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This paper presents the gait design and comparison study of a quadruped robot. The main contributions of this paper are: (i) Explore the modularized system design of an 8 degree-of-freedom quadruped robot with a power management system. (ii) Propose a gait design method based on drive function. (iii) Propose another gait design method based on foot trajectory. (iv) The simulation of the walk and trot gaits with above-mentioned methods are validated in Webots. (v) The walk and trot gait are successfully implemented in the real robot. Experimental results show that the stable locomotion can be achieved with improved elliptical foot trajectory.

Currently, the shock absorption system for outdoor AGV is mainly built with identical-spring solution, in which all the damping springs have the identical elastic coefficient. In this paper, we introduce a method to design the shock absorption system of the AGV using dual-spring solution. Springs of two different elastic coefficients are used, and an enhanced performance in obstacle-crossing capability can be achieved. The proposed dual-spring solution design method takes several critical outdoor working requirements as design constraints, and integrates damping system analysis to determine the optimal coefficients for the dual-spring system. To validate the effectiveness of this dual-spring design method, theoretical analysis is fully derived and a AGV chassis is physically built using dual-spring solution and is subjected to a series of validation tests.

In this paper, we propose a novel design of gaits for space manipulators so as to minimize the dynamic disturbance on a space station. Without modifying their joint configuration, we first propose the use of our previous developed gripping methodology. It combines the concepts of wheels motion in parallel grippers to reduce the swing motions in conventional gaits. For the proposed gaits, three major aspects are considered including 1) linear motion; 2) turning; and 3) exterior transition. Based on the conventional modeling approach for fixed base space manipulators, we extend the method to formulate the dynamic coupling during wheels motion. It is to visualize the manipulator as a prismatic joint with its gripping base moving with wheels motion. Also, we develop an experimental platform for verification. For disturbance analysis, various simulations have been conducted. Results have validated that the proposed gaits generate the lowest disturbance on a space station.

Ocean exploration has attracted enormous interest from human-kind for thousands of years. The sailing boat, propelled by wind is a historical invention. But human-involved marine exploration has high risk and low efficiency. This paper presents a new autonomous surface vehicle (ASV) based on retrofitting a trimaran sailing boat. The previous human-based maneuvering interfaces, i.e. sail and rudder control, are motorized by three electric actuators. The ASV acquires signals of wind speed and direction, GPS position, and rolling angle, determine the heading based on various wind direction. Experiments validates the effectiveness of the ASV.

Monocular cameras are not generally considered as a 3D measurement tool in the field of intelligent transportations. This belief limits the application of monocular cameras in many applications such as guiding robots or vehicles for autonomous driving. In this paper, we demonstrate that with the reference of a license plate, a monocular camera can be used as a tool to measure 3 dimensional parameters (width, height and length) of a passenger vehicle down to 10 cm accuracy. The proposed measurement method does not reply on any training process, but is designed as a fast and direct approach utilizing a series of fundamental computer vision techniques such as ROI extraction, edge/line detection, space coordinates transformations, etc. A series of validation tests were conducted and the results demonstrated the robustness of this measurement method and implies the possibility to broaden the application scope of monocular cameras in the field of autonomous driving and intelligent transportation systems.

Space robots are in huge demand due to the rapid growth of their service targets, i.e., spacecraft. There are generally large flexible components on spacecraft, such as antenna reflectors and solar paddles. Due to the vibratility of their structure, it is challenging for a space robot with flexible appendages (the base is then called flexible-base) to capture and repair the large flexible spacecraft. After capturing, the two spacecraft with flexible appendages are connected by a space manipulator, and a compounded system is formed. In this paper, we developed a dynamic model and a closed-loop simulation system, to provide a means to verify path planning and control algorithms. Initially, the dynamic characteristics of different capturing stages (preimpact and post-impact) were analyzed. The topologies of a flexible-base space robot and the compounded system were described based on incidence and channel matrices. Secondly, the recursive dynamics was formulated and resolved by an effective numerical method. The modeling was verified by Adams’ model. Thirdly, we implemented a dynamics calculation block in Matlab/Simulink environment using the S-function package for the C program, and developed a closed-loop simulation system, which was composed of the Planning and Controller, the Multibody Dynamic, and the 3D Display modules. Finally, based on the simulation system, two typical missions—target berthing and on-orbital manipulation of the target along a circle, were simulated and evaluated. Dynamics analysis results presented some useful rules for the path planning and control to suppress the vibration of the flexible structure.

In this paper, a fast charging system (FCS) is designed for service robots. In this design, batteries are used to due to its energy density, whereas supercapacitors (SCs) are used to enhance the power density. DC-DC converter controls the FCS. The main advantage of the design of fast charging system is that, the SCs are charged with a large current fast and then discharge to the batteries with small current slowly. This feature is beneficial for batteries' long-life and store more energy for service robots which have been in great demand for multiple purposes, such as elderly care, child education etc. Some experimental and testing results are presented to validate the design of FCS for service robots.

Battery fast charging is one of the most significant and difficult techniques affecting the commercialization of electric vehicles (EVs). In this paper, we propose a fast charge framework based on model predictive control, with the aim of simultaneously reducing the charge duration, which represents the out-of-service time of vehicles, and the increase in temperature, which represents safety and energy efficiency during the charge process. The RC model is employed to predict the future State of Charge (SOC). A single mode lumped-parameter thermal model and a neural network trained by real experimental data are also applied to predict the future temperature in simulations and experiments respectively. A genetic algorithm is then applied to find the best charge sequence under a specified fitness function, which consists of two objectives: minimizing the charging duration and minimizing the increase in temperature. Both simulation and experiment demonstrate that the Pareto front of the proposed method dominates that of the most popular constant current constant voltage (CCCV) charge method.

This paper presents an omnidirectional steer-by-wire system for a four-wheel independent steering vehicle. The system consists of an extended steering interface and a behavior-based steering controller. The extended steering interface provides a novel manipulation way to vehicle driver. The driver can control the vehicle in traditional way or omnidirectionally without any mode switching operation. The reservation of the traditional driving way makes the driver adapt the novel steering interface easily. The steering interface also has a force-feedback function to synchronize the extended steering interface and the orientations of wheels so as to improve vehicle handling. The behavior-based steering controller is designed for controlling the orientations of wheels. It acts as virtual linkage among each wheel to minimize wheel slip resulted by the misalignment of wheels. As there is no trajectory planning needed in the control algorithm, the proposed controller is especially suitable for a nonautonomous vehicle, the driving path of which cannot be predetermined.

In this paper, we discuss the problem of how human control strategy can be represented as a parametric model using a support vector machine (SVM) and how an SVM-based controller can be used in controlling dynamically stable systems. The SVM approach has been implemented in the balance control of Gyrover, which is a dynamically stable, statically unstable, single-wheel mobile robot. The experimental results that compare SVM with general artificial neural network approaches clearly demonstrate the superiority of the SVM approach with regard to human control strategy learning.

With the emerging need for household applications, service robotics has become a hot spot attracting researchers globally. Universities, institutes, and industrial companies, either independently or collaboratively, have been focusing on research and development for household service robots, resulting in interesting and well-performing household service robotic systems. In this chapter, we first give a categorization of the various applications, followed by an elaboration of some highlighted cases with their overall system designs.

Human–robot interaction (HRI) studies on how humans collaborate and interact with robots in an effective and natural way have received considerable attention in the robot research community, especially with regard to the design and development of household service robots, owing to their highly interactive characteristics in domestic service activities. Many researchers have proposed a variety of principles, algorithms, and systems to achieve an effective and natural HRI in the domestic environment. In this chapter, we first give a brief illustration of the state of the art for HRI, divided into several categories, and then elaborate on some selected cases with regard to the design and implementation of HRI systems.

Robotic grasping and manipulation comprise three subtasks, i.e., target detection, planning, and control. The robot first needs to detect the target. Here “detect” means to find (or recognize) the target and locate it. The target could be the object to be manipulated (e.g., electrical plugs) or the destination of the object (electrical sockets). Then the robot approaches the target in an optimized trajectory. In the process of approaching and grasping, the robot should be controlled to perform the task correctly and smoothly. In this chapter, we review the approaches to accomplish these three subtasks, i.e., target detection, planning, and control.

With the significant development of robotic technologies, the applications of robots have been extended broadly from their traditional industry role to military, medical field, and even daily services. Robotics has gradually become a very large industry with tremendous influence on the sustainable development of the world.

To provide a high starting torque and to work under a high rpm range are the design requirements for the integrated startgenerators (ISG) used for intelligent vehicles with hybrid powertrains. However, meeting with both of these two requirements usually involves with sophisticated motor and controller design and high manufacturing cost. Hence, a transmission with variable reduction ratio is desired to be configured between the ISG and the engine. In an earlier paper, a Controller-Free Ratio-Variable gearbox design concept has been introduced, which can auto change gear ratios in a purely mechanical way and avoid from using any controller or actuators. This paper represents the work on implementation, testing and evaluation of the gearbox. The performance of the gearbox proof to be functional though the test result.

Electronic devices (PCs, smart phones, etc.) are ubiquitous in the world and the convenience in users' input has become increasingly important for their extensive applications. Disabled armless people are also in need of a novel input interface for a better life. In this paper, we present our research on a wearable sensor-integrated shoe system as a writing platform, by sensing and analyzing the foot movement, so it enables people to write by foot. Also using this system, people can exercise their lower limbs and related brain parts for recovery from illness, e.g. stroke. We use the force sensors (FSRs) to localize the foot when it attacks ground, i.e. touching ground from air. This system classifies FSRs data by BP neural network to obtain the start point for each basic stroke. Also the force sensor can classify the location of stroke starting point from another aspect. Finally, low cost optical tracking sensor is used to draw the stroke. Experiments validate the design of the shoe writing system.

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 Differential Modular Robot Joint (DMRJ), which has high payload capability and low weight. The ratio between the output torque and the reducer's weight is 178.3 Nm/kg, which is the highest value we have known. The DMRJ is designed modularly for high payload tasks and can be attached to robots as arms or legs. Two high power DC brush motors are applied to drive the differential mechanism, which can rotate in two degrees of freedom (DOF) and has a minimal size. A control algorithm for the DMRJ has been devised in accordance with the kinematic and mechanical analysis. A prototype of the DMRJ has been manufactured, and the effectiveness of the control algorithm has been verified by several experiments. Thanks to its excellent performance, the DMRJ can be widely utilized in many robotic applications.

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.

This paper proposed a biologically inpired handrail climbing robot "MonkeyBot" that could provide inspection and maintenance for the space station. Space debris is the biggest hazards for space station from the outer space. When a threat from orbital derbis is idenfitied a bit late, the station will be potentially damaged by the debris. MonkeyBot is designed to meet this challenge, especially for some key components or areas on the space station. MonkeyBot is a redundancy robot with four limbs, which not only has multi climbing strategies and gaits to achieve a full coverage of the space module, but also it has dexterous capability for on-orbit servicing with each limb. This paper will focus on the geometry design, mathematical modeling, workspace and dexterity analysis and climbing strategies and climbing gaits evaluation. The analysis result shows that the maneuverability surpasses the state of art space station exterior robot.

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.

In this paper, a simple redundant space robot is proposed to assist astronauts and execute extra-vehicular activities (EVA) on a space station. Different from conventional space station robots, proposed robot has good mobility and high coverage in space station modules. The improvement is mainly contributed by a novel rail and robot base. The rail is designed with a circular shape so it can cover most exterior of the space station modules. Robot arm is designed in high accuracy, 6 meter long and 7 degree of freedom with redundant and modular joints. It is also designed in symmetrical configuration to have walking capability among the space station modules. This paper discusses the robot design and tasks simulation.

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.

People usually need carrying load in the daily life, especially teenagers, who need carrying school bag everyday. In this research, we target to find which approach to carry load is the best. Based on this target, we design an intelligent shoe system with sensors in high pressure area on the insole, 3-axis acceleration and 3-axis angular acceleration in the front of shoe. We proposed a new method to process the data, which is using the re-sampling to partition data cycle by cycle. Using Support Vector Machine (SVM), we build the model by normal walking gait and abnormal gaits without load. We tested six approach to carry load and found that the carrying to balanced load carrying approach is better than unbalanced.

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.

Injury duo to falling has accounted for a significant portion of accident. In order to provide prompt first-aid service to the victims, automatic and wearable devices are necessary to report the accident as soon as it occurs. This paper presents an intelligent shoe system which can not only detect the fall, but also classify the fall direction, especially the serious backward fall. In the prototype, eight pairs of force sensing resistors (FSRs) acquire the forces in different location of the insole. To reduce the computational cost and power consumption, and enhance the real-time performance, we propose an approach to reduce the sensor number based on principle component analysis (PCA), and lighten the system into a four-pair version. By means of artificial neural network (ANN), we classify the system input into three observations, and develop a finite state machine to trigger correct alarm and prevent false alarm by other complex human actions. To overcome the problem in shortage of learning data to detect the falling direction, nearest neighbor approach is utilized, which learns the necessary pattern from abundant tilted standing data. The experiment validates system and approaches to detect fall and fall direction.

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 study the stability region for a set of intelligent controllers developed by learning human expert control skills using support vector machines (SVMs). Based on the discrete-time system Lyapunov theory, a Chebychev points based estimation approach is proposed to evaluate the stability region, a key property of this set of SVM-based human learning controllers. One of such learning controllers has been implemented in vertical balance control of a dynamically stable, statically unstable single wheel mobile robot - Gyrover. The experimental results validate the proposed scheme for estimation of the stability region.

The promising electric vehicle (EV) technology is a direction to tackle the global non-renewable energy problem. However, the efficiency to use the electric energy still needs deliberate research. Traditional EV has no choice to manage its energy flow, because it has only one traction motor. With the robotic research in 4 wheel independent drive (4WID), the driving task of the single traction motor can be shared by 4 independent in-wheel motors. By exploring the motor efficiency map, we propose the energy management strategy based on optimal driving torque distribution (ODTD). The total input power of the 4 motors can be minimized while the driving performance is still maintained, and electric energy consumption can be reduced compared with traditional single motor driving EV. Simulation results validate the proposed strategy. The energy management strategy can also be applied to multi-driving-wheel mobile robots.

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 fast charging is a crucial issue in both research and application to realize and promote the mass commercialization of electric vehicles, especially pure electric vehicles. However, due to the strong nonlinear properties of batteries, the charging process should take into consideration various factors such as state of charge (SoC), temperature, and charging current, so as to assure the safety, reduce charging time, and enhance charging efficiency. In this paper, we propose a fast charging strategy under the model predictive control framework. Two models are employed to predict SoC and temperature under a sequence of future charging currents. SoC predictor is based on RC equivalent circuit and temperature predictor is based on thermal conduction and convection. The prediction of battery future states allows optimization of the control sequence, with the objectives to follow a predetermined SoC trajectory and to minimize battery temperature rising. Genetic algorithm are introduced to solve the constrained multi-objective optimization problem. The results using Advisor platform demonstrate the availability and efficacy of the proposed framework and prove that it has the ability to reduce charging time and heat generation simultaneously.

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.

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.

In this paper, an omni-directional steer-by-wire interface for four wheel independent steering vehicle is presented. The proposed steering interface is an extension of a traditional steering interface that provides three steering inputs. By combination of which, driver can control the vehicle in traditional way or omni-directionally without any mode switching operation. The reservation of the conventional steering behavior makes driver easy to adapt the novel steering interface. The force feedback controller is designed to synchronize the extended steering interface and the orientations of wheels so as to improve vehicle handling. Hardware-in-the-loop simulations are conducted to verify the hardware prototype and examine the proposed algorithms.

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.

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 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.

This paper presents a new Automatic Guided Vehicle (AGV) system, in which unique compact and heavy-payload under-cart design, high precision hybrid-mode stopping, modularized software and distributed AGV-caller system, are integrated to provide a reliable and worker-friendly AGV system for the flexible production Une. Besides, the results of a series of in-field tests validate the payload and precise stopping performance of our AGV system.