A nonlinear control law for the quad-rotor of a low-complexity, global approximation-free from system uncertainties and external disturbances are described in this paper. The control law guarantees convergence to a small bounded error using a prescribed performance function. The stability of the proposed nonlinear control system is also proven by the Lyapunov stability theorem. The advantage of this technique is that it has a simpler form than any other nonlinear compensators and is applicable to any nonlinear systems without precise knowledge of the systems. In this paper, the proposed approach is applied to attitude/altitude control of a quad-rotor. Numerical simulations are performed to investigate the proposed nonlinear attitude control law by applying it to an uncertain quadcopter system with external disturbances.

Bearing and frequency measurements of TMA (Target Motion Analysis) in passive line array SONAR have lower bearing rate and frequency doppler, and are not detected or tracked continuously because of various ocean environments. This is a main reason to effect the TMA performance and it takes a long time to get TMA solutions. We propose the bearing and frequency TMA(BFTMA) using accumulative batch estimation to solve the TMA problem of line array passive SONAR. The accumulative batch estimation structure is based on MLE (Maximum Likelihood Estimation) but used accumulative measurements. The accumulative batch estimation is applied for the BFTMA with nonlinear Kalman filter to estimate the target range, speed and course. Simulation and sea data analysis were carried out to verify the performance and applicability of the proposed techniques.

This paper proposes a noble voice recognition method based on an adaptive MFCC and deep learning for embedded systems. To enhance the recognition ratio of the proposed voice recognizer, ambient noise mixed into the voice signal has to be eliminated. However, noise filtering processes, which may damage voice data, diminishes the recognition ratio. In this paper, a filter has been designed for the frequency range within a voice signal, and imposed weights are used to reduce data deterioration. In addition, a deep learning algorithm, which does not require a database in the recognition algorithm, has been adapted for embedded systems, which inherently require small amounts of memory. The experimental results suggest that the proposed deep learning algorithm and HMM voice recognizer, utilizing the proposed adaptive MFCC algorithm, perform better than conventional MFCC algorithms in its recognition ratio within a noisy environment.

This paper presents a study of localization methods based on particle filter using 2D laser sensor measurements and road feature map information, for autonomous vehicles. In order to navigate in an urban environment, an autonomous vehicle should be able to estimate the location of the ego-vehicle with reasonable accuracy. In this study, road features such as curbs and road markings are detected to construct a grid-based feature map using 2D laser range finder measurements. Then, we describe a particle filter-based method for accurate positional estimation of the autonomous vehicle in real-time. Finally, the performance of the proposed method is verified through real road driving experiments, in comparison with accurate DGPS data as a reference.

In light of recently developed 3D printers for rapid prototyping, there is increasing attention on the 3D scanner as a 3D data acquisition system for an existing object. This paper presents a prototypical 3D scanner based on a striped laser light image. In order to solve the problem of shadowy areas, the proposed 3D scanner has two cameras with one laser light source. By using a horizontal rotation table and a rotational arm rotating about the latitudinal axis, the scanner is able to scan in all directions. To remove an additional optical filter for laser light pixel extraction of an image, we have adopted a differential image method with laser light modulation. Experimental results show that the scanner's 3D data acquisition performance exhibited less than 0.2 mm of measurement error. Therefore, this scanner has proven that it is possible to reconstruct an object's 3D surface from point cloud data using a 3D scanner, enabling reproduction of the object using a commercially available 3D printer.

In this paper, we introduce a flexible gripper that we have engineered to precisely pinch in parallel and compliantly grasp objects. As found in most conventional industrial grippers, the parallel pinching property is essential for precise manipulation. On the other hand, the grippers with a flexible structure are more adept at grasping objects with arbitrary shapes and softness. To achieve these disparate properties, we introduce a flexible gripper mechanism composed of multiple flexible beam structures. Utilizing these beam structures, the proposed gripper is able to grasp arbitrarily shaped objects. Additionally, a unique combination of flexible beams enables the gripper to pinch objects using the parallel fingertips for enhanced precision. A detailed description of the proposed mechanism is provided, and an analysis of the strength and stiffness of the fingertip and finger body is presented. The Results section compares the theoretical and experimental analyses and verifies the properties and performance of the proposed gripper.

A two-wheeled balancing mobile robot (TWBMR) has the characteristics of both nonlinear and underactuated system. In this paper, the disturbances acting on a TWBMR are classified into body disturbance and wheel disturbance. Additionally, we describe a nonlinear disturbance observer, which is suitable as a single input multi-output (SIMO) system for the longitudinal motion of TWBMR. Finally, we propose a reasonable disturbance compensation technique that combines the indirect reference input of equilibrium point and the direct torque compensation input. Simulations and experimental results show that the proposed disturbance compensation method is an effective way to achieve robust postural stability, specifically on inclined terrains.

This paper presents a performance evaluation of various feature-initialization algorithms for forward-viewing mono-camera based simultaneous localization and mapping (SLAM), specifically in indoor environments. For mono-camera based SLAM, the position of feature points cannot be known from a single view; therefore, it should be estimated from a feature initialization method using multiple viewpoint measurements. The accuracy of the feature initialization method directly affects the accuracy of the SLAM system. In this study, four different feature initialization algorithms are evaluated in simulations, including linear triangulation; depth parameterized, linear triangulation; weighted nearest point triangulation; and particle filter based depth estimation algorithms. In the simulation, the virtual feature positions are estimated when the virtual robot, containing a virtual forward-viewing mono-camera, moves forward. The results show that the linear triangulation method provides the best results in terms of feature-position estimation accuracy and computational speed.

Bin picking is a task of picking a small object from a bin. For accurate bin picking, the 3D pose information, position, and orientation of a small object is required because the object is mixed with other objects of the same type in the bin. Using this 3D pose information, a robotic gripper can pick an object using exact distance and orientation measurements. In this paper, we propose a 3D vision technique for accurate measurement of 3D position and orientation of small objects, on which a paper label is stuck to the surface. We use a maximally stable extremal regions (MSERs) algorithm to detect the label areas in a left bin image acquired from a stereo camera. In each label area, image features are detected and their correlation with a right image is determined by a stereo vision technique. Then, the 3D position and orientation of the objects are measured accurately using a transformation from the camera coordinate system to the new label coordinate system. For stable measurement during a bin picking task, the pose information is filtered by averaging at fixed time intervals. Our experimental results indicate that the proposed technique yields pose accuracy between 0.4~0.5mm in positional measurements and $0.2-0.6^{\circ}$ in angle measurements.

In this paper, important design parameters for parallel kinematic robots are defined, paying special attention to machining errors which may cause kinematic errors at the end effector of a robot. The kinematic effects caused by each design parameter, as well as their upper/lower limits, are analyzed here. To do so, we have developed a novel software program to compute kinematic errors by considering its defined design parameters. With this program, roboticists designing parallel kinematic robots can understand the important design parameters for which upper/lower allowances have to be strictly controlled in the design process. This tactic can be used for the design of high-speed, parallel kinematic robots to reduce the design/manufacturing costs and increase kinematic precision.

Cooperation and collaboration with robots are key functions of robotic utility that are currently developing. Thus, robots should be safe and resemble human beings to cope with these needs. In particular, dual-arm robots that mimic human kinetics are becoming the focus of recent industrial robotics research. Their size is similar to the size of a human adult; however, they lack natural, human-like motion. One of the critical reasons for this is the shoulder complex. Most recent dual-arm robots have only 2 degrees of freedoms (DOFs), which significantly limits the workspace and mobility of the shoulders and arms. Therefore, a redundant shoulder complex could be very important in new developments that enable new capabilities. However, constructing a kinematically redundant shoulder complex is difficult because of spatial constraints. Therefore, we propose a novel, redundant shoulder complex for a human-like robot that is driven by flexible wire tendons. This kinematically redundant shoulder complex allows human-like robots to move more naturally because of redundant DOFs. To control the proposed shoulder complex, a hybrid control scheme is used. The positioning precision has also been considered, and the ability of the shoulder complex to perform several human-like motions has been verified.

This paper proposes a new global positioning system (GPS) receiver algorithm to improve the positioning accuracy of a transporter using fault detection and isolation techniques from satellite signals. To improve the positioning accuracy, several factors including a feasible number of satellite signals, SNR, NAV Measurement Quality Indicator (mesQI), and Doppler, among others, have been utilized in the proposed algorithm. To increase the number of feasible satellite signals, an erroneous satellite signal has been replaced by the previous one. In conventional approaches, received GPS signals are analyzed and directly determined to be contaminated or not. The only clean signals are utilized for identifying the current location. This fault detection and isolation (FDI) feasibility test is popular for commercial GPS receivers. In the urban environment, especially near a building, the feasible number of satellite signals becomes insufficient to position the transporter. To overcome this problem, satellite signals are efficiently selected and recovered. Additionally, using the proposed GPS receiver algorithm, a feasible number of satellite signals can be increased, thereby improving the positional accuracy. Real world experiments using a transporter that carries blocks in a shipyard have demonstrated the superiority of the proposed algorithm compared to conventional approaches.

In this paper, a novel scheme with which to mitigate a repeat-back jamming effect is proposed for the GPS L1 coarse/acquisition signal. The proposed scheme estimates the code tracking bias caused by repeat-back jamming signals using a Combined Pseudo-random noise signal. It then mitigates the repeat-back jamming effect by subtracting the estimated code timing on a normal correlation channel from the estimated value. Through a Monte-Carlo simulation, the proposed scheme can diminish the running average of code tracking bias to less than 10% of the bias using the conventional scheme.