Support Vector Machine for Behavior-Based Driver Identification System

We present an intelligent driver identification system to handle vehicle theft based on modeling dynamic human behaviors. We propose to recognize illegitimate drivers through their driving behaviors. Since human driving behaviors belong to a dynamic biometrical feature which is complex and difficult to imitate compared with static features such as passwords and fingerprints, we find that this novel idea of utilizing human dynamic features for enhanced security application is more effective. In this paper, we first describe our experimental platform for collecting and modeling human driving behaviors. Then we compare fast Fourier transform (FFT), principal component analysis (PCA), and independent component analysis (ICA) for data preprocessing. Using machine learning method of support vector machine (SVM), we derive the individual driving behavior model and we then demonstrate the procedure for recognizing different drivers by analyzing the corresponding models. The experimental results of learning algorithms and evaluation are described

The Kinematics Analysis of a Novel 3-DOF Cable-Driven Wind Tunnel Mechanism

The kinematics analysis method of a novel 3-DOF wind tunnel mechanism based on cable-driven parallel mechanism is provided. Rodrigues' parameters are applied to express the transformation matrix of the wire-driven mechanism in the paper. The analytical forward kinematics model is described as three quadratic equations using three Rodridgues' parameters based on the fundamental theory of parallel mechanism. Elimination method is used to remove two of the variables, so that an eighth-order polynomial with one variable is derived. From the equation, the eight sets of Rodridgues' parameters and corresponding Euler angles for the forward kinematical problem can be obtained. In the end, numerical example of both forward and inverse kinematics is included to demonstrate the presented forward-kinematics solution method. The numerical results show that the method for the position analysis of this mechanism is effective

How Can Brain Learn to Control a Nonholonomic System?

Humans can often conduct both linear and nonlinear control tasks after a sufficient number of trials, even if they initially do not have sufficient knowledge about the system's dynamics and the way to control it. Theoretically, it is well known that some nonlinear systems cannot be stabilized asymptotically by any linear controllers and we have reported by an f-MRI experiment that different types of information may be involved in linear and nonlinear control tasks, respectively, from a brain function mapping point of view. In this paper, from a controllability analysis, we still show a possibility that human may use a linear control scheme for such nonlinear control tasks by switching the linear controllers with a virtual constraint. It is suggested that the proposed virtual constraint can play an important role to overcome a limitation of the linear controllers and to mimic human control behavior

An Autonomous Omnidirectional Robot

RoboCup is an international research and education initiative, which aims to foster artificial intelligence and robotics research by using competitive soccer as a standard problem. This paper presents a detailed engineering design process and the outcome for an omni-directional mobile robot platform for the Robocup Middle Size League competition. A prototype that can move omnidirectionally with kicking capability was designed, built, and tested by a group of senior students. The design included a mechanical base, pneumatic kicking mechanism, a DSP microcontroller-based control system, various sensor interfacing units, and the analysis of omnidirectional motions. The testing results showed that the system was able to move omnidirectionally with a speed of ∼2 m/s and able to kick a size 5 FIFA soccer ball for a distance of at least 5 meters

Parameterless-Growing-SOM and Its Application to a Voice Instruction Learning System

An improved self-organizing map (SOM), parameterless-growing-SOM (PL-G-SOM), is proposed in this paper. To overcome problems existed in traditional SOM (Kohonen, 1982), kinds of structure-growing-SOMs or parameter-adjusting-SOMs have been invented and usually separately. Here, we combine the idea of growing SOMs (Bauer and Villmann, 1997; Dittenbach et al. 2000) and a parameterless SOM (Berglund and Sitte, 2006) together to be a novel SOM named PL-G-SOM to realize additional learning, optimal neighborhood preservation, and automatic tuning of parameters. The improved SOM is applied to construct a voice instruction learning system for partner robots adopting a simple reinforcement learning algorithm. User's instructions of voices are classified by the PL-G-SOM at first, then robots choose an expected action according to a stochastic policy. The policy is adjusted by the reward/punishment given by the user of the robot. A feeling map is also designed to express learning degrees of voice instructions. Learning and additional learning experiments used instructions in multiple languages including Japanese, English, Chinese, and Malaysian confirmed the effectiveness of our proposed system

Computer Simulation Tests of Feedback Error Learning Controller with IDM and ISM for Functional Electrical Stimulation in Wrist Joint Control

Feedforward controller would be useful for hybrid Functional Electrical Stimulation (FES) system using powered orthotic devices. In this paper, Feedback Error Learning (FEL) controller for FES (FEL-FES controller) was examined using an inverse statics model (ISM) with an inverse dynamics model (IDM) to realize a feedforward FES controller. For FES application, the ISM was tested in learning off line using training data obtained by PID control of very slow movements. Computer simulation tests in controlling wrist joint movements showed that the ISM performed properly in positioning task and that IDM learning was improved by using the ISM showing increase of output power ratio of the feedforward controller. The simple ISM learning method and the FEL-FES controller using the ISM would be useful in controlling the musculoskeletal system that has nonlinear characteristics to electrical stimulation and therefore is expected to be useful in applying to hybrid FES system using powered orthotic device

Prediction Control for Brachytherapy Robotic System

In contemporary brachytherapy procedure, needle placement at desired location is challenging due to a variety of reasons. We have designed and fabricated an image-guided robot-assisted brachytherapy system to improve the needle placement and seed delivery. In this article we have used two different predictive control strategies in order to investigate the needle insertion efficacy and system dynamics during prostate brachytherapy. First, we used neural network predictive control (NNPC) to predict an insertion force. The NNPC uses the linearized state-space model of the robotic system to predict future system performances. Second, we used feedforward model predictive control (MPC) which allows the controller to compensate the influence of a measured disturbance's impact immediately rather than waiting until the effect appears in the system. Feedback control problem for the contact force regulation is considered. The simulation results and experiments for both cases are presented and compared

Skill Evaluation from Observation of Discrete Hand Movements during Console Operation

This paper focused on discrete movements of hand in reaching actions, which necessarily occur during machine operation. The relationship between the performance of a console operation and the operator's reaching actions was investigated by applying Fitts' law and by examining a state transition of the operation. A remote operation experimental system was built using two radio controlled construction equipments, and the operator training process was analyzed empirically with those methods. The results showed the covariance of the fitting error to Fitts' law decreased as the operators' skill improved, although the fitting itself to the law was not sufficiently good. And it was confirmed that the covariance of difficulty index of the reaching action increased. These facts indicated that skill level of the discrete motion during operation can be estimated by investigating two types of the covariance

Cohesive Motion Control Algorithm for Formation of Multiple Autonomous Agents

This paper presents a motion control strategy for a rigid and constraint consistent formation that can be modeled by a directed graph whose each vertex represents individual agent kinematics and each of directed edges represents distance constraints maintained by an agent, called follower, to its neighbouring agent. A rigid and constraint consistent graph is called persistent graph. A persistent graph is minimally persistent if it is persistent, and no edge can be removed without losing its persistence. An acyclic (free of cycles in its sensing pattern) minimally persistent graph of Leader-Follower structure has been considered here which can be constructed from an initial Leader-Follower seed (initial graph with two vertices, one is Leader and another one is First Follower and one edge in between them is directed towards Leader) by Henneberg sequence (a procedure of growing a graph) containing only vertex additions. A set of nonlinear optimization-based decentralized control laws for mobile autonomous point agents in two dimensional plane have been proposed. An infinitesimal deviation in formation shape created continuous motion of Leader is compensated by corresponding continuous motion of other agents fulfilling the shortest path criteria