Real-time Robot Control System Research Articles

Comparing Support Vector Machines and Artificial Neural Networks in the Recognition of Steering Angle for Driving of Mobile Robots Through Paths in Plantations

The use of mobile robots turns out to be interesting in activities where the action of human specialist is difficult or dangerous. Mobile robots are often used for the exploration in areas of difficult access, such as rescue operations and space missions, to avoid human experts exposition to risky situations. Mobile robots are also used in agriculture for planting tasks as well as for keeping the application of pesticides within minimal amounts to mitigate environmental pollution. In this paper we present the development of a system to control the navigation of an autonomous mobile robot through tracks in plantations. Track images are used to control robot direction by pre-processing them to extract image features. Such features are then submitted to a support vector machine and an artificial neural network in order to find out the most appropriate route. A comparison of the two approaches was performed to ascertain the one presenting the best outcome. The overall goal of the project to which this work is connected is to develop a real time robot control system to be embedded into a hardware platform. In this paper we report the software implementation of a support vector machine and of an artificial neural network, which so far presented respectively around 93% and 90% accuracy in predicting the appropriate route.

Associative Motion Generation for Humanoid Robot Reflecting Human Body Movement

This paper proposes an intuitive real-time robot control system using human body movement. Recently, it has been developed that motion generation for humanoid robots with reflecting human body movement, which is measured by a motion capture. However, in the existing studies about robot control system by human body movement, the detailed structure information of a robot, for example, degrees of freedom, the range of motion and forms, must be examined in order to calculate inverse kinematics. In this study, we have proposed Associative Motion Generation as humanoid robot motion generation method which does not need the detailed structure information. The associative motion generation system is composed of two neural networks: nonlinear principal component analysis and Jordan recurrent neural network, and the associative motion is generated with the following three steps. First, the system learns the correspondence relationship between an indication and a motion using training data. Second, associative values are extracted for associating a new motion from an unfamiliar indication using nonlinear principal component analysis. Last, the robot generates a new motion through calculation by Jordan recurrent neural network using the associative values. In this paper, we propose a real-time humanoid robot control system based on Associative Motion Generation, that enables user to control motion intuitively by human body movement. Through the task processing and subjective evaluation experiments, we confirmed the effective usability and affective evaluations of the proposed system.

Autonomous Robot Control Using Facial Expressions

In this paper, a novel real time robot control system using human facial expressions is presented. The proposed system mainly consists of three modules: face detection, facial expression recognition and robot control. The first module aims to find the user's face from the image captured from a live video through a series of steps including skin color classification, edge detection and mathematical morphology, while the second module analyses the detected face to recognize the facial expressions like happiness, sadness, surprise, anger and neutral using Principal Component Analysis (PCA) and Euclidian distance calculation. Finally, the detected facial expressions are used as controlling commands for the robot. Experiments were conducted for respective modules. 150 images under different lightening conditions and complex background were employed. In the experiment on detecting the facial region, the rate of detection was 99.3% and on recognizing the facial expression, the recognition rate was 97.3%. The robot was then controlled autonomously with an accuracy of 100%. Combining the above three rates, the overall rate of success of our system was 97.3%.

MODELING AND MOTION CONTROL OF ROBOTIC HAND FOR TELEMANIPULATION APPLICATION

This paper presents the process of a motion control system design and implementation for a teleminulation system. The robotic manipulator/hand is a nonlinear system with coupled effects among the joints. By applying the frequency response method, the nonlinear system is modeled as decoupled linear ones, in which each joint is controlled independently. The experimental results show the feasibility of the modeling process. Based on the obtained transfer function of each joint, the digital PID controllers are designed. The motion control system for the telemanipulation system is then implemented using the software package MATLAB, Simulink, Real Time Workshop, xPC Target and a C/C++ compiler, and the hardware I/O boards, amplifiers, etc. The presented developing procedure shows a convenient way to implement a real time robotic control system.