Quang Vinh Tran

Image segmentation based on histogram of depth and an application in driver distraction detection

This study proposes an approach to segment human object from a depth image based on histogram of depth values. The region of interest is first extracted based on a predefined threshold for histogram regions. A region growing process is then employed to separate multiple human bodies with the same depth interval. Our contribution is the identification of an adaptive growth threshold based on the detected histogram region. To demonstrate the effectiveness of the proposed method, an application in driver distraction detection was introduced. After successfully extracting the drivers position inside the car, we came up with a simple solution to track the driver motion. With the analysis of the difference between initial and current frame, a change of cluster position or depth value in the interested region, which cross the preset threshold, is considered as a distracted activity. The experiment results demonstrated the success of the algorithm in detecting typical distracted driving activities such as using phone for calling or texting, adjusting internal devices and drinking in real time.

Mobile robot localization using fuzzy neural network based extended Kalman filter

Localization is fundamental to autonomous operation of the mobile robot. In this paper, a new optimal filter namely fuzzy neural network based extended Kalman filter (FNN-EKF) is introduced to improve the localization of a mobile robot in unknown environment. The filter is a combination between a normal extended Kalman filter (EKF) installed on a differential-drive wheeled mobile robot and an online adjustment of the process noise covariance matrix Q and the measurement noise covariance matrix R. The adjustment is performed by fuzzy system and the purpose is to overcome the divergence of the EKF when the matrices Q and R are fixed or wrongly determined. The membership functions of the antecedent and consequent parts of fuzzy if-then rules in the fuzzy system are tuned by neural network. Integrating neural network into the fuzzy system called the fuzzy neural network is to gain the accuracy while reducing the time and cost in designing the membership functions. Simulating experiments have been conducted and results show that the FNN - EKF is more accurate than the EKF in localizing the mobile robot. An evaluation of the system with respect to suggestions of possible future developments is also mentioned in the paper.

Localization of networked robot systems subject to random delay and packet loss

This paper deals with the localization problem of mobile robot subject to communication delay and packet loss. The delay and loss may appear in a random fashion in both control inputs and observation measurements. A unified state-space representation is constructed to describe these mixed uncertainties. Based on it, the optimal linear estimator is developed. The main idea is the derivation of a relevance factor to incorporate delayed measurements to the being estimate. The estimator is then extended for nonlinear systems. The performance of this method is tested within the simulations in MATLAB and the experiments in a real robot system. The good localization results prove the efficiency of the method for the purpose of localization of networked mobile robot.

Easy-setup eye movement recording system for human-computer interaction

Tracking the movement of human eyes is expected to yield natural and convenient applications based on human-computer interaction (HCI). To implement an effective eye-tracking system, eye movements must be recorded without placing any restriction on the userpsilas behavior or user discomfort. This paper describes an eye movement recording system that offers free-head, simple configuration. It does not require the user to wear anything on her head, and she can move her head freely. Instead of using a computer, the system uses a visual digital signal processor (DSP) camera to detect the position of eye corner, the center of pupil and then calculate the eye movement. Evaluation tests show that the sampling rate of the system can be 300 Hz and the accuracy is about 1.8deg/s.

Localization of a unicycle-like mobile robot using LRF and omni-directional camera

This paper addresses the localization problem. The extended Kalman filter (EKF) is employed to localize a unicycle-like mobile robot equipped with a laser range finder (LRF) sensor and an omni-directional camera. The LRF is used to scan the environment which is described with line segments. The segments are extracted by a modified least square quadratic method in which a dynamic threshold is injected. The camera is employed to determine the robots orientation. The prediction step of the EKF is performed by extracting parameters from the kinematic model and input signal of the robot. The correction step is conducted with the implementation of a line matching algorithm and the comparison between lines parameters of the local and global maps. In the line matching algorithm, a conversion matrix is introduced to reduce the computation cost. Experiments have been carried out in a real mobile robot system and the results prove the applicability of the method for the purpose of localization.

Control of Internet-based robot systems using multi transport protocols

This paper proposes a novel approach to implement communication channels for Internet-based robotic systems. The exchange information between the operator and the robot is classified into categories with specific features and requirements. Appropriate transport protocols are utilized for each set of data. The TCP is for the administrative data; the UDP is for the control signals and the RTP is for the vision data. Simulations and experiments show that this approach strengthens advantages of each transport protocol while maintains good performance of the system.

Development of a tele-guidance system with fuzzy-based secondary controller

Dealing with the uncertainties of Internet characteristics is an important issue that needs being taken into account in developing Internet-based real-time systems. In this paper, we present our approach in applying fuzzy logic to develop back-up mechanisms for an Internet-based mobile robot to deal with unwanted network problems such as long delays or network interruptions. A tele-guidance application involving the remote control of a mobile robot via the Internet is set up as the context to verify the effectiveness and applicability of the proposed approach.

Behavior-based Navigation of Mobile Robot in Unknown Environments Using Fuzzy Logic and Multi-Objective Optimization

This study proposes behavior-based navigation architecture, named BBFM, to deal with the problem of navigating the mobile robot in unknown environments in the presence of obstacles and local minimum regions. In the architecture, the complex navigation task is split into principal sub-tasks or behaviors. Each behavior is implemented by a fuzzy controller and executed independently to deal with a specific problem of navigation. The fuzzy controller is modified to contain only the fuzzification and inference procedures so that its output is a membership function representing the behaviors objective. The membership functions of all controllers are then used as the objective functions for a multi-objective optimization process to coordinate all behaviors. The result of this process is an overall control signal, which is Pareto-optimal, used to control the robot. A number of simulations, comparisons, and experiments were conducted. The results show that the proposed architecture outperforms some popular behavior-based architectures in term of accuracy, smoothness, traveled distance, and time response.

Navigation of networked mobile robot using behavior-based model

This paper presents a behavior-based approach for the navigation of networked mobile robot. In the model, a complex navigation task is divided into individual behaviors. Each behavior deals with a different aspect of the network and environment. The predictive filter, stabilization control, and fuzzy logic are employed to design and implement the behavior. The coordination between behaviors is handled by a supervision layer. It determines the priorities of behaviors based on the context and then blends the output of each behavior to generate a crisp output. Experiments shows that the robot can follow users commands, avoid obstacles, and reach destination while adapt to uncertain elements such as network delay, out-of-order delivery and packet loss.