Purpose This paper aims to present an open-architecture kinematic controller, which was developed for articulated robots, facing the demands of various applications and low cost on robot system. Design/methodology/approach A general approach to develop this controller is described in hardware and software design. The hardware consists of embedded boards and programable multi-axes controller (PMAC), connected with ethernet, and the software is implemented on a robot operating system with MoveIt!. The authors also developed a teach pendant running as a LAN node to provide a human–machine interface (HMI). Findings The proposed approach was applied to several real articulated robot systems and was proved to be effective and portable. The proposed controller was compared with several similar systems to verify its integrality and flexibility. The openness of this controller was discussed and is summarized at the end of this paper. Practical implications The proposed approach provided an open and low-complex solution for experimental studies in the lab and short-run production in small workshops. Originality/value Several contributions are made by the research. The actuation model and communication were implemented to integrate the trajectory planning module and PMAC for setting up the physical interface. Method and program interface based on kinematics was provided to generate various interpolations for trajectory planning. A teach pedant with HMI was developed for controlling and programing the robot.

Upper limb rehabilitation robotic systems have been studied for decades to improve the rehabilitation training of hemiplegic patients. Ideally such robotic systems should be capable of detecting the intentions of the patients and assisting them as needed. In this paper, we integrate a virtual environment and a low-cost motion sensor, the Microsoft Kinect II, into a robotic upper limb rehabilitation system to detect user motion intentions and generate motion commands for a rehabilitation robot. The system requires users to mimic a pre-programmed bimanual motion sequence shown in the virtual environment. A Gaussian Process based predictive controller uses the motion of the unaffected arm and the programmed motion sequence to estimate the motion intentions of the affected arm. We also adopt this controller in Mirror Therapy, a widely-practised therapeutic intervention method. Two preliminary experiments have been conducted to validate the proposed controller and test proper function and safety of the system including a patient case-study.

This paper intends to develop a novel planar hybridly actuated two-finger gripper with three phalanxes, namely a kind of gripper whose intermediate and distal phalanxes are underactuated through cable-pulley, and proximal phalanx is driven by a separate motor otherwise. Therefore, a dynamic analysis method based on virtual prototype technology and MAT-LAB is proposed for the underactuated gripper to complete optimal structural design. Firstly, the gripper model is simplified for dynamic analysis, and the structure design variables of the gripper are determined. Secondly, three kinds of performance indexes are defined for grasp quality, including stability, flexibility and adaptability. Moreover, a comprehensive evaluation index is proposed for the underactuated gripper in the cases of different driving forces, different grasped objects as well as different initial poses through combining different mechanical design parameters and carrying out dynamics simulation in a batch process. Thus, an optimal objective function with weights is designed for different grasping cases. Finally, a structure scheme of underactuated gripper suitable for expansion of the multi-finger hand is designed according to the requirements of outdoor sampling mission and the simulation results, in which the distance between the modular fingers is adjustable.

Aiming at searching the source of thermal radiation fields,nuclear radiation fields,strong electromagnetic radiation fields and sound fields in outdoor environment,a source searching algorithm based on Gaussian Process Regression using a single robot is proposed.First of all,the continuous searching area is discretized with square grids,and the radiation intensity of adjacent grids is predicted by Gaussian Process Regression.Then a local path planning is considered to be an optimization problem and the next path point is obtained by solving the problem.Finally,the robot is guided to the global optimal point through repeated local path planning.To verify the proposed algorithm,an experiment in an environment of 300m×200mhas been conducted.

The underactuated robotic hands is able to adapt to a wide variety of objects without a complex control structure, which makes it an apt end-effector for robotic systems working in unstructured environments, such as, rescue robotic systems, service robots and so on. The finger movements of an underactuated robotic hand is mainly determined by its mechanical design. Recent studies do not consider the movement of the object (to be grasped), which commonly occurs in real-world applications. Therefore, they cannot fully describe the performance of underactuated robotic hands. In order to handle this problem, we introduce the Permissible Initial Object Pose Error (PIOPE) as a performance metric of the underactuated robotic hands. Key aspects of dynamic simulation of under-actuated robotic hands are analyzed to define the design parameter of underactuated robotic hands. The PIOPE and other metrics are calculated with dynamic simulation to examine the influences of design parameters. The method is applied on a kind of underactuated robotic hand. Simulations are carried out to investigate the effects of design parameters and to design the parameters of the underactuated robotic hand.

In robot-assisted upper limb rehabilitation, detecting the intentions of hemiplegic patients is essential towards assisting the patients to actively exercise instead of driving passive motions. Many interactive channels, such as voice, EMG and EEG, have been studied to estimate the motion intentions. However, limitations of these techniques, such as high complexity, have constrained their applications in practice. In this paper, we integrate a virtual environment and a low-cost motion sensor into a novel control strategy to detect motion intentions for a rehabilitation robot. Several bimanual motion sequences are intuitively programmed by a professional therapist for subjects to repeat. The strategy uses the unaffected arm and the programmed motion sequence to estimate the motion intentions of the affected arm. We adopt this strategy in Mirror Therapy, a widely-practised therapeutic intervention method. Experiments have been conducted to validate the control strategy.

Robots have been applied to search for radioactive sources. Until now, however, it is still an open problem to accomplish this task by one autonomous robot. In this paper, we propose an algorithm based on Gaussian Process Regression (GPR) to search for a radioactive source in a discrete 2D environment. We predict the radiation intensities of 8 positions using GPR. We consider searching a radioactive source as an optimization problem so as to obtain target positions for a mobile robot. The objective function describes three aspects: the radiation intensity, the amount of information for GPR, and the environment with obstacles. Simulation experiments are conducted to verify the proposed algorithm. The simulation results show that our algorithm is effective and robust when searching radioactive sources located at different points in an indoor environment.

The kinematic modelling has been applied to many controllers of under-actuated manipulators. Most of these studies assume that the control process is conducted within the workspace. However, as such a kinematic model cannot describe the situations when the stable grasping is violated in the real environment, these controllers may fail unexpectedly. In this paper, we propose a combination of kinematics based Workspace Analysis (WA) and Gaussian Process Classification (GPC) to model the success rates of control actions in the theoretical workspace. We also use the Gaussian Process Regression (GPR) to model the residual between the prediction of the WA and the ground truth data. We then apply this integrated model, Gaussian Processes enhanced Workspace Analysis (GP-WA), into an optimal controller. The optimal controller is implemented on a planar under-actuated gripper with two three-phalanx fingers. Two sets of simulation experiments are carried out to validate our method. The results demonstrate that the optimal manipulation controller based on GP-WA achieves high control accuracy for manipulating a wide range of objects.

This paper presents a grasp planning method for grasping novel objects from point clouds provided by the Kinect camera. By applying machine learning, the planning method can generate two points which represent the contact point and direction of grasp. This method is based on three components: 1) grasp configuration which can present the location of contact points and the direction of grasp, 2) features which take force closure and grasp stability into account, and 3) Gaussian Process Classifier which is used to calculate the grasp quality by using the features of each grasp configuration. Two experiments are carried out to verify our method. The results demonstrate that the robot using this approach can successfully grasp objects with partial point clouds.

In this paper, we present a general approach to develop a kinematic controller for any serial robots using Programmable Multi-Axes Controller (PMAC) and MoveIt!. PMAC, a commercial product for motion control, is an all-in-one embedded system to control motion, amplify signals and acquire sensor information. MoveIt! is a state-of-the-art software for kinematics-based manipulations. It integrates many recent robotic advances, such as, Universal Robot Description File (URDF), Open Motion Planning Library (OPML) and so on. We will describe YARC (Yet Another Robot Controller), a principled way of integrating PMAC and MoveIt! together with a design of a smart control panel (TCPad). Several examples were demonstrated to show the functional superiority of this controller. The source code and hardware design have been made publicly accessible for the community.

Rescue missions after coal mine accidents are highly risky and sometimes impossible for rescuers to perform. To decrease the risk to rescuers, two generations of tracked mobile robots have been designed and developed to replace the rescuers. In this paper, we present the design iterations with experiments carried out in training sites for rescuers and in working coal mines, and we summarize the design and development experiences of the mobile robots for such rescue missions. In coal mine rescue robots, the explosion-proof and waterproof designs are adapted to the explosive and wet environments, while the suspension systems are adapted to the unstructured working environments. The design and development experiences may provide a reference for designing and developing future mobile robot systems for coal mine accident rescue missions.

In this paper, we present a general approach to develop a kinematic controller for any serial robots using Programmable Multi-Axes Controller (PMAC) and MoveIt!. PMAC, a commercial product for motion control, is an all-in-one embedded system to control motion, amplify signals and acquire sensor information. MoveIt! is a state-of-the-art software for kinematics-based manipulations. It integrates many recent robotic advances, such as, Universal Robot Description File (URDF), Open Motion Planning Library (OPML) and so on. We will describe YARC (Yet Another Robot Controller), a principled way of integrating PMAC and MoveIt! together with a design of a smart control panel (TCPad). Several examples were demonstrated to show the functional superiority of this controller. The source code and hardware design have been made publicly accessible for the community.

Virtual Reality (VR) presents a promising future in the field of rehabilitation due to its advantages brought to the training process as is indicated in many articles and researches. In this paper, we describe a novel method in developing a virtual training environment for a 5 degrees of freedom (DOF) upper limb rehabilitation robot which has been designed to help provide assistance for patients who survive stroke but remain hemiplegic to complete rehabilitation exercise. The method involved utilizes ROS (Robot Operating System) and Gazebo (a multi-robot simulator) to set up an interesting virtual scene of daily life in a 3D world to facilitate the patients to move their affected arms with synchronous visual feedback and interact with the virtual training task. The control method of human model in the virtual world and the communication mechanism between the host machine which controls movement of the real robot and the master machine which runs the VR will be introduced. Finally, a virtual training environment containing reaching task set in a modern kitchen is presented.

A Piezo Tip/Tilt Platform (PT2P) is presented with its structure, kinematics, and preliminary experiments. Two essential models of the presented PT2P, an equivalent hinge of the flexure hinge and a simplified model of the transmission mechanism, are discussed with the analysis on the structure of the PT2P. Based on these models, the inverse kinematics of the PT2P is derived. Two experiments are conducted on a prototype of the PT2P. The kinematic model is verified with experimental results, which also indicate that the resolution and the repeatability of the PT2P is, respectively, better than 0.50 μrad and 0.25 μrad.

Many corporations have produced various teach pendants. However, most of these products are exclusive in terms of communication protocols and robot programming languages. Therefore, programs and teach pendant are usually not exchangeable between robots. To tackle this problem, we present U-Pendant, an open source universal teach pendant for serial robots based on Robot Operating System (ROS), a meta-operating system with a number of robotic applications shared online, and Yet Another Robot Controller (YARC), a universal kinematic controller for serial robots. We design and implement essential functionality and interactivity of teach pendants on U-Pendant. Several demonstrations are made to show the universality and functional superiority of U-Pendant. The source code and hardware design have been made publicly accessible for the community.

Many recent studies describe micromanipulation systems by using complex Analytic Forward Models (AFM), but such models are difficult to build and incapable of describing unmodelable factors, such as manufacturing defects. In this work, we propose the Enhanced Analytic Forward Model (EAFM), an integrated model of the AFM and the Heteroscedastic Gaussian Processes (HGP). The EAFM can compensate the shortfalls of the AFM by training the HGP on the residual of the AFM. This also allows the HGP to learn the repeatability of the micromanipulation system. Based on the EAFM, we further contribute an optimal position controller for improving the accuracy and the repeatability. This optimal EAFM controller is implemented and tested on a three degree-of-freedom micromanipulator based micromanipulation system. Two sets of real-world experiments are carried out to verify our method. The results demonstrate that the controller using EAFM can statistically achieve higher accuracy and repeatability than solely using the AFM.

Robot learning by demonstration is key to bringing robots into daily social environments to interact with and learn from human and other agents. However, teaching a robot to acquire new knowledge is a tedious and repetitive process and often restrictive to a specific setup of the environment. We propose a template-based learning framework for robot learning by demonstration to address both generalisation and adaptability. This novel framework is based upon a one-shot learning model integrated with spectral clustering and an online learning model to learn and adapt actions in similar scenarios. A set of statistical experiments is used to benchmark the framework components and shows that this approach requires no extensive training for generalisation and can adapt to environmental changes flexibly. Two real-world applications of an iCub humanoid robot playing the tic-tac-toe game and soldering a circuit board are used to demonstrate the relative merits of the framework.

This paper describes a syntactic approach to imitation learning that captures important task structures in the form of probabilistic activity grammars from a reasonably small number of samples under noisy conditions. We show that these learned grammars can be recursively applied to help recognize unforeseen, more complicated tasks that share underlying structures. The grammars enforce an observation to be consistent with the previously observed behaviors which can correct unexpected, out-of-context actions due to errors of the observer and/or demonstrator. To achieve this goal, our method (1) actively searches for frequently occurring action symbols that are subsets of input samples to uncover the hierarchical structure of the demonstration, and (2) considers the uncertainties of input symbols due to imperfect low-level detectors. We evaluate the proposed method using both synthetic data and two sets of real-world humanoid robot experiments. In our Towers of Hanoi experiment, the robot learns the important constraints of the puzzle after observing demonstrators solving it. In our Dance Imitation experiment, the robot learns 3 types of dances from human demonstrations. The results suggest that under reasonable amount of noise, our method is capable of capturing the reusable task structures and generalizing them to cope with recursions.