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This paper provides a new scheme to improve the sailing distance and working time of the autonomous sailboat. The main part of the article introduces a new design of sail tug which can carry more batteries that provides more power for control system, enabling sailboats to sail longer in both time and distance. The modified sail tug was tested at an advanced test site equipped with high precision infrared motion capture system. The air curtain machines in test site can produce constant wind field, to drive sailboat steadily. Comparative experiments have been conducted to show the advantages of self-supporting ability of autonomous sail tug over ordinary autonomous sailboat.

Wing sail land-yacht is one of the promising vehicles which utilizes wind energy. However, its autonomous driving in all directions is challenging, especially the upwind steering (tacking) maneuver. This paper proposes a novel design of low cost three-wheeled land-yacht with a T-frame and a foamed wing sail, which is able to move and tack upwind owing to the lightweight design. To achieve the successful tacking, a model describing the steering process is developed based on the law of energy conservation and used to predict the minimum initial velocity for steering. In the model, an acceleration error function C induced by the environment disturbance and manufacturing error is identified by a series of experiments. Finally, the tacking experiments verify the steering model and show that the land-yacht can achieve the tacking with a high success rate of 94.7%, based on the predicted minimum initial steering velocity.

This paper proposes an interactive system for the non-musician learners to get inspired from a song. Differing from complex models of deep learning or simple Markov models sparse of music inter-features, in this research, we unify the composing of a song in a general architecture with music theory, and thus provide a much more understandable view of the music generation for non-musician learners. The proposed model focuses on extracting the extant feature from a target song and recreating different phrases with the representing probabilistic graph underlying the target song based on the relationship among notes in a phrase. Furthermore, an interactive interface between the users and the proposed system is built with a tunable parameter for them to be involved in the music generation and creating procedure. This procedure provides practical experience in aiding the non-musicians to understand and learn from composing a song. Approximately 700 samples of preferences questionnaire survey about the generated music and original music and more than 3000 samples for interactive preferences voting for the tunable parameter have been collected. Quantities of experiments have proved the validation of the proposed system.

Self-assembly has attracted growing interests in modular robotics during past decades. Recent work accelerates the assembly process by parallelizing the docking actions among robots. However, these methods can only apply to ideal environments without obstacles. Otherwise, robots will get trapped during the assembly process, due to the complex scenes with obstacles. This paper presents an efficient parallel assembly planning algorithm for modular robots by taking the surrounding obstacles into consideration. By this algorithm, the docking actions are able to avoid immovable obstacles, and therefore parallel self-assembly of robots can adapt to complex environments. To validate the efficacy and generality, the authors have implemented this algorithm in a grid-world simulation environment with 25 distinct maps. The simulation results show a much higher success rate (more than 80%) of our proposed algorithm compared with the existing parallel self-assembly planning algorithms. Finally, the feasibility of the algorithm is affirmed by a self-assembly experiment on the automated guided vehicles (AGVs).

The new era with prosperous artificial intelligence (AI) and robotics technology is reshaping the materials discovery process in a more radical fashion. Here we present authentic intelligent robotics for chemistry (AIR-Chem), integrated with technological innovations in the AI and robotics fields, functionalized with modules including gradient descent-based optimization frameworks, multiple external field modulations, a real-time computer vision (CV) system, and automated guided vehicle (AGV) parts. AIR-Chem is portable and remotely controllable by cloud computing. AIR-Chem can learn the parametric procedures for given targets and carry on laboratory operations in standalone mode, with high reproducibility, precision, and availability for knowledge regeneration. Moreover, an improved nucleation theory of size focusing on inorganic perovskite quantum dots (IPQDs) is theoretically proposed and experimentally testified to by AIR-Chem. This work aims to boost the process of an unmanned chemistry laboratory from the synthesis of chemical materials to the analysis of physical chemical properties, and it provides a vivid demonstration for future chemistry reshaped by AI and robotics technology.

This paper proposes a novel modular selfreconfigurable robot (MSRR) "FreeBOT", which can be connected freely at any point on other robots. FreeBOT is mainly composed of two parts: a spherical ferromagnetic shell and an internal magnet. The connection between the modules is genderless and instant, since the internal magnet can freely attract other FreeBOT spherical ferromagnetic shells, and not need to be precisely aligned with the specified connector. This connection method has fewer physical constraints, so the FreeBOT system can be extended to more configurations to meet more functional requirements. FreeBOT can accomplish multiple tasks although it only has two motors: module independent movement, connector management and system reconfiguration. FreeBOT can move independently on the plane, and even climb on ferromagnetic walls; a group of FreeBOTs can traverse complex terrain. Numerous experiments have been conducted to test its function, which shows that the FreeBOT system has great potential to realize a freeform robotic system.

This paper introduces an obstacle-crossing strategy, and the self-reconfiguration algorithm for a new class of modular robots called the rolling sphere, which can fit obstacles represented by cubes of different sizes due to the chain connection of multiple spheres. For the self-reconfiguration of the rolling spheres, a large gradient is obtained by classifying its action types and hierarchically minimizing the distance between the initial configuration and the final configuration. The most direct use of this large gradient is the fast crossing of various obstacles, by jointing multiple self-reconfigurations according to the OctoMap of the obstacles. It is verified in simulation that the self-reconfiguration takes full advantage of the parallel movement of multiple modules to reduce the total time steps, and the obstacle-crossing strategy can adapt to a variety of obstacles.

This paper presents a novel Cable Climbing Robot CCRobot-III, which is the third version designed for bridge cable inspection tasks, aiming at surpassing previous versions in terms of climbing speed and payload capacity. Benefiting from Split-type Wire-driven design, CCRobot-III can climb along a 90-110mm diameter bridge cable in inchworm-like gait at a speed of up to 12m/min, and carrying more than 40kg payload at the same time. CCRobot-III consists of a climbing precursor and a main-body frame. The two parts are connected and driven by steel wires. The climbing precursor, acting as a mobile anchor, moves quickly on a bridge cable. The mainbody frame, acting as a mobile winch, carries payload and pulls itself to a certain position with steel wires. Both parts have one or two pairs of palm-based gripper, which is the key component for providing strong adhesion to support the robot climbing. Experimental results have shown that CCRobotIII possesses outstanding climbing performance, high payload capacity, and good adaptability to complex conditions of cable surface. Moreover, it has potential engineering applications on the cable-stayed bridge for fieldwork.

Traditional solar trackers often adopt motors to automatically adjust the attitude of the solar panels towards the sun for maximum power efficiency. In this paper, a novel design of solar tracker for the ocean environment is introduced. Utilizing the fluctuations due to the waves, electromagnetic brakes are utilized instead of motors to adjust the attitude of the solar panels. Compared with the traditional solar trackers, the proposed one is simpler in hardware while the harvesting efficiency is similar. The desired attitude is calculated out of the local location and time. Then based on the dynamic model of the system, the angular acceleration of the solar panels is estimated and a control algorithm is proposed to decide the release and lock states of the brakes. In such a manner, the adjustment of the attitude of the solar panels can be achieved by using two brakes only. Experiments are conducted to validate the acceleration estimator and the dynamic model. At last, the feasibility of the proposed solar tracker is tested on the real water surface. The results show that the system is able to adjust 40° in two dimensions within 28 seconds.

Wind-driven vehicle is a kind of promising way to utilize wind energy due to its non-pollution feature. This paper presents a practical design of the wind-driven vehicle in the form of land-yacht, which is equipped with a wing sail and a micro-controller unit. Theoretical analysis and simulation were performed to find the total force coefficient corresponding to each combination of attack angle and heading angle, and the best attack angle corresponding to each different heading angle. A prototype was built and tested in a wind-field environment. The simulation and experiment were concordant, both of which showed the relation between the movement of land-yacht and these angles when moving upwind well.

The commercial solar energy market is dominated by non-concentrating photovoltaics and concentrating solar thermal systems. With the development of high efficiency thermoelectric materials, the solar thermoelectric generator is becoming to be a competitive alternative solar energy technology in certain applications. This paper proposes a simulation model and a design methodology for solar thermoelectric generator. Geometry parameters, namely the height, the fill-ratio, the ratio of the cross-section area of n-type material over p-type material of thermoelectric module, solar concentration ratio, were analyzed under different operating conditions. In order to perform the analysis, an L27 (35) orthogonal array was employed to assess all of the design parameters returning the maximum output power. By the analysis of variance, the effect of each design parameter on the output power performance and the interaction between design parameters are identified. The optimal design parameter set is also obtained and it is found that the optimal design performs can potentially achieve 5.47 W output power compared with the output power of 1.95 W from the original design in most operating conditions.

This paper describes a low-cost, wide-range and multi-functional vision backend for Sailboat Research Testbed (SRT). SRTs are of great significance in autonomous sailing vessels (ASVs) research. The motivation behind this vision backend is the demand for various functions of an SRT, such as sailboat localization and control, yet the cost of building such a testbed should be economic. Hence, cost-effective hardware along with robust and lightweight vision algorithms are integrated into a vision backend to assist our SRT. This vision backend overcomes several inherent problems when applying some CV algorithms in the water environment, and presents multiple functionalities with high performance. This paper details our solutions and design of this vision backend. Moreover, a variety of schemes are introduced to reduce the cost when building SRTs.

In this paper, we propose a navigation algorithm oriented to multi-agent environment. This algorithm is expressed as a hierarchical framework that contains a Hidden Markov Model (HMM) and a Deep Reinforcement Learning (DRL) structure. For simplification, we term our method Hierarchical Navigation Reinforcement Network (HNRN). In high-level architecture, we train an HMM to evaluate the agents perception to obtain a score. According to this score, adaptive control action will be chosen. While in low-level architecture, two sub-systems are introduced, one is a differential target-driven system, which aims at heading to the target; the other is a collision avoidance DRL system, which is used for avoiding dynamic obstacles. The advantage of this hierarchical structure is decoupling the target-driven and collision avoidance tasks, leading to a faster and more stable model to be trained. The experiments indicate that our algorithm has higher learning efficiency and rate of success than traditional Velocity Obstacle (VO) algorithms or hybrid DRL method.

Personal handwriting style fonts generation is a diverting but time-consuming task due to the large size of Chinese character set. In addition, unlike standard printed style fonts, hand-writing style fonts are of more complicated stroke and glyph feature. In this paper, an improved network architecture is proposed for learning and generation of personal hand-writing style fonts based on small character set. The network is composed of three sub-networks: 1) a classification network for identifying the general style of the target fonts; 2) a generating network for transferring the identified fonts to the target fonts; 3) a discriminating network for differentiating the generated image from real ones. The experiments revealed the effectiveness of the model for generating personal hand-writing style font with relatively small data size, reduction by a scale of 10 comparing to previous reported works.

This paper presents a new model of automatic music generation which is based on simulated annealing and genetic algorithm. Our model uses simulated annealing algorithm to create melody units on different chords, and regards them as the genes in genetic algorithm, which can pick out a fine combination of these genes. This model is efficient for producing massive fair-sounding pieces of double-track music, when provided with a customized metric. Hence, it can be especially useful for massively producing background musics for video games, or providing inspirations for music composers. In this paper, we detailedly state how to transform the prior knowledge about music theory into a reasonable metric, and propose a sample metric for reference. Using this sample metric, we generate a set of music pieces as test cases. The evaluation results show that these pieces fully satisfy the need of quality and creativity.

In this paper, we present an accurate approach to estimate vehicles' pose and shape from off-board multiview images. The images are taken by monocular cameras and have small overlaps. We utilize state-of-the-art convolutional neural networks (CNNs) to extract vehicles' semantic keypoints and introduce a Cross Projection Optimization (CPO) method to estimate the 3D pose. During the iterative CPO process, an adaptive shape adjustment method named Hierarchical Wireframe Constraint (HWC) is implemented to estimate the shape. Our approach is evaluated under both simulated and real-world scenes for performance verification. It's shown that our algorithm outperforms other existing monocular and stereo methods for vehicles' pose and shape estimation. This approach provides a new and robust solution for off-board visual vehicle localization and tracking, which can be applied to massive surveillance camera networks for intelligent transportation., Comment: 8 pages, 8 figures, published in ROBIO 2018

In the field of autonomous driving, abundant researches have been carried out in development of advanced Lidar or vision algorithms for mapping and localization, and some efforts in the field of sensor fusion have been made to combine the advantages of multiple sensor types. However, most works focused on the 2D map construction, which had its own limitation with complex environments. In this paper, a new algorithm based on the fusion of Lidar and Inertial Measurement Unit (IMU) data is developed to construct a 2.5D map. We integrate the height information transformed from IMU sensor with laser scan results to construct a 2.5D occupied grid map. The proposed algorithm is convenient to implement on real-world mobile vehicles, and it is fast and robust. The effectiveness of our algorithm is validated by both simulation and practical experiment.

The challenges in transforming paintings into music is well-known, since the relationship between two kinds of art is unclear. Different composers write different music when the same painting is presented to them. In this paper, a cross-mordality model has been proposed for transforming images into multitrack music based on the framework of deep convolutional generative adversarial networks (DCGANs). The proposed model is trained on a classical music dataset and a dataset of impressionist paintings. The model can be applied to transfer impressionist paintings into classical music with two tracks. By using music evaluation methods, the harmonicity of the generated music can be confirmed. Our model is the first attempt of our knowledge at transforming paintings into music segments.

A vision-based perception system for understanding the outdoor environment has been proposed. This system utilizes and applies the useful information from the binocular cameras of a legged robot for landform — terrain — ground environmental perception. The ground texture recognition can provide information about the material of the ground then a legged robot can choose an appropriate walking pose accordingly. With the terrain perception and obstacle detection, combined with semantic segmentation of the environment, a legged robot can move towards the target with intelligent obstacle avoidance strategy. Based on this vision-based perception system, we propose a vision-based perception framework for outdoor navigation tasks applicable to legged robots. In this framework, environmental modeling and situation assessment will be firstly carried out combined with multi-mode sensor fusion, and then footsteps and local path planning can be generated.

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.

Localization and navigation are fundamental issues to autonomous mobile robotics. In the case of the environmental map has been built, the traditional two-dimensional (2D) lidar localization and navigation system can't be matched to the initial position of the robot in dynamic environment and will be unreliable when kidnapping occurs. Moreover, it relies on high-cost lidar for high accuracy and long range. In view of this, the paper presents a low cost navigation system based on a low cost lidar and a cheap webcam. In this approach, 2D-codes are attached to the ceiling, to provide reference points to aid the indoor robot localization. The mobile robot is equipped with webcam pointing to the ceiling to identify 2D-codes. On the other hand, a low-cost 2D laser scanner is applied to build a map in unknown environment and detect obstacles. Adaptive Monte Carlo Localization (AMCL) is implements for lidar positioning, A* and Dynamic Window Approach (DWA) are applied in path planning based on a 2D grid map. The error analysis and experiments has validated the proposed method.

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.

This paper introduces a face detection, classification and counting system that is robust and works in real-time. It tracks multiple people, which is useful for face counting. The classification problem is defined as differentiating and then classifying the front of a face into Asian or non-Asian categories. The first step, is principal component analysis (PCA) for feature generation and independent component analysis (ICA) for feature extraction. Then, we employ support vector machine (SVM) for the training process and combine different SVM classifiers to create new classifiers, which improves the classification rate. Based on this, we can count the number of Asians and non-Asians. Experiments show that our system achieves a classification rate of 82.5% based on a database containing 750 face images from FERET.

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.

Mapping, localization, and navigation are functions necessitated by a diverse range of household service robot solutions. This chapter presents a wide variety of mapping, localization, and navigation techniques that suit a wide range of robot platforms and applications. The problem of mapping and localization has been approached in many ways and we review successful results attained by traditional radio tracking, ultra wideband sensing, RFID communication, North-Star infrared sensing, two-dimensional special pattern detection, laser-range surveying, various camera system sensing, time-of-flight camera ranging, and finally low-cost contact and near-field sensing. The problem of navigation, path planning, and obstacle avoidance requires not only sensing described above but adequate robot responses in a dynamic situation. There are many approaches to successful obstacle avoidance and our review of the literature covers the use of structured light as well as linear and planar sensors, along with visual processing techniques such as optic flow, ground plane segmentation, and feature-based obstacle avoidance, in addition to the optimization of path planning, robot interaction, and behavior.

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 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.

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.

In this paper ceiling affixed ARToolKitPlus 2D code artificial landmarks are evaluated for purposes of robot localization and navigation. Ceiling affixed codes rarely come in contact with people, equipment or robots, and for this reason they are more likely to stay detectable over a longer period of time. Multi threshold averaging, light gradient compensation and neighbourhood search techniques further enhanced AR-ToolKitPlus performance. Multi threshold averaging collects positioning results at each gray scale threshold level. After all of the threshold levels are analyzed, the related results are averaged into a final result. Light gradient compensation eliminates effects of uneven lighting in the neighbourhood of a 2D code. Neighbourhood search for a 2D code requires fewer computational resources than a global search. Further repeatability improvements are achieved by means of averaging localizations. Localization performance is evaluated at varying distances of the 2D code from the camera. Experimental results show substantial improvements in repeatability and reliability over the baseline ARToolKitPlus performance. Improved performance will allow for realtime 2D code based localization for indoor robot navigation.

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.