and better navigation technologies in the last years. With the development of path finding, several new classical routing algorithms have been introduced to generate better routing solution. For example the Dijkstra algorithm is the most famous one, which evaluates the moving cost from one node to any other node and sets the shortest moving cost as the connecting cost of two nodes (Eklund et al., 1996). Around the same period of time, Best-First-Search algorithm is also introduced in the researchers’ community. A little different from the Dijkstra algorithm, Best-First-Search algorithm has a different approach because it estimates the distance from current position to goal position, and it chooses the step that is closer to the goal position (LaValle, 2006). The difficulty was growing with the new path finding situations so the old path finding algorithm had to be improved to meet the new introduced requirements. A new path finding algorithm was introduced and it was named the A* algorithm. The A* algorithm tries to combine the advantages offered by Dijkstra algorithm and Best-FirstSearch algorithm. This paper presents tests performed with various implementations of graph search algorithms (A*, D*, focused D*) as path planners for a mobile robot, focused on strong points and drawbacks of each implementation. 8

Industrial robot controller using miniature computers

This paper presents the conceptual design and the experimental results regarding the development of an industrial robot wireless controller using miniature computers. The industrial robot wireless controller is a part of a bigger project which has as main goal the development of a low-cost industrial robot simulation system. In this paper we describe the general hardware and software architecture of the controller, together with the preliminary experimental results.

Mobile Robot Navigation Using Graph Search Techniques over an Approximate Cell Decomposition of the Free Space

Mobile robots often operate in domains that are incompletely known. This article addresses the goal-directed navigation problem in unknown terrain where a mobile robot has to move from its current configuration to given goal configuration. We will present tests performed with various implementations of graph search algorithms (A*, D*, focused D*) as path planners for a mobile robot, focusing on the inherent strong points and drawbacks of each implementation.

Myoelectrical signal classification for the hierarchical control of a human hand prosthesis

Most electrically powered upper limb prostheses are myoelectrically controlled. The myoelectric controllers use surface electromyographical signals as inputs. These signals, collected from the surface of the skin, have to be preprocessed before being used as inputs for the controller. In this paper we present a classifier for surface electromyographical signals based on an autoregressive (AR) model representation and a neural network, and the higher level of the hierarchical controller implemented using Finite State Machine. The results had shown that using a low order autoregressive model combined with feed forward neural networks achieves a rate of classification of 91% while keeping the computational cost low. Using the hierarchical controller, the necessary effort to control the prosthesis by the patient is reduced since the patient only have to initiate the movement which is finalized by the low level part of the controller. The inputs of the high level controller are obtained from the classifier. The outputs of the high level controller are applied as inputs to the low level controller.

Mobile robots formation navigation with behavior based algorithms

The aim of this paper is to present concepts and algorithms for formation navigation in multi-mobile robots systems. The behavior based approach was chosen considering a correspondence with biological systems. The paper presents aspects concerning absolute and relative mobile robots positioning within a formation, as well as inter-robot communication using a client-server model and TCP/IP protocol. The simulations and real-world experiments with Pioneer mobile robots led to results backing up the theoretical part.