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Humans have a remarkable ability to learn continuously from th e environment and inner experience. One of the grand goals of robots is to build an artificial "lifelong learning" agent that can shape a cultivated understanding of the world from the current scene and previous knowledge via an autonomous lifelong development. It is challenging for the robot learning process to retain earlier knowledge when robots encounter new tasks or information. Recent advances in computer vision and deep -learning methods have been impressive due to large-scale data sets, such as ImageNet and COCO. However, robotic vision poses unique new challenges for applying visual algorithms developed from these computer vision data sets because they implicitly assume a fixed set of categories and time -invariant task distributions.

In this paper, a novel soft inflatable arm is proposed for telepresence robots. The new proposed structure of the arm is achieved by a very common and low-cost inflatable material and it is very light, weighing only about 50 g. However, it can realize agile movement by driving six tiny cables installed in the shoulder and elbow joints. The soft inflatable arm can work by pumping air at a very low pressure ( $7.32 \pm 3.45$ kPa) and allows direct and soft human contact without any external force sensors. This paper proposes joint compressed models, joint kinematic models, and kinematic models for the whole inflatable robot arm. These models can be easily applied to multijoint arms. In addition, a new redundancy resolution method is also developed for the inflatable arm, which makes it easier to control resolution and is less complex than other traditional approaches. Numerous experiments have been conducted, including performances of accuracy, repeatability, motion trajectories, human-safe interaction, and remote interaction. Results are satisfactory and validate the expected performance of the proposed robotic arm.

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.

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.

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.

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, we study the parameter setting for a set of intelligent multi-category classifiers in wall-following robot navigation. Based on the swarm optimization theory, a particle selecting approach is proposed to search for the optimal parameters, a key property of this set of multi-category classifiers. By utilizing the particle swarm search, it is able to obtain higher classification accuracy with significant savings on the training time compared to the conventional grid search. For wall-following robot navigation, the best accuracy (98.8%) is achieved by the particle swarm search with only 1/4 of the training time by the grid search. Through communicating the social information available in particle swarms in the training process, classification-based learning can achieve higher classification accuracy without prematurity. One of such learning classifiers has been implemented in SIAT mobile robot. Experimental results validate the proposed search scheme for optimal parameter settings.

This paper proposes a global path- and motion-planning algorithm that enables inchworm-like robots to navigate their way up tree branches. The intuitive climbing space representation method proposed here greatly simplifies the path-planning problem. The dynamic programming algorithm can be used to identify the optimal path leading to the target position in the target direction according to the constraints and requirements specified. The planned path can be applied in any tree-climbing robot that utilizes the nonenclosure gripping method. An efficient motion-planning algorithm for continuum inchworm-like robots is then developed to enable them to climb along the planned path with a high degree of accuracy. In comparison with the method proposed in our previous study, the method proposed herein significantly improves consistency between the planned path and the motions of the robot, and therefore makes it more practical to implement the motion-planning algorithm in trees of different shapes. The paper also describes hardware experiments in which the proposed planning algorithm is applied to enable inchworm-like robots to climb real trees, thus validating the proposed planning algorithm in practice. © 2012 Wiley Periodicals, Inc. © 2013 Wiley Periodicals, Inc.

Treebot is the first tree-climbing robot that is capable of climbing from a tree trunk to a branch. The robot employs several design principles adapted from arboreal animals, including claw gripping and inchworm locomotion, with a certain artificial optimization to achieve high maneuverability on irregular-shaped trees. Treebot is composed of a pair of tree grippers that permits Treebot to attach to a wide variety of trees with a wide range of gripping curvature, and a novel continuum maneuvering structure that provides high maneuverability and adaptability. In the robot actuation, only five actuators are necessary. Although Treebot weighs only 600 gr, it has a payload capability of 1.75 kg, which is nearly three times its own weight. This paper describes the design process and specifically addresses the robot locomotion and optimization of gripping force. Experimental results demonstrate the robot's ability to climb trees with high maneuverability. © 2012 Wiley Periodicals, Inc. © 2012 Wiley Periodicals, Inc.

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.

Space robotic systems are expected to play an increasingly important role in future space activities. Nevertheless, dynamics modeling and motion planning of a space robot are much more complex than those of a fixed-base robot, due to the dynamic coupling between the manipulator and its base. On the other hand, in order to assure the success of on-orbital missions, many experiments are required to verify the key algorithms on the ground before the space robot is launched. In this paper, the main research achievements on dynamics modeling, path planning, and ground verification are reviewed, and future studies are recommended. Firstly, we summarize the essential modeling concepts, and deduce the kinematics and dynamics equations of a space robot. Secondly, the main motion planning approaches are discussed. Then, different ground verification systems, including the air-bearing table, neutral buoyancy, airplane flying, free-falling motion, suspension system, and hybrid system, are introduced. Finally, the future research trends are forecasted.

Space robotic systems are expected to play an increasingly important role in the future on-orbit service. The applications include repairing, refueling or de-orbiting of a satellite, or removal of the space debris. The dynamical performances of space robotic system result from the multi-physics interactions between mechanical, electrical, electronic, control, etc. In this paper, we developed a unified multi-domain modelling and simulation system. The system is composed of the following modules: the path planner, joint controllers, motor and its driver, gearing mechanism of the space manipulators, the Guidance, Navigation, and Control (GNC) system, the actuators of the base, and the orbital environment, orbital dynamic and the multi-body dynamic of the whole system, etc. Based on this system, the operation during different stages, including far range rendezvous, close range rendezvous (is usually divided into two sub-phases: closing and final approach) and target capturing can be studied from the view of multi-physics domains. The key algorithms, such as pose (position and attitude) measurement, GNC of the base, path planning and control of the space manipulator, and so on, can be validated using the system. As examples, the capturing processes of a moving target under free-floating and attitude-controlled modes are simulated and the simulation results are given.

SUMMARYThe technologies of autonomous rendezvous and robotic capturing of non-cooperative targets are very crucial for the future on-orbital service. In this paper, we proposed a method to achieve this aim. Three problems were addressed: the target recognition and pose (position and attitude) measurement based on the stereo vision, the guidance, navigation and control (GNC) of the chaser, and the coordinated plan and control of space robot (CP&C). The pose measurement algorithm includes image filtering, edge detection, line extraction, stereo match and pose computing, et al. Based on the measured values, a certain GNC algorithm was designed for the chaser to approach and rendezvous with the target. Then the CP&C algorithm, which is proved to be advantageous over the traditional separated method, was used to plan and track the trajectories of the base pose and the joint angle. At last, a 3D simulation system was developed to evaluate the proposed method. Simulation results verified the corresponding algorithms.

SUMMARYSpace robotic systems are expected to play an increasingly important role in the future. Unlike on the earth, space operations require the ability to work in the unstructured environment. Some autonomous behaviors are necessary to perform complex and difficult tasks in space. This level of autonomy relies not only on vision, force, torque, and tactile sensors, but also the advanced planning and decision capabilities. In this paper, the authors study the autonomous target capturing from the issues of theory and experiments. Firstly, we deduce the kinematic and dynamic equations of space robotic system. Secondly, the visual measurement model of hand–eye camera is created, and the image processing algorithms to extract the target features are introduced. Thirdly, we propose an autonomous trajectory planning method, directly using the 2D image features. The method predicts the target motion, plans the end-effector's velocities and solves the inverse kinematic equations using practical approach to avoid the dynamic singularities. At last, numeric simulation and experiment results are given. The ground experiment system is set up based on the concept of dynamic simulation and kinematic equivalence. With the system, the experiments of autonomous capturing a target by a free-floating space robot, composed of a 6-DOF manipulator and a satellite as its base, are conducted, and the results validate the proposed algorithm.

For the aging population, surveillance in household environments has become more and more important. In this paper, we present a household robot that can detect abnormal events by utilizing video and audio information. In our approach, moving targets can be detected by the robot using a passive acoustic location device. The robot then tracks the targets by employing a particle filter algorithm. To adapt to different lighting conditions, the target model is updated regularly based on an update mechanism. To ensure robust tracking, the robot detects abnormal human behavior by tracking the upper body of a person. For audio surveillance, Mel frequency cepstral coefficients (MFCC) is used to extract features from audio information. Those features are input to a support vector machine classifier for analysis. Experimental results show that the robot can detect abnormal behavior such as "falling down" and "running". Also, a 88.17% accuracy rate is achieved in the detection of abnormal audio information like "crying", "groan", and "gun shooting". To lower the false alarms by abnormal sound detection system, the passive acoustic location device directs the robot to the scene where abnormal events occur and the robot can employ its camera to further confirm the occurrence of the events. At last, the robot will send the image captured by the robot to the mobile phone of master.

Space robots are playing an increasingly important role in on-orbital servicing, including repairing, refueling, or de-orbiting the satellite. The target must be captured and berthed before the servicing task starts. However, the attitude of the base may lean much and needs re-orientating after capturing. In this paper, a method is proposed to berth the target, and re-orientate the base at the same time, using manipulator motion only. Firstly, the system state is formed of the attitude quaternion and joint variables, and the joint paths are parameterized by sinusoidal functions. Then, the trajectory planning is transformed to an optimization problem. The cost function, defined according to the accuracy requirements of system variables, is the function of the parameters to be determined. Finally, we solve the parameters using the particle swarm optimization algorithm. Two typical cases of the spacecraft with a 6-DOF manipulator are dynamically simulated, one is that the variation of base attitude is limited; the other is that both the base attitude and the joint rates are constrained. The simulation results verify the presented method.

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.

Space robotic systems are expected to play an increasingly important role in the future. The robotic on-orbital service, whose key is the capturing technology, becomes research hot in recent years. In this paper, the authors propose an autonomous path planning method for target capturing. The task is described in Cartesian space and it can drive the manipulator to approach the target along the closest path. Firstly, the target feature is extracted based on the measured information via the hand-eye camera, and the target pose (position and orientation) and velocities (linear velocity and angular velocity) are estimated using Kalman filtering technology. Then, a numerically feasible approach is presented to plan the manipulator motion and avoid the dynamic singularities, which are transformed into real-time kinematic singularities avoiding problem. Thirdly, the potential disturbance on the base due to the manipulator's motion is estimated, and the joint rates are autonomously adjusted to reduce the disturbance if it is beyond the allowed bound. At last, a ground experiment system is set up based on the concept of dynamic emulation and kinematic equivalence. With the experiment system, the autonomous target capturing experiments are conducted. The experiment results validate the proposed algorithm.

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.

Household service is one of the most important fields in robot applications. Compared with applications in industry, the household environment is more challenging. First, the household environment is usually unstructured, which makes it more difficult to handle simple tasks such as what the assembly line robot can easily achieve. Second, the object for household services is human, sometimes a small child. For safety reasons, the robot should be very careful when taking actions. Third, the household robot is expected to be cleverer in dealing with complicated situations, which requires flexibility in responding. All these challenges are, to some extent, related to the capability of perceiving various objects, from an old table to a sleeping baby. In this chapter, we discuss the state of the art in object recognition for household service.

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.

In this chapter, an efficient and low-cost cellphone-commandable mobile manipulation system is described. Aiming at home use and elderly caring, this system can be easily commanded through a common cellphone network to efficiently grasp objects in a household environment, utilizing several low-cost off-the-shelf devices. Unlike the visual servo technology using a high quality vision system with the associated high cost, the household-service robot would not be able to afford such a high quality vision servo system, and thus it is essential to use some low-cost devices. However, it is extremely challenging to create such a vision system with precise localization, as well as motion control. To tackle this challenge, we developed a real-time vision system with which a reliable grasping algorithm combining machine vision, robotic kinematics and motor control technology is presented. After the target is captured by the arm camera, the arm camera keeps tracking the target while the arm keeps stretching until the end effector reaches the target. However, if the target is not captured by the arm camera, the arm will make a move to help the arm camera capture the target under the guidance of the head camera. This algorithm is implemented on two robot systems: one with a fixed base and another with a mobile base. The results demonstrate the feasibility and efficiency of the algorithm and system we developed, and our study shows the significance of developing a service robot in a modern household environment.

For an aging population, surveillance in household environments has become more and more important. In this chapter, we present a household robot that can detect abnormal events by utilizing video and audio information. In our approach, moving targets can be detected by the robot using a passive acoustic location device. The robot then tracks the targets by employing a particle filter algorithm. To adapt to different lighting conditions, the target model is updated regularly based on an update mechanism. To ensure robust tracking, the robot detects abnormal human behavior by tracking the upper body of a person. For audio surveillance, Mel frequency cepstral coefficients is used to extract features from audio information. Those features are input to a support vector machine classifier for analysis. Experimental results show that the robot can detect abnormal behavior such as “falling down” and “running.” Also, an 88.17% accuracy rate is achieved in the detection of abnormal audio information like “crying,” “groaning,” and “gun shooting.” To lower the false alarms by the abnormal sound detection system, the passive acoustic location device directs the robot to the scene where the abnormal events are occuring and the robot can employ its camera to further confirm the occurrence of the events. At last, the robot will send the image captured by the robot to the mobile phone of its master.

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.

Technology for the capturing of space target is very important for on-orbit servicing. In order to assure the task is accomplished successfully, ground experimentations are required for the verification of the planning and control algorithms of space robotic system before it is launched. In this paper, an experiment concept is used, which is a hybrid approach, i.e. it combines the mathematical model with the physical model. The key issues of the concept are dynamic emulation and kinematic equivalence, in which the behaviors of the space robotic system are calculated by its dynamic equations. The motion of its end-effector and the space target is realized by two industrial robots. According to different observation spots, two modes of capturing process are emulated: one is observed from the inertial frame, the other is from the space base. Based on the concept proposed above, a ground experiment system is set up, which is composed of two industrial robots, a set of global visual system and five industrial computers. Using the system, algorithms of space robot of any geometry and mass properties can be tested. As an example, the autonomous trajectory planning algorithm is verified by the experiment of capturing a moving target. Moreover, a real-time 3D simulation system is developed to emulate the capturing process in 3D space. Numeric simulation and experiment results show that the ground system is effective in evaluating the planning and control algorithms of space robot.

In practice, the environments in which mobile robots operate are usually modeled in highly complex forms and, as a result, autonomous navigation and localization can be difficult. The difficulties are exacerbated for practical robots with limited on-board computational resources and complex planning algorithms, since this paradigm of environmental modeling requires enormous computational power. A novel navigation/localization learning methodology is presented to abstract and transfer the human sequential navigational skill to a robotic wheelchair by showing the platform how to respond in different local environments along a demonstrated, static cluttered route using a lookup table representation. This method utilizes limited on-board range sensing information to concisely model local unstructured environments, with respect to the robot, for navigation or localization along the learned route in order to achieve good performance with low on-line computational demand and low-cost hardware requirements. Experimental study demonstrates the feasibility of this method and some interesting characteristics of navigation, localization, and environmental modeling problems. Analysis is also conducted to investigate performance evaluation, advantages of the approach, choices of lookup table inputs and outputs, and potential generalization of this paper

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.

Finite-time control of the robot system is studied through both state feedback and dynamic output feedback control. The effectiveness of the proposed approach is illustrated by both theoretical analysis and computer simulation. In addition to offering an alternative approach for improving the design of the robot regulator, this research also extends the study of the finite-time control problem from second-order systems to a large class of higher order nonlinear systems.

Modeling human control strategy (HCS) is becoming an increasingly popular paradigm in a number of different research areas, ranging from robotics and intelligent vehicle highway systems to expert training and virtual reality computer games. Usually, HCS models are derived empirically, rather than analytically, from real-time human input–output data. While these empirical models offer an effective means of transferring intelligent behaviors from humans to robots and other machines, there is a great need to develop adequate performance criteria for these models. It is our goal in this paper to develop several such criteria for the task of human driving. We first collect driving data from different individuals through a real-time graphic driving simulator that we have developed, and identify each individual’s control strategy model through the flexible cascade neural network learning architecture. We then define performance measures for evaluating two aspects of the resultant HCS models. The first is based on event analysis, while the second is based on inherent analysis. Using the proposed performance criteria, we demonstrate the procedure for evaluating the relative skill of different HCS models. Finally, we propose an iterative algorithm for optimizing an initially stable HCS model with respect to independent, user-specified performance criteria, by applying the simultaneously perturbed stochastic approximation (SPSA) algorithm. The methods proposed herein offer a means for modeling and transferring HCS in response to real-time inputs, and improving the intelligent behaviors of artificial machines.

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.

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.

A single wheel, gyroscopically stabilized robot was developed to provide a dynamic stability for rapid locomotion. It is a sharp-edged wheel actuated by a spinning flywheel for steering and a drive motor for propulsion. The spinning flywheel acts as a gyroscope to stabilize the robot and it can be tilted to achieve steering. Its nature is nonholonomic, underactuated and inherently unstable in the lateral direction. In this paper, we discuss the problem of controlling the tilt-up motion of such a dynamically stable but statically unstable robot. We first present a dynamic model which is verified through simulations and experiments. Then, we develop a model-based controller for tilt-up motion, assuming that the robot is rolling without slipping. Taking advantage of human skill in teleoperating the robot, we develop a human-based controller for tilt-up motion, in which we train a human control strategy model to abstract the operator’s skill in controlling the tilt-up motion. The controller was successfully implemented, such that the robot can recover from fall automatically. This work is a significant step towards a fully automatic control of such a dynamically stable but statically unstable robot.

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.

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

This paper 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.

The underactuated free floating robot in space is nonlinear systems where velocity and acceleration constraints are both nonintegrable, therefore it is a secondorder nonholonomic system. Some of the existing nonholonomic control methods will not be directly applicable to such systems as it is extremely difficult, if not impossible, to find the control Lie brackets. In this paper, by investigating the system dynamics in depth, a simple velocity-based method is proposed to control the unactuated joints and a multi-step composite strategy to implement orientation tracking tasks. The proposed algorithm is of significance in controlling of space robots when some joints fail to function, or they are intentionally set to be passive for energy efficiency and safety purposes.

This paper presents a novel concept of mobility that provides dynamic stability and rapid locomotion. The concept, called Gyrover, is a single-wheel, gyroscopically stabilized robot that can be considered as a single wheel actuated by a spinning flywheel attached to a two-link manipulator at the wheel bearing and drive motor. The spinning flywheel acts as a gyroscope to stabilize the robot, and it can be tilted to achieve steering. This configuration conveys significant advantages over multiwheel, statically stable vehicles, including good dynamic stability and insensitivity to attitude disturbances, high maneuverability, low rolling resistance, ability to recover from falls, and amphibious capability. In this paper, the authors present the robot concept, three prototypes of the design, and system implementation. The authors study the robot’s nonholonomic constraints and the stabilizing effect of the flywheel on the system through simulation and experiments.

In this paper, we present a method for high-level control of robots whose low-level software is based on dynamically reconfigurable, reusable real-time software modules. Our approach is to use an embedded interpreter for a general-purpose programming language to direct the operation of the low-level modules toward meeting the task-level goals of the robot. To this end, we present RSK, a virtual-machine kernel implementing a Scheme interpreter capable of hard real-time operation, and employing a method of code execution we call “message-based evaluation” (MBE) . MBE is a novel combination of a traditional code execution model and a message-passing architecture, which simplifies the process of writing code for managing the robot‘s reconfigurable subsystem.

This article discusses the problem of controlling robot manipulators with passive joints, when the number of passive joints is larger than the number of active joints. Assuming that brakes and position sensors are available at each passive joint, we investigate the following issues: (1) what is a sufficient condition for controllability of the passive joints via dynamic coupling with the active joints and how can we quantify the controllability at a given configuration; (2) what is the optimal control and locking sequence of the passive joints; and (3) how can we control both passive and active joints to an equilibrium point in joint space. We propose an optimal control method and demonstrate its validity with both simulation and experimental results. The work presented here is significant because it provides a better understanding and a guideline for utilizing manipulators with passive joints for energy efficiency and fault-tolerant design in applications such as space robotics, hyperredundant robots, and sport mechanics. © 1998 John Wiley & Sons, Inc. 15: 115–129, 1998

The authors have developed a unique, single-wheel robot that exploits gyroscopic forces for steering and stability. Experiments with two working models show promise for the concept for high-speed, rough-terrain and amphibious applications.