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The purpose of this paper is to solve the ground verification and test method for space robot system capturing the target satellite based on visual servoing with time-delay in 3-dimensional space prior to space robot being launched.

To implement the approaching and capturing task, a motion planning method for visual servoing the space manipulator to capture a moving target is presented. This is mainly used to solve the time-delay problem of the visual servoing control system and the motion uncertainty of the target satellite. To verify and test the feasibility and reliability of the method in three-dimensional (3D) operating space, a set of ground hardware-in-the-loop simulation verification systems is developed, which adopts the end-tip kinematics equivalence and dynamics simulation method.

The results of the ground hardware-in-the-loop simulation experiment validate the reliability of the eye-in-hand visual system in the 3D operating space and prove the validity of the visual servoing motion planning method with time-delay compensation. At the same time, owing to the dynamics simulator of the space robot added in the ground hardware-in-the-loop verification system, the base disturbance can be considered during the approaching and capturing procedure, which makes the ground verification system realistic and credible.

The ground verification experiment system includes the real controller of space manipulator, the eye-in-hand camera and the dynamics simulator, which can veritably simulate the capturing process based on the visual servoing in space and consider the effect of time delay and the free-floating base disturbance.

Social signal processing under affective computing aims at recognizing and extracting useful human social interaction patterns. Fight is a common social interaction in real life. A fight detection system finds wide applications. This paper aims to detect fights in a natural and low-cost manner.

Research works on fight detection are often based on visual features, demanding substantive computation and good video quality. In this paper, the authors propose an approach to detect fight events through motion analysis. Most existing works evaluated their algorithms on public data sets manifesting simulated fights, where the fights are acted out by actors. To evaluate real fights, the authors collected videos involving real fights to form a data set. Based on the two types of data sets, the authors evaluated the performance of their motion signal analysis algorithm, which was then compared with the state-of-the-art approach based on MoSIFT descriptors with Bag-of-Words mechanism, and basic motion signal analysis with Bag-of-Words.

The experimental results indicate that the proposed approach accurately detects fights in real scenarios and performs better than the MoSIFT approach.

By collecting and annotating real surveillance videos containing real fight events and augmenting with well-known data sets, the authors proposed, implemented and evaluated a low computation approach, comparing it with the state-of-the-art approach. The authors uncovered some fundamental differences between real and simulated fights and initiated a new study in discriminating real against simulated fight events, with very good performance.

Positioning is a key task in most field robotics applications but can be very challenging in GPS‐denied or high‐slip environments. The purpose of this paper is to describe a visual odometry strategy using only one camera in country roads.

This monocular odometery system uses as input only those images provided by a single camera mounted on the roof of the vehicle and the framework is composed of three main parts: image motion estimation, ego‐motion computation and visual odometry. The image motion is estimated based on a hyper‐complex wavelet phase‐derived optical flow field. The ego‐motion of the vehicle is computed by a blocked RANdom SAmple Consensus algorithm and a maximum likelihood estimator based on a 4‐degrees of freedom motion model. These as instantaneous ego‐motion measurements are used to update the vehicle trajectory according to a dead‐reckoning model and unscented Kalman filter.

The authors' proposed framework and algorithms are validated on videos from a real automotive platform. Furthermore, the recovered trajectory is superimposed onto a digital map, and the localization results from this method are compared to the ground truth measured with a GPS/INS joint system. These experimental results indicate that the framework and the algorithms are effective.

The effective framework and algorithms for visual odometry using only one camera in country roads are introduced in this paper.

Learning from demonstration (LfD) provides an intuitive way for non-expert persons to teach robots new skills. However, the learned motion is typically fixed for a given scenario, which brings serious adaptiveness problem for robots operating in the unstructured environment, such as avoiding an obstacle which is not presented during original demonstrations. Therefore, the robot should be able to learn and execute new behaviors to accommodate the changing environment. To achieve this goal, this paper aims to propose an improved LfD method which is enhanced by an adaptive motion planning technique.

The LfD is based on GMM/GMR method, which can transform original off-line demonstrations into a compressed probabilistic model and recover robot motion based on the distributions. The central idea of this paper is to reshape the probabilistic model according to on-line observation, which is realized by the process of re-sampling, data partition, data reorganization and motion re-planning. The re-planned motions are not unique. A criterion is proposed to evaluate the fitness of each motion and optimize among the candidates.

The proposed method is implemented in a robotic rope disentangling task. The results show that the robot is able to complete its task while avoiding randomly distributed obstacles and thereby verify the effectiveness of the proposed method. The main contributions of the proposed method are avoiding unforeseen obstacles in the unstructured environment and maintaining crucial aspects of the motion which guarantee to accomplish a skill/task successfully.

Traditional methods are intrinsically based on motion planning technique and treat the off-line training data as a priori probability. The paper proposes a novel data-driven solution to achieve motion planning for LfD. When the environment changes, the off-line training data are revised according to external constraints and reorganized to generate new motion. Compared to traditional methods, the novel data-driven solution is concise and efficient.

Based on virtual maintenance, this paper aims to propose a time prediction method of assembly and disassembly (A&D) actions of product maintenance process to enhance existing methods’ prediction accuracy, applicability and efficiency.

First, a framework of A&D time prediction model is constructed, which describes the time prediction process in detail. Then, basic maintenance motions which can comprise a whole A&D process are classified into five categories: body movement, working posture change, upper limb movement, operation and grasp/placement. A standard posture library is developed based on the classification. Next, according to motion characteristics, different time prediction methods for each motion category are proposed based on virtual maintenance simulation, modular arrangement of predetermined time standard theory and the statistics acquired from motion experiment. Finally, time correction based on the quantitative evaluation method of motion time influence factors is studied so that A&D time could be predicted with more accuracy.

Case study of time prediction of products’ various A&D processes is conducted by implementing the proposed method. The prediction process of diesel cooling fan disassemble time is presented in detail. Through comparison, the advantages and effectiveness of the method are demonstrated.

This paper proposes a more accurate, efficient and applicable product A&D time prediction method. It can help designers predict A&D time of a product maintenance accurately in early design phases without a physical prototype. It can also provide basis for the verification of maintainability, the balance of the design of product structure and system layout.

The purpose of this paper is to introduce a motion control method for WFF-AmphiRobot, which can effectively realize the flexible motion of the robot on land, underwater and in the transition zone between land and water.

Based on the dynamics model, the authors selected the appropriate state variables to construct the state space model of the robot and estimated the feedback state of the robot through the maximum a posteriori probability estimation. The nonlinear predictive model controller of the robot is constructed by local linearization of the model to perform closed-loop control on the overall motion of the robot. For the control problem of the terminal trajectory, using the neural rhythmic movement theory in bionics to construct a robot central pattern generator (CPG) for real-time generation of terminal trajectory.

In this paper, the motion state of WFF-AmphiRobot is estimated, and a model-based overall motion controller for the robot and an end-effector controller based on neural rhythm control are constructed. The effectiveness of the controller and motion control algorithm is verified by simulation and physical prototype motion experiments on land and underwater, and the robot can ideally complete the desired behavior.

The paper designed a controller for WFF-AmphiRobot. First, when constructing the robot state estimator in this paper, the robot dynamics model is introduced as the a priori estimation model, and the error compensation of the a priori model is performed by the method of maximum a posteriori probability estimation, which improves the accuracy of the state estimator. Second, for the underwater oscillation motion characteristics of the flipper, the Hopf oscillator is used as the basis, and the flipper fluctuation equation is modified and improved by the CPG signal is adapted to the flipper oscillation demand. The controller effectively controls the position error and heading angle error within the desired range during the movement of the WFF-AmphiRobot.

Motion capture system (MoCap) has been used in measuring the human body segments in several applications including film special effects, health care, outer-space and under-water navigation systems, sea-water exploration pursuits, human machine interaction and learning software to help teachers of sign language. The purpose of this paper is to help the researchers to select specific MoCap system for various applications and the development of new algorithms related to upper limb motion.

This paper provides an overview of different sensors used in MoCap and techniques used for estimating human upper limb motion.

The existing MoCaps suffer from several issues depending on the type of MoCap used. These issues include drifting and placement of Inertial sensors, occlusion and jitters in Kinect, noise in electromyography signals and the requirement of a well-structured, calibrated environment and time-consuming task of placing markers in multiple camera systems.

This paper outlines the issues and challenges in MoCaps for measuring human upper limb motion and provides an overview on the techniques to overcome these issues and challenges.

This paper aims to introduce the human arm movement primitive (HAMP) to express and plan the motions of anthropomorphic arms. The task planning method is established for the minimum task cost and a novel human-like motion planning method based on the HAMPs is proposed to help humans better understand and plan the motions of anthropomorphic arms.

The HAMPs are extracted based on the structure and motion expression of the human arm. A method to slice the complex tasks into simple subtasks and sort subtasks is proposed. Then, a novel human-like motion planning method is built through the selection, sequencing and quantification of HAMPs. Finally, the HAMPs are mapped to the traditional joint angles of a robot by an analytical inverse kinematics method to control the anthropomorphic arms.

For the exploration of the motion laws of the human arm, the human arm motion capture experiments on 12 subjects are performed. The results show that the motion laws of human arm are reflected in the selection, sequencing and quantification of HAMPs. These motion laws can facilitate the human-like motion planning of anthropomorphic arms.

This study presents the HAMPs and a method for selecting, sequencing and quantifying them in human-like style, which leads to a new motion planning method for the anthropomorphic arms. A similar methodology is suitable for robots with anthropomorphic arms such as service robots, upper extremity exoskeleton robots and humanoid robots.

Over the last few years, there has been considerable interest in developing autonomous robots that are able to move in constrained environments, inspired by the motion of lower animal forms such as parasites, worms, insects and even snakes and eels. In this paper, we describe a new design and concept of autonomous microrobot based on senseless motion. The “senseless motion” is the movement in absence of an external perception system. In a lot of living species, rhythmic movements, finalized to locomotion, are produced by oscillating circuits in the central nervous system. We reproduced this motion using a voice‐coil actuator embedded with its control hardware in a cylinder presents on its external surface a skate‐like structure produces a differential friction in order to move the robot on different substrates. Preliminary experiments have been carried out on several materials in order to measure the frictional forces produced by the robot during its motion and to verify the repeatability of senseless motion.

The purpose of this paper is to present the design and implementation of a zero‐initial‐velocity self‐motion scheme on a six degrees of freedom (six‐DOF) planar robot manipulator.

In view of the existence of physical limits in an actual robot manipulator, both joint‐angle limits and joint‐velocity limits are initially incorporated into the proposed self‐motion scheme for practical purposes. The proposed self‐motion scheme is then reformulated as a quadratic program (QP) and resolved at the joint‐velocity level. By combining the zero‐initial‐velocity constraint, the resultant QP can prevent the occurrence of a large initial joint velocity. Finally, based on the conversion technique of QP to a linear variational inequality, a numerical computing algorithm is presented to solve the QP and the corresponding self‐motion scheme.

The proposed zero‐initial‐velocity self‐motion scheme eliminates the phenomenon of the abrupt and drastic increase in joint velocity at the beginning of the self‐motion task execution. Simulative and experimental results based on a practical six‐DOF planar robot manipulator further verify the realizability, effectiveness and accuracy of the proposed self‐motion scheme. Based on the simulative results, the joint angle and the joint velocity meet the joint physical constraints.

The paper provides effective methods for handling the physical limits, the design of zero‐initial velocity, and the conversion from joint angle and joint velocity to motor‐driving pulses. Thus, the effective and safe self‐motion control of a manipulator is realized.

The paper describes the design and implementation of a zero‐initial‐velocity self‐motion scheme.