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This paper introduces a real-time video surveillance system which can track people and detect human abnormal behaviors. In the blob detection part, an optical flow algorithm for crowd environment is studied experimentally and a comparison study with respect to traditional subtraction approach is carried out. The different approaches in segmentation and tracking enable the system to track persons when they change movement unpredictably in occlusion. We developed two methods for the human abnormal behavior analysis. The first one employs Principal Component Analysis for feature selection and Support Vector Machine for classification of human behaviors. The proposed feature selection method is based on the border information of four consecutive blobs. The second approach computes optical flow to obtain the velocity of each pixel for determining whether a human behavior is normal or not. Both algorithms are successfully developed in crowded environments to detect the following human abnormal behaviors: (1) Running people in a crowded environment; (2) falling down movement while most are walking or standing; (3) a person carrying an abnormal bar in a square; (4) a person waving hand in the crowd. Experimental results demonstrate these two methods are robust in detecting human abnormal behaviors.

This paper presents a new design for the grippers of Treebot, a novel tree climbing robot that can move from trunks to branches. As trees are unstructured environments, grippers can easily become embedded in the barks. To reduce these occurrences and enhance Treebot's robustness, we designed a retractable spine with increased withdrawal force. Here, we analyze the relationship between the insertion and adhesion forces of the tips using data from a large number of experiments. The magnitude of the withdrawal force of the previous gripper is calculated using kinematic analysis. By comparing the expected and actual forces, we assess the advantages of retractable spine. The results of these structural analyses and simulations demonstrate the feasibility of the new retractable spine.

This paper presents a novel shape representation approach, Finite Element Skeleton (FES), based on studies of planar shape analysis from the perspective of Finite Element Method (FEM). First, we derive the shape contour of an object in image with edge detection approach. Then, the approach builds connections of all pixels on the shape contour with the inner center point of the shape, and models these virtual connections as bendable beam elements. Finally, we obtain a multi-degree-of-freedom (MDOF) model of the original shape. By this model, we can calculate its natural frequency, which is one of mechanical properties that directly related to the geometric shape, and employed it as the shape representation. FES shape descriptor possesses the natural properties on translation and rotation invariant. Especially, FES descriptor has the feature of articulation insensitivity which is the most important property of a shape descriptor to ensure a good performance of the intra-class similarity measurement. Experimental results validated that the proposed FES descriptor is capable of identifying shape for object recognition.

The human behavior, such as gait, is controlled by the brain and other peripheral neural system, and thus is significantly affected by the internal emotion. It is important to carry out the affective sensation of abnormal internal emotion in a non-invasive and non-privacy-infringed way. This paper presents an experiment method and platform to sense the abnormal affective status through gaits. We used a wearable intelligent shoe system for affective sensing, designed an experiment for the human subject to have the normal gait and the gait with emotional disorder, and finally classified the gaits through machine learning.

A transmission from the engine crank to the main transmission with variable reduction ratio is desirable for the hybrid electric vehicles, especially for micro/mild hybrid vehicles with auto start-stop function, for the sake of reducing the design requirements for integrated starter-generator and thus reducing the cost. This paper presents a solution to the issue by introducing a Controller-Free Ratio-Variable gearbox. Unlike other solutions, the proposed gearbox avoids from using actuators to change gear ratios but realize the function in a purely mechanical and fully automatic way. This feature reduces system complexity and improves system stability, as electronic-based info sensing and actuating are not as robust in the engine compartment. Also, the feature allows to change gear ratio faster and thus better protecting integrated starter-generator from over speeding. In this paper, the principle of the gearbox is explained and its performance under different conditions is studied.

This paper presented a novel power transmission lines inspecting robot named “Linebot”, which has high maneuverability and keeps in compact size and lightweight. Linebot has 11 degrees of freedom totally. It equips with two open-close wheels and a pair of grippers, which can be driven separately. As a result, there are three main types of motions to overcome the obstacles on the lines more efficiently. Prototype of Linebot has been developed for further research. It is composed of two parts connected by a pitch joint. Each part contains an open-close assembly, friction adjustment mechanism, grippers, pitch control mechanism, which can be controlled separately. The desired motion can be obtained by appropriate control of the drive cells. The prototype of Linebot had been developed and some experiments had been done to validate the robot's motions.

In this paper, a novel precise pick and place device (PPD) is proposed for robotic manipulators. It is capable of accurately picking the work pieces and placing them when they are deviated from their initial taught picking positions. In addition, a new muti-line identification algorithm is also developed, which is very effective, especially when the obtained data is limited but needs precise identification. Moreover, the design and implementation of PPD are developed, including hardware design, control algorithm, muti-line fitting approach, offsets calculation and compensation of inverse kinematics, etc. The advantages of PPD are low cost, effective algorithm with high accuracy, when compare with vision or other traditional approaches. Numerous pick and place experiments have been conducted and the results are satisfied.

Hand posture is a natural and effective human robot interaction way. In this paper, an user-independent hand posture recognition system using depth and color images captured from an RGB-D camera is presented. To recognize hand posture against complicated background conditions, we propose a novel method for automatic and accurate hand posture segmentation which detects the hand with Chamfer matching, tracks the hand with Kalman filter and segments the hand with region growing algorithm only in the depth space. A new hand posture descriptor invariant to scale, shift and in-plane rotation is constructed with the combination of local contour Fourier descriptor and global Bag-of-Features (BoF) descriptor based on Scale Invariance Feature Transform (SIFT). The sparse representation-based classification (SRC) is applied to perform the hand posture recognition task in the system. Experiments with a self-built large scale hand posture database collected online show the robustness and effectiveness of the proposed system.

This paper1 presents a novel shape representation approach, Finite Element Contour (FEC), based on studies of shape analysis from the perspective of Finite Element Method (FEM). We assume that an edge of a contour can be modeled as a bendable beam element. Linking finite number of beam elements end to end along the contour, we obtain a closed-loop Finite Element Contour model as an approximate physical model of the original shape. By this model, we can calculate its natural frequency, which is one of mechanical properties that directly related to the geometric shape, and employed it as the shape representation. FEC shape feature possesses translation and rotation invariant properties naturally. We also realized scale and unique affine normalization in few simple steps based on intrinsic physical properties of shape from FEM viewpoint. Experimental results validated that the proposed FEC feature is capable of identifying shape in object recognition task. It also can describe shape deformation. In the well-known MPEG 7 shape retrieval task, the enhanced FEC approach obtains Bullseye score 87.11%.

This paper presents a novel shape representation approach based on Finite Element Region (FER). We assume an image blob can be treated as a mesh model that consists of a finite number of deformable quadrilateral pixel elements. These elements connecting side by side form an approximate physical model of the surface of real object. By employing Finite Element Method (FEM), the model's natural frequency can be derived, which is one of mechanical properties that only related to geometric shape, as the shape representation. Experimental results validated the proposed FER descriptor possessing good performance on shape representation. It also has translation, rotation invariant properties naturally, and scale and affine normalization can be realized in few steps. Experiments also demonstrated that the FER descriptor is sensitive to shape deformation. It can precisely describe subtle changing processes between different gestures. This property allows us to establish a conceptual graph model of hand gestures changing to improve the performance of ambiguous gesture recognition.

This paper mainly presents the survey of mobility approaches for the Extra Vehicular Robotics (EVR) application in space station. Seven different kinds of mobility approaches are identified, including straight rail mobile approach, inchworm-like movement approach, free flying vehicle/robot with docking approach, handrail climbing approach, mobile base with circular climbing approach and long manipulator transferring approach. The performance and difference of each approach are elaborated carefully, which offers reference for further research.

This paper presents the optimization for rail-type climbing robot in space application, especially for the rail and mobile base subsystems. Firstly, the rail-type climbing robot is modeled and the evaluation performance indexs are identified. Then five main parameters are modeled and analyzed to find out the contribution function separately. Finally, the system optimization function is built. The output results offer a guideline for space researcher when doing rail-type climbing robot design.

As one of the most unique types of art in Chinese culture, nowadays Chinese calligraphy is attracting increasing interests from researchers. It will be a big step if we have a robot to write Chinese calligraphy, especially in various styles, and it will form a bridge to combine science with art directly. However, there are so many different types and styles in Chinese calligraphy, and to distinguish them is the most basic quality to a green hand, but it is a big challenge for a robot to do so. For the lack of exploration about this, we conduct a lot of experiments to help the robot to accomplish it automatically. We first propose a parametric representation of calligraphic characters, and then adopt the Mahalanobis distance for similarity measurement and classification. The average accuracies of classifying types and styles of the Chinese calligraphy are 96.36% and 95.61% respectively. During the experiments, some interesting phenomena are discovered through similarity measure. Meanwhile, the parametric representation also has some potential applications, such as defining aesthetic grading standards of calligraphy and synthesizing calligraphy. Based on our research, the calligraphy robot can tell which style of calligraphy it sees for mimicking.

In this paper, a traction/braking force distribution algorithm for omni-directional all-wheel-independent-drive vehicles is proposed as a tool to enhance driving stability. In the proposed algorithm, the amount of the traction or braking force on each driving wheel can be determined so as to generate a desired tangential force, yaw moment and centripetal force independently. The algorithm considers omni-directional steering command and is capable of handling both traction and braking force commands. The algorithm is applicable on vehicles with at least three independent driving wheels. Simulations have been conducted to illustrate the use of the proposed force distribution method in enhancing vehicles stability.

The internet and computer are more popular in the world, and the keyboard is the important input for the computer. However, in our society, many disable people cannot type by using keyboard. In order to assist them, we developed a shoe platform which senses the foot motion and use it as the input for the computer. In this system, we used force sensing resistors (FSRs) and a 3-axis accelerometer to detect foot movements. Based on these sensors, we analyzed human foot motions in different positions, and selected those with more comfortable motions when people sitting in front of the computer with a healthy attitude. To reduce the computational cost and power consumption, and enhance the real-time performance, we use the principle component analysis (PCA) for sensor reduction. Through PCA, we selected some important FSRs in this system. Also we compared two classification methods, chose two layer back propagation(BP) networks, to detect the foot movements for typing.

With the increase of vehicle amount all over the world, vehicle safety has become a tremendous problem. Chained accidents, which result in numerous losses of lives and properties, happen due to the fact that the following drivers are unaware of the accident occurrence. In this paper, a real-time vehicle safety system is proposed. A prototype test platform, consisting of collision detection and classification module, vehicle-to-vehicle (V2V) communication module and graphic user interface (GUI) module, has been built for validating the performance. 12 sensors are installed in collision detection module. Threshold under rule based strategy is adopted for collision classification. We also propose a GPS based AODV (GBAODV) protocol for V2V communication application. One full version and eight sets of portable version subsystems are implemented for road test. The experiment results validate the performance of this safety vehicle system.

This paper proposed a novel Tri-star wheeled robot called “Tribot”, which targets on high obstacle performance in unstructured environments, especially at the performance for climbing vertical obstacles. Tribot equips with six Tri-star wheels and each wheel can be driven independently. The chassis of the Tribot is divided into two parts which are connected by an articulated mechanism, making the Tribot has a remarkable obstacle performance to adapt changing environments mechanically, without any interpolate complex control. Numerous experiments have been conducted for vertical obstacle performance tests. Although the diameter of the wheel of the Tribot is only 220 mm, the robot can climb over vertical obstacle of 450 mm high, twice more of the wheel diameter. All results show that Tribot has excellent vertical climbing performance in unstructured environments.

Collision avoidance of industrial robot arms in varying environment is a challenging task which has been a tough problem for decades. It often requires a large number of sensors and high computational power. Moreover, since the sensors are often mounted on the surface of robot arms, they may affect the appearance of the robot arms and may be vulnerable to damage. This video presents a cost-effective invisible sensitive skin that can cover a large area without utilizing a large number of sensors and it is built inside the robot arm. By using only 5 contactless capacitive sensors and specially designed antennas, collision avoidance of a 6-DOF industrial robot arm is attained.

This paper presents a novel climbing robot called Clothbot which has high maneuverability on flexible clothes. It has a novel gripper consisting of two parallel wheels that can grip continuously and stably on various kinds of clothes. Clothbot also has an omni-directional tail of two DOFs so that it can change its center of gravity to control the moving direction on complex and undeterminate clothes. Consequently, Clothbot is able to access most positions of the clothes by moving straight and turning around with only four motors. It is compact, small and light-weighted but has a load capacity six times its own weight. A series of experiments validate its high performance on flexible clothes.

In this paper, a deterministic yaw moment controller for four wheel independent steering and drive vehicles is proposed to enhance driving stability and controllability. Different to conventional methods that track a desired yaw rate, the proposed controller stabilizes a vehicle by additionally tracking the heading angle of a vehicle which is more efficient and robust. The heading angle of a vehicle is obtained by a novel method which is based on centripetal force detection. It eliminates the prerequisite knowledge of the characteristics between wheels and road surface which are time varying and difficult to be measured in real time. The proposed system only requires low cost sensing equipment such as wheel speed sensor and accelerometer that makes the system practical to be utilized. The proposed heading angle detection method can be generally applied to any kind of vehicle. The deterministic yaw moment controller is also applicable to any type of four wheel independent drive vehicles.

Arc tracking is a significant function in welding robot applications. In this paper, we present an eight-axis robot arm system, and propose an arc tracking on this system, including signal collection, process, and offset derive. Moreover, we establish a kinematics modeling of sine weaving, and offset compensate models, which can compensate actual offsets to the welding process, and experiments show that they can be satisfied to the industrial needs.

In this paper, a new Cartesian gas cutting robot was designed, which had five servo motors and six stepped motors. Also, we developed a three-torch model and studied algorithm and boundary conditions that were pretty available for three-torch device and experiment verified this theory. A laser height control system was also developed in this robot. And the experiments certified that the model was effective and could be satisfy with the actual industrial needs. Meanwhile, the laser height control system could realize height tracking when the surface of the steel was not flat.

Vehicle parking in crowded parking lots has become an un-ignorable problem in urban areas. Traditional manmaneuvering parking structures has low space efficiency, and in contrary, the current fully automatic parking frame system has high cost and difficulty in processing the vehicles in parallel. Inspired by the wheeled mobile robot, the paper investigates the relationship between the space efficiency (the ratio of space for parking over the total space for parking and traveling) and the structures of four-wheel robots, both the classical car-like robot and the omni-directional robot, which can be the platforms for vehicle carriers for parking in parallel to reduce the total time requirement and increase the flexibility of parking systems. An omni-directional driving and 4 wheel independent steering (4WIS) platform is designed and developed to demonstrates its advantage of parking in narrow space. For the type of carlike robot, space efficiency is optimized with respect to the rear overhang and the capacity of the parking lot. Omni-directional structure, in contrary, still has a higher space efficiency, and thus is more ideal for the parking platform.

In this paper, we introduce a data gathering algorithm for mobile sink in WSN system, which is based on the swarm intelligence and load balancing strategy, called SIDG-MS algorithm. The preying behaviour from swarm intelligence shows that individual could find food in swarm territorial. Each node in WSN system has limited calculation ability, memory space, energy and wireless transmission range. This action is similar with the individual of swarm intelligence. This paper focuses on the concept and elaboration of the SIDG-MS and the load balancing strategy. Experimental results validate the effectiveness of this approach. We analysis and demonstrate the effectiveness of the proposed methods over other existed methods, and defined some future work.

With the acquisition of an image, the first step is to distinguish objects of interest from the background. In surveillance applications, those objects of interest are usually humans. Their various shapes and different motions, including walking, jumping, bending down, and so forth, represent significant challenges in the extraction of foreground pixels from the image.

An important feature of crowd surveillance is the estimation of the number of persons in the monitored scene. People counting is especially important for crowd modeling as it not only gives a direct estimation of the current situation in the field of view but also contributes to other tasks including behavior analysis and crowd simulation.

In the surveillance of public areas, human groups should be the major targets monitored because of their highly sociable character. In groups, the behavior of individuals usually depends on that of others. Hence, group behavior can explain a large number of phenomena, including overcrowding, chaos, stampedes (as a result of a terrorist attack such as a bomb explosion), fights, and riots.

In many cases, we cannot obtain the background image easily, and if the occlusion is serious, then it is difficult to segment masses of blobs into single blobs.We therefore use optical flow to detect abnormal behavior in crowded environments [57], [58].

After successful segmentation and tracking, the temporal-spatial information of each blob sequence can be indexed for further behavior analysis. In this chapter, we elaborate two kinds of approaches for the behavior analysis of individuals: learningbased and rule-based.

After foreground extraction, if nonsignificant merging occurs among people, then segmentation is necessary, and the merged blobs can be split into separate ones equal in number to the number of people in the merged blob. Thereafter, all of the blobs can be indexed and tracked for motion and behavior analysis.

In the previous chapter, we presented behavior analysis, which is based on dynamic information, including background extraction based on a series of frames, blob detection based on two consecutive frames, and so forth. However, for intelligent surveillance, we can also explore static information, i.e., facial images. Facial detection is straightforward as it does not depend on whether or not background information is extractable. Hence, it is useful in highly cluttered environments, with the only requirement being sufficient image resolution of the image.

State of Charge (SoC) estimation is one of the most significant and difficult techniques to promote the commercialization of electric vehicles (EVs). Suffering from various interference in vehicle driving environment and model uncertainties due to the strong time-variant property and inconsistency of batteries, the existing typical SoC estimators such as coulomb counting and extended Kalman filter cannot perform their theoretically optimal efficacy in practical applications. Aiming at enhancing the robustness of SoC estimation and improving accuracy under the real driving conditions with noises and uncertainties, this paper proposes a framework consisting of (1) an adaptive-κ nonlinear diffusion filter to reduce the noise in current measurement, (2) a self-learning strategy to estimate and remove the zero-drift, (3) a coulomb counting algorithm to realize open-loop SoC estimation, (4) an H ∞ filter to implement closed-loop robust estimation, and (5) a data fusion unite to achieve the final estimation by integrating the advantages of the two SoC estimators. The availability and efficacy of each component have been demonstrated based on comparative studiesin simulation with the conventional approaches respectively, under the testing conditions of noises with various signal-noise-ratios, varying zero-drifts, and different model errors. The overall framework has also been verified to rationally and efficiently combine these components and achieve robust estimation results in the presence of kinds of noises and uncertainties.

Battery equalization, aiming at keeping the state of charge of inside cells in the same level, is of great importance to maximize the capacity of whole battery pack and keep cells away from overcharge and overdischarge damage. In this paper, based on the analysis of bi-directional Cuk converter, we have proposed a fuzzy controller to adaptively tune the equalizing current. The inputs of fuzzy controller are selected as the difference in state of charge, the average of state of charge and the total internal resistance. The overall performance of the proposed equalizer is evaluated by multi-indexes such as equalizing speed, efficiency and cell protection. Simulations are conducted based on a well established 6Ah Li-ion battery provided in Advisor. The results under various initial conditions show that the proposed equalizer has the ability to balance the equalizing speed and efficiency. Any pair of cells with difference in state of charge less than 0.3 can be equalized within one hour and with the energy efficiency around 0.95.

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.

In a freeway vehicular ad hoc network (VANET) environment, an efficient ad hoc routing protocol plays a very important role to enhance the safety of passengers. This paper presents the latest result of simulation model of three mobile ad hoc routing protocols: destination-sequenced distance-vector protocol (DSDV), dynamic source routing (DSR) and ad hoc on-demand distance vector (AODV). The performance of three protocols under constant bit rate traffic is evaluated and compared with five quality indices. The simulation results show that both the reactive routing protocols, DSR and AODV, have better performance than the proactive routing protocol DSDV. DSDV has largest packet loss rate and smallest system throughput among the three protocols. This paper also shows that the effect of varying mobility has undeniably influenced the performance of both reactive routing protocols. DSR is better in the protocol overhead than AODV. However, there is critical jitter variation in the DSR. As a whole, AODV is more appropriate than DSR for the freeway VANET.

To manage the drivestrain and power flow of hybrid electric vehicle (HEV) and achieve balanced performances on fuel economy, emissions, grade ability and acceleration ability, it is necessary to develop a control system with suitable parameters by solving a multi-objective optimization problem. Based on the proof of the relationship between dominating number and diversity in objective-space, a dominating number based multi-objective genetic algorithm is proposed to enhance the diversity of Pareto-front and has been applied to optimize parameters of electric assist control strategy for parallel HEV under standard test procedure provided in Advisor. Simulations under four drive cycles demonstrate availability and efficacy of the proposed algorithm.

This paper proposed a behavior-based steering controller for four wheel independent steering vehicle. The proposed controller acts as virtual linkages among each wheel to minimize wheel slip resulted by the misalignment of the orientations of wheels effectively. As there is no trajectory planning needed in the control algorithm, the proposed controller is especially suitable for non-autonomous vehicle which the driving path cannot be pre-determined. Numerical simulations to examine the performance of the proposed controller are implemented. Results show the effectiveness and robustness of the proposed behavior-based controller.

In this paper, an integrated video surveillance system for robust tracking is introduced. In the blob detection part, an optical flow algorithm for crowded environment is studied experimentally and a comparison study with respect to traditional subtraction approach is carried out. In the segmentation part, different algorithms are fused to develop a hybrid algorithm for stable segmentation, and validation rules for successful segmentation are also presented preventing from false segmentation. In the tracking part, a blob's parameter, which we call color spectrum, is developed to identify different persons and track them robustly. A hybrid algorithm for tracking is also developed to combine color tracking with traditional distance tracking. The hybrid algorithms in segmentation and tracking enable the system to track persons when they change movement unpredictably in occlusion. Experimental results validate the proposed algorithm.

This paper presents a novel vehicle named omni-directional Kart (OK-1), in which robotic technologies, such as 4 wheel independent driving (4WID) and 4 wheel independent steering (4WIS), are integrated to decouple the motions of translation and rotation from each other. As a result, omni-directional motions such as zero radius turning (ZRT) and lateral parking (LP) are realized for better vehicle steerability and mobility in limited space.

This paper presents the progress toward a face detection and race classification system that is robust and works in real-time. We address the race classification problem as classifying a frontal face into Asian or non-Asian. Firstly, we propose principal component analysis (PCA) for feature generation and independent component analysis (ICA) for feature extraction. Then, we use SVM for training process and combine different SVM classifiers to some new classifiers, which improve the classification rate to a new level. Experiments show that our system achieves a classification rate of 82.5 % based on a database containing 750 face images from FERET.

This paper introduces a real-time video surveillance system which detects human abnormal behaviors. We present two approaches to such a problem. The first one employs principal component analysis for feature selection and support vector machine for classification of human behaviors. The proposed feature selection method is based on the border information of four consecutive blobs. The second approach computes optical flow to obtain the velocity of each pixel for determining whether a human behavior is normal or not. Both algorithms are successfully implemented in crowded environments for detecting the human abnormal behaviors, such as (1) running people in a crowded environment, (2) bending down movement while most are walking or standing, (3) a person carrying a long bar and (4) a person waving hand in the crowd. Experimental results demonstrate the two methods proposed are robust and efficient in detecting human abnormal behaviors.