Hongbin Ma

Neural-Learning-Based Telerobot Control With Guaranteed Performance

In this paper, a neural networks (NNs) enhanced telerobot control system is designed and tested on a Baxter robot. Guaranteed performance of the telerobot control system is achieved at both kinematic and dynamic levels. At kinematic level, automatic collision avoidance is achieved by the control design at the kinematic level exploiting the joint space redundancy, thus the human operator would be able to only concentrate on motion of robot’s end-effector without concern on possible collision. A posture restoration scheme is also integrated based on a simulated parallel system to enable the manipulator restore back to the natural posture in the absence of obstacles. At dynamic level, adaptive control using radial basis function NNs is developed to compensate for the effect caused by the internal and external uncertainties, e.g., unknown payload. Both the steady state and the transient performance are guaranteed to satisfy a prescribed performance requirement. Comparative experiments have been performed to test the effectiveness and to demonstrate the guaranteed performance of the proposed methods.

Teleoperation of humanoid baxter robot using haptic feedback.

This paper presents a teleoperation strategy, which is featured by haptic feedback. The teleoperation system is composed of a SensAble® Omni haptic device, set as the master and providing haptic feedback, and an anthropomorphic robot slave, which is embodied by the 7-DOF (degrees of freedom) robotic arm of the Baxter® robot. The haptic feedback enables a bilateral manipulation of the teleoperation system. The joint angles and Cartesian position of the stylus of the Omni device are sampled and transferred to the slave, determining its motion. Meanwhile a force, proportional to the amplitude of position error of the slave manipulator, is sent back to the master and applied to the stylus. Hereby, the operator can sense the motion of the Baxter robot and adjust its manipulator accordingly. The kinematics of the master and slave have been analysed and a workspace mapping has been realized. Two methods, direct angle mapping and CLIK (closed-loop inverse kinematics), are used to implement the manipulation of the slave in position-position mode. Two experiments have been designed and tested to verify the validity of the methods provided by this paper. The results of the experiments illustrate that the designed teleoperation system is feasible and effective.

Novel Hybrid Adaptive Controller for Manipulation in Complex Perturbation Environments

In this paper we present a hybrid control scheme, combining the advantages of task-space and joint-space control. The controller is based on a human-like adaptive design, which minimises both control effort and tracking error. Our novel hybrid adaptive controller has been tested in extensive simulations, in a scenario where a Baxter robot manipulator is affected by external disturbances in the form of interaction with the environment and tool-like end-effector perturbations. The results demonstrated improved performance in the hybrid controller over both of its component parts. In addition, we introduce a novel method for online adaptation of learning parameters, using the fuzzy control formalism to utilise expert knowledge from the experimenter. This mechanism of meta-learning induces further improvement in performance and avoids the need for tuning through trial testing.

Teleoperation of a virtual iCub robot under framework of parallel system via hand gesture recognition

This paper describes our preliminary development of a virtual robot teleoperation platform based on hand gesture recognition using visual information. Hand gestures in images captured by a camera are recognised to control a virtual iCub. We employ two methods to realise the classification: Adaptive Neuro-fuzzy Inference Systems (ANFIS) and Support Vector Machines (SVM). We realise the teleoperation of a virtual robot using iCubSimulator. The technique in the paper will enable us to teleoperate a physical robot in the future work. In addition, a video server is set up to monitor the real robot. By using the parallel system we are able to improve the robots performance. Based on the techniques presented in this paper, the virtual iCub can perform the specified actions remotely in a natural manner.

A Review of Fuzzy Logic and Neural Network Based Intelligent Control Design for Discrete-Time Systems

Over the last few decades, the intelligent control methods such as fuzzy logic control (FLC) and neural network (NN) control have been successfully used in various applications. The rapid development of digital computer based control systems requires control signals to be calculated in a digital or discrete-time form. In this background, the intelligent control methods developed for discrete-time systems have drawn great attentions. This survey aims to present a summary of the state of the art of the design of FLC and NN-based intelligent control for discrete-time systems. For discrete-time FLC systems, numerous remarkable design approaches are introduced and a series of efficient methods to deal with the robustness, stability, and time delay of FLC discrete-time systems are recommended. Techniques for NN-based intelligent control for discrete-time systems, such as adaptive methods and adaptive dynamic programming approaches, are also reviewed. Overall, this paper is devoted to make a brief summary for recent progresses in FLC and NN-based intelligent control design for discrete-time systems as well as to present our thoughts and considerations of recent trends and potential research directions in this area.

Hand Gesture Based Robot Control System Using Leap Motion

Gesture based human-robot interface is a highly efficient robot control strategy for its simple operation and high availability. This paper develops a hand gesture based robot control system using Leap Motion. The process that the robot responds to humans hand gesture contains noise suppression, coordinate transformation and inverse kinematics. A Client/Server structured robot control system is developed, which provides the function of controlling virtual universal robot UR10 with hand gesture. Finally, experimental results demonstrate that the system is effective and practical.

Robot manipulator self-identification for surrounding obstacle detection

Obstacle detection plays an important role for robot collision avoidance and motion planning. This paper focuses on the study of the collision prediction of a dual-arm robot based on a 3D point cloud. Firstly, a self-identification method is presented based on the over-segmentation approach and the forward kinematic model of the robot. Secondly, a simplified 3D model of the robot is generated using the segmented point cloud. Finally, a collision prediction algorithm is proposed to estimate the collision parameters in real-time. Experimental studies using the KinectⓇ sensor and the BaxterⓇ robot have been performed to demonstrate the performance of the proposed algorithms.

Discrete-time adaptive control of robot manipulator with payload uncertainties

In this paper, a new discrete-time adaptive control scheme for controlling robot manipulators is proposed. The objective is to control position of a robot manipulator end effector in the presence of uncertainties caused by unknown fixed or time-varying payload. For simplicity, the unknown payload is considered as the only unknown factor and the data in use is sampled from the true continuous-time plant with constant fixed sampling interval. We estimate the payload according to the available history information and design a discrete-time adaptive controller based on the estimation of the external payload. The adaptive estimator adopted in the adaptive controller only uses one step history and is capable of fast adaptation. The simulation results demonstrated that the new controller can yield a satisfactory tracking performance in the presence of payload uncertainties.

Three-robot minimax travel-distance optimal formation

Multi-robot formation problem has received increasing attention due to its wide applications such as surveillances and various services. To illustrate a novel framework on optimal multi-robot formation given in our previous work, which aims to answer the long-term ignored fundamental problem of describing the formation and clarifying optimal formation rigorously in a mathematical manner, as a preliminary case study, various cases of the simplest optimal line formation of three robots, i.e. minimax travel-distance line formation problems, where each robot admits to move with the same constant speed along any chosen direction and the three-robot team aims to row on a straight line with the minimum maximal travel distance, are investigated in this note. Such problems look like very easy to resolve, however, to our surprise, mathematical results for these cases established with geometric analysis and inequalities have shown the non-trivialness of the most simple optimal line formation problem. Extensive simulations have also been conducted and briefly reported in this contribution, and these experimental results are found to coincide with the established theoretical results.

Multi-lanes detection based on panoramic camera

The lane detection system is one of the most important subsystems to achieve the environmental perception of autonomous vehicles. This paper addresses the problem of detecting multiple lanes in a relatively large range around the autonomous vehicle without making too much approximation on the shape of the lane marks. A panoramic camera and a LIDAR is used to obtain a wide range of image information and exclude part of the interference information. The vertical lane marks with specific width is extracted by a filter with 2D Gaussian kernel. In order to describe the lanes with relatively complex shapes like variable curvature curves, the model of equidistant curves is proposed and a robust detection method of equidistant curves is designed under the guidance of the lane model. Satisfactory experimental results in diverse environment and the successful application on autonomous vehicles demonstrate the effectiveness of the proposed method.