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Scene recognition is a fundamental task in robotic perception. For human beings, scene recognition is reasonable because they have abundant object knowledge of the real world. The idea of transferring prior object knowledge from humans to scene recognition is significant but still less exploited. In this paper, we propose to utilize meaningful object representations for indoor scene representation. First, we utilize an improved object model (IOM) as a baseline that enriches the object knowledge by introducing a scene parsing algorithm pretrained on the ADE20K dataset with rich object categories related to the indoor scene. To analyze the object co-occurrences and pairwise object relations, we formulate the IOM from a Bayesian perspective as the Bayesian object relation model (BORM). Meanwhile, we incorporate the proposed BORM with the PlacesCNN model as the combined Bayesian object relation model (CBORM) for scene recognition and significantly outperforms the state-of-the-art methods on the reduced Places365 dataset, and SUN RGB-D dataset without retraining, showing the excellent generalization ability of the proposed method. Code can be found at https://github.com/hszhoushen/borm., Comment: 8 pages, 5 figures, conference, Accepted by IROS2021

Hand gesture recognition plays an essential role in the human-robot interaction (HRI) field. Most previous research only studies hand gesture recognition in a short distance, which cannot be applied for interaction with mobile robots like unmanned aerial vehicles (UAVs) at a longer and safer distance. Therefore, we investigate the challenging long-range hand gesture recognition problem for the interaction between humans and UAVs. To this end, we propose a novel attention-based single shot multibox detector (SSD) model that incorporates both spatial and channel attention for hand gesture recognition. We notably extend the recognition distance from 1 meter to 7 meters through the proposed model without sacrificing speed. Besides, we present a long-range hand gesture (LRHG) dataset collected by the USB camera mounted on mobile robots. The hand gestures are collected at discrete distance levels from 1 meter to 7 meters, where most of the hand gestures are small and at low resolution. Experiments with the self-built LRHG dataset show our methods reach the surprising performance-boosting over the state-of-the-art method like the SSD network on both short-range (1 meter) and long-range (up to 7 meters) hand gesture recognition tasks.

The ability to maintain balance and stability is a major challenge for quadruped robots in locomotion. For example, when walking in the unstructured environment, the robot not only needs to adjust its gait according to the terrain information, but also needs to tune the body posture based on the real-time body posture acquired from inertial measurement unit (IMU) to maintain stability. In this paper, we present a method that focuses on the design of the posture correction controller based on the IMU feedback for adaptive slope walking for a quadruped robot. To this end, the posture correction controller is integrated into the foot trajectory generator to maintain balance. We experimentally validated our approach both on simulation and real quadruped robot. Compared with the one only with the foot trajectory generator, the quadruped robot can walk more stable with the posture correction controller integrated into the foot trajectory generator and has a better locomotion ability. Our method increases locomotion stability when walking on the slopes.

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.

Currently, AGVs (Auto Guided Vehicles) are the most widely used type of mobile robot in real world for logistics and intelligent manufacturing. While most of indoor AGVs do not have a special requirement on shock absorption systems, it remains a major concern for the outdoor AGVs' application. Compared with most of the current mono-spring absorption system designs, which means that all the spring dampers have identical elastic coefficient, dual-spring designs have an advantage in obstacle-crossing capability and shock absorption performances. This paper studies and validates the enhanced outdoor working performances of AGVs with dual-spring shock absorption systems. Theoretical dynamic analysis has been conduct to reveal the advantages in shock absorption characteristics. A series of simulation experiments have been performed to verify its advantages in stability and obstacle-crossing capabilities compared to conventional mono-spring absorption designs.

Recent studies have witnessed a significant progress in developing legged robots with rapid and stable locomotion speeds and adaptability to complex roads, but their high energy consumption limited their development and applications. This paper analyzes the energy efficiency of a bionic electric-drive panda robot serving as an emotional companion for robot-enhanced therapy. Unlike other quadruped robots, our bionic robot focuses on emotional interaction rather than capability of dexterous locomotion and operations. This results in the need to change working period. Then we proposed four strategies to reduce energy consumption. The energy efficiency optimization strategy described in this paper is suitable for quadruped robots focusing on none-highly dynamic motions, and can be treated as locomotion optimization strategy based on energy efficiency optimization for quadruped robots to some extent.

Accurate turning is not an easy task for the quadruped robot and previous work mainly addresses the problem with complex control strategy or systems. This paper proposes a design method for the accurate turning motion of a quadruped robot with 12 degrees of freedom (DOF). Unlike others, we have achieved precise turning motion with a relatively simple geometric approach. Once the parameters of desired movement are set, such as the turning radius and angles, the robot can realize corresponding circle locomotion. A design architecture including geometric analysis, 3D foot trajectory design and inverse kinematics is presented to solve the problem systematically. In addition, according to the different conditions, the corresponding compensation coefficient is necessarily considered in order to acquire precise values of leg joints. At last, comparison results by several experiments have identified the feasibility and accuracy of turning along a circle trajectory based on our proposed method.

Trajectory tracking control is one of the core techniques that impacts the auto-driving performance of a mobile robot. Whereas, there lacks enough work on reference trajectory generation and controller design for practical usage. This paper considers mobile robots with unicycle vehicle model on which most of automatic guided vehicles (AGVs) in real world are built. A new trajectory planning algorithm is developed, and is applied along with a control law considering constraints of the unicycle model and limited motor capabilities. The proposed algorithm is easy to be implemented on real world AGVs, and it yields a fast, accurate and robust trajectory tracking performance. The effectiveness of the algorithm is validated by simulation tests.

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.

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.

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.

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.

Recently, the control of telepresence robots is still a challenge for researchers. In this paper, we propose a method to realize a more natural assisted navigation for telepresence robots during teleoperation. Different from existing approaches, the proposed method utilizes an omni-directional chassis which is realized by a three wheels drive (3WD) mechanism. Without using any expensive 3D sensing equipments, our proposed method is realized by using only low cost ultrasonic sensors. To increase the sensing frequency, we propose a new sensing method for the ultrasonic sensors. To generate the avoidance motions, Potential Field (PF) method is applied. Different from conventional approach, obstacle avoidance motions are realized naturally through linear translations. With this method, the robot's orientation can be maintained which improves user experience and allows a more user-friendly teleoperation. To evaluate the proposed method, it has been implemented on our previous developed telepresence robot, called ‘T-robot’. Results show that the PF method helps reduce the teleoperated time with no collisions and user intervention. Also, a more natural movement and user friendly control is achieved.

Localization is one of the key research topics in robotics. Although computer vision based methodologies such as Augmented Markers provides a realtime localization approach, the sensing range and localization accuracy are the main limitations for applications. In this paper, we proposed the use of a problem solving tool, TRIZ to overcome these limitations. Among the tools provided by TRIZ, we illustrated the selection of principles for problem solving. To enhance the sensing range, a novel AR marker is presented. To improve the localization accuracy, we presented the use of an AR polyhedron for system localization. With the use of an AR cube for illustration, we further developed a new optimization strategy. It is a weighted sum method which aims to optimize the position of the AR cube by considering the orientation of different AR markers captured by a camera. To evaluate the proposed methods, different experiments have been conducted including static and dynamic localizations. Results show that the proposed solutions and strategy can significantly improve the sensing range and accuracy of the conventional AR localization system.

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.

Localization of an autonomous mobile robot has always been a topic of interest in Robotics community. The constraints of working under different environments are constantly a challenge in designing an efficient localization system. We propose a localization algorithm for a mobile robot, which will be used for home security. The objective is to perform indoor localization efficiently and keeping the cost as low as possible so as to make it commercializable. Here, we introduce an approach based on recognizing a pattern generated by laser pointers on the wall, which is captured by an infrared camera. The image of this pattern is used to locate the position of a robot in the room. The problem of working under black ceiling has been overcomed because the projection is on the wall and is moving which also increases the range of operation. As the pattern is unique, the interference of strong sunlight has also been fixed to some extent. Experimental results show that the proposed approach is effective in real environment and opens new doors for future research.

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.

As a key device in nuclear station, the steam generator is vulnerable to certain kinds of damages, which make it necessary to check the generator annually. However, it is impossible for human to finish the examine because of that the tremendous tubes inside the generator makes its inner space too crowded. In this paper, we introduced a novel designed climbing robot system which is competent to the work. The main part of the robot system is a vehicle which can moves inside the steam generator carrying a PT Arm. Four cameras are installed in the vehicle and PT Arm to send the real time video back for analysis. We built a control panel as the interface for users to control the vehicle and a cabinet as the container of all the processors int the control system. Experiments show that our system works stably inside the steam generator.

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.

Combining functions with aesthetics, characters with drawings, Chinese calligraphy is a unique type of traditional art in China. To mimic the special writing skills, we develop a calligraphy robot which consists of one 6-DOF robot arm, one linear rail and one paper conveyor. This calligraphy robot, called Callibot, has several features. Firstly, it has sufficient degrees of freedom and very few singularities which improve the manipulability of the robot. Secondly, Callibot could mimic real motion of human writing because playback from demonstration approach is adopted. The encoders will record the joint positions while demonstrating. Then Callibot filters out the noises (error readings in serial port communication) and repeats the motion. Thirdly, the workspace of the robot arm is largely expanded due to the linear rail and the paper conveyor. The experimental results show that Callibot is capable of writing a large piece of aesthetic calligraphic work.

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.

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 paper presents a newly-designed robot named “Rubbot” dedicated to climbing on soft flexible clothes. Equipped with novel grippers which grip and rub on clothes, Rubbot is able to climb on flexible clothes and control how much fabric to grasp by feedback from infrared sensor. Rubbot also has a frame which has three passive folders which adjust the climbing posture of Rubbot. This not only makes Rubbot quite functional with clothes of different thicknesses and curved surfaces, but also makes Rubbot's motion more flexible. A theory of the deformation of cloth is then presented based on an analysis of creases created while Rubbot is climbing, this leads to a more reliable method to climb flexible surfaces. Finally experiments have verified that Rubbot is effective on flexible surfaces, as it can climb on 95% of the surfaces human clothes and still perform well on non-rigidly backed cloth.

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%.

In decades, many researchers have came up with many path planning algorithms. Robots rely on sensor data to deduce inner and outer state as its position. Many localization algorithm suggests robot's knowledge of its position should be a probability distribution. However, previews path planning algorithms fell to utilize this information and just simply assume the robots to be in a single certain point. In this paper, we described the implementation of a new algorithm M* that takes the uncertainty of the robots location into detailed consideration. M* uses Monto Carlo method to represent probability distribution and by considering problem in high dimension space it transfer the problem into a neatly form. Some important concepts are introduced and pseudo code is used to show how them work.

For surveillance robots, road detection is of high importance for other functionalities such as pedestrian detection, obstacle avoidance, autonomous running, etc. The vision-based road detection is to classify image pixels belonging to road surface or not. Up to now, most algorithms are designed for working during daytime. In this paper, we focus on road detection at night. Firstly a planar reflection model is used to fit the intensity distribution of the images pixels got from a near-infrared camera. After that, we use a pixel-based classification to determine whether the pixel belongs to the road surface or not. In the experiments, we compare our algorithm with the region growing method. The results show that our approach works better in several aspects.

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.