Real-time accurate hand path tracking and joint trajectory planning for industrial robots(Ⅰ)

Previously, researchers raised the accuracy for a robot′s hand to track a specified path in Car-tesian space mainly through increasing the number of knots on the path and the number of the path′s segments, which results in the heavier online computational burden for the robot controller. Aiming at overcoming this drawback, the authors propose a new kind of real-time accurate hand path tracking and joint trajectory planning method. Through selecting some extra knots on the specified hand path by a certain rule and introducing a sinusoidal function to the joint displacement equation of each segment, this method can greatly raise the path tracking accuracy of robot′s hand and does not change the number of the path′s segments. It also does not increase markedly the computational burden of robot controller. The result of simulation indicates that this method is very effective, and has important value in increasing the application of industrial robots.

Real-time accurate hand path tracking and joint trajectory planning for industrial robots(Ⅱ)

Previously, researchers raised the accuracy for a robot′s hand to track a specified path in Cartesian space mainly through increasing the number of knots on the path and the segments of the path. But, this method resulted in the heavier on line computational burden for the robot controller. In this paper, aiming at this drawback, the authors propose a new kind of real time accurate hand path tracking and joint trajectory planning method for robots. Through selecting some extra knots on the specified hand path by a certain rule, which enables the number of knots on each segment to increase from two to four, and through introducing a sinusoidal function and a cosinoidal function to the joint displacement equation of each segment, this method can raise the path tracking accuracy of robot′s hand greatly but does not increase the computational burden of robot controller markedly.

REAL-TIME TRACKING FOR FAST MOVING OBJECT ON COMPLEX BACKGROUND

A real-time tracking system for the fast moving object on the complex background is proposed.The Markov random filed（MRF）model based background subtraction algorithm is used to detect the changing pixels and track the moving object.The prior probability of the segmentation mask is modeled by using MRF,and the object tracking task is translated into the maximum a-posterior（MAP）problem.Experimental results show that the method is efficient at both offline and online moving objects on simple and complex background.

Adaptive Trajectory Tracking Control for Nonholonomic Wheeled Mobile Robots:A Barrier Function Sliding Mode Approach

The trajectory tracking control performance of nonholonomic wheeled mobile robots(NWMRs)is subject to nonholonomic constraints,system uncertainties,and external disturbances.This paper proposes a barrier function-based adaptive sliding mode control(BFASMC)method to provide high-precision,fast-response performance and robustness for NWMRs.Compared with the conventional adaptive sliding mode control,the proposed control strategy can guarantee that the sliding mode variables converge to a predefined neighborhood of origin with a predefined reaching time independent of the prior knowledge of the uncertainties and disturbances bounds.Another advantage of the proposed algorithm is that the control gains can be adaptively adjusted to follow the disturbances amplitudes thanks to the barrier function.The benefit is that the overestimation of control gain can be eliminated,resulting in chattering reduction.Moreover,a modified barrier function-like control gain is employed to prevent the input saturation problem due to the physical limit of the actuator.The stability analysis and comparative experiments demonstrate that the proposed BFASMC can ensure the prespecified convergence performance of the NWMR system output variables and strong robustness against uncertainties/disturbances.

Adaptive state-constrained/model-free iterative sliding mode control for aerial robot trajectory tracking

This paper develops a novel hierarchical control strategy for improving the trajectory tracking capability of aerial robots under parameter uncertainties.The hierarchical control strategy is composed of an adaptive sliding mode controller and a model-free iterative sliding mode controller(MFISMC).A position controller is designed based on adaptive sliding mode control(SMC)to safely drive the aerial robot and ensure fast state convergence under external disturbances.Additionally,the MFISMC acts as an attitude controller to estimate the unmodeled dynamics without detailed knowledge of aerial robots.Then,the adaption laws are derived with the Lyapunov theory to guarantee the asymptotic tracking of the system state.Finally,to demonstrate the performance and robustness of the proposed control strategy,numerical simulations are carried out,which are also compared with other conventional strategies,such as proportional-integralderivative(PID),backstepping(BS),and SMC.The simulation results indicate that the proposed hierarchical control strategy can fulfill zero steady-state error and achieve faster convergence compared with conventional strategies.

Trajectory Tracking for MmWave Communication Systems via Cooperative Passive Sensing

A cooperative passive sensing framework for millimeter wave(mmWave)communication systems is proposed and demonstrated in a scenario with one mobile signal blocker.Specifically,in the uplink communication with at least two transmitters,a cooperative detection method is proposed for the receiver to track the blocker’s trajectory,localize the transmitters and detect the potential link blockage jointly.To facilitate detection,the receiver collects the signal of each transmitter along a line-of-sight(LoS)path and a non-line-of-sight(NLoS)path separately via two narrow-beam phased arrays.The NLoS path involves scattering at the mobile blocker,allowing its identification through the Doppler frequency.By comparing the received signals of both paths,the Doppler frequency and angle-of-arrival(AoA)of the NLoS path can be estimated.To resolve the blocker’s trajectory and the transmitters’locations,the receiver should continuously track the mobile blocker to accumulate sufficient numbers of the Doppler frequency and AoA versus time observations.Finally,a gradient-descent-based algorithm is proposed for joint detection.With the reconstructed trajectory,the potential link blockage can be predicted.It is demonstrated that the system can achieve decimeterlevel localization and trajectory estimation,and predict the blockage time with an error of less than 0.1 s.

Adaptive Robust Control with Leakage-Type Control Law for Trajectory Tracking of Exoskeleton Robots

This paper investigates the trajectory following problem of exoskeleton robots with numerous constraints. However, as a typical nonlinear system with variability and parameter uncertainty, it is difficult to accurately achieve the trajectory tracking control for exoskeletons. In this paper, we present a robust control of trajectory tracking control based on servo constraints. Firstly, we consider the uncertainties (e.g., modelling errors, initial condition deviations, structural vibrations, and other unknown external disturbances) in the exoskeleton system, which are time-varying and bounded. Secondly, we establish the dynamic model and formulate a close-loop connection between the dynamic model and the real world. Then, the trajectory tracking issue is regarded as a servo constraint problem, and an adaptive robust control with leakage-type adaptive law is proposed with the guaranteed Lyapunov stability. Finally, we conduct numerical simulations to verify the performance of the proposed controller.

Novel Real-Time Seam Tracking Algorithm Based on Vector Angle and Least Square Method

Real-time seam tracking can improve welding quality and enhance welding efficiency during the welding process in automobile manufacturing.However,the teaching-playing welding process,an off-line seam tracking method,is still dominant in automobile industry,which is less flexible when welding objects or situation change.A novel real-time algorithm consisting of seam detection and generation is proposed to track seam.Using captured 3D points,space vectors were created between two adjacent points along each laser line and then a vector angle based algorithm was developed to detect target points on the seam.Least square method was used to fit target points to a welding trajectory for seam tracking.Furthermore,the real-time seam tracking process was simulated in MATLAB/Simulink.The trend of joint angles vs.time was logged and a comparison between the off-line and the proposed seam tracking algorithm was conducted.Results show that the proposed real-time seam tracking algorithm can work in a real-time scenario and have high accuracy in welding point positioning.

A machine vision approach to seam tracking in real-time in PAW of large-diameter stainless steel tube

Manual monitoring and seam tracking through watching weld pool images in real-time, by naked eyes or by industrial TV, are experience-depended, subjective, labor intensive, and sometimes biased. So it is necessary to realize the automation of computer-aided seam tracking. A PAW (plasma arc welding) seam tracking system was developed, which senses the molten pool and the seam in one frame by a vision sensor, and then detects the seam deviation to adjust the work piece motion adaptively to the seam position sensed by vision sensor. A novel molten pool area image-processing algorithm based on machine vision was proposed. The algorithm processes each image at the speed of 20 frames/second in real-time to extract three feature variables to get the seam deviation. It is proved experimentally that the algorithm is very fast and effective. Issues related to the algorithm are also discussed.

Real-Time Front Vehicle Detection Algorithm Based on Local Feature Tracking Method

This paper proposes an algorithm that extracts features of back side of the vehicle and detects the front vehicle in real-time by local feature tracking of vehicle in the continuous images.The features in back side of the vehicle are vertical and horizontal edges,shadow and symmetry.By comparing local features using the fixed window size,the features in the continuous images are tracked.A robust and fast Haarlike mask is used for detecting vertical and horizontal edges,and shadow is extracted by histogram equalization,and the sliding window method is used to compare both side templates of the detected candidates for extracting symmetry.The features for tracking are vertical edges,and histogram is used to compare location of the peak and magnitude of the edges.The method using local feature tracking in the continuous images is more robust for detecting vehicle than the method using single image,and the proposed algorithm is evaluated by continuous images obtained on the expressway and downtown.And it can be performed on real-time through applying it to the embedded system.

A Real-time Face/Hand Tracking Method for Chinese Sign Language Recognition

This paper introduces a new Chinese Sign Language recognition (CSLR) system and a method of real time tracking face and hand applied in the system. In the method, an improved agent algorithm is used to extract the region of face and hand and track them. Kalman filter is introduced to forecast the position and rectangle of search, and self adapting of target color is designed to counteract the effect of illumination.

Real-time tracking of deformable objects based on MOK algorithm

The traditional oriented FAST and rotated BRIEF（ORB） algorithm has problems of instability and repetition of keypoints and it does not possess scale invariance. In order to deal with these drawbacks, a modified ORB（MORB） algorithm is proposed. In order to improve the precision of matching and tracking, this paper puts forward an MOK algorithm that fuses MORB and Kanade-Lucas-Tomasi（KLT）. By using Kalman, the object＇s state in the next frame is predicted in order to reduce the size of search window and improve the real-time performance of object tracking. The experimental results show that the MOK algorithm can accurately track objects with deformation or with background clutters, exhibiting higher robustness and accuracy on diverse datasets. Also, the MOK algorithm has a good real-time performance with the average frame rate reaching 90.8 fps.

A Back-stepping Based Trajectory Tracking Controller for a Non-chained Nonholonomic Spherical Robot

Spherical robot has good static and dynamic stability, which provides it with strong viability in hostile environment, but the lack of effective control methods has hindered its application and development. This article deals with the dynamic trajectory tracking problem of the spherical robot BHQ-2 designed for unmanned environment exploration. The dynamic model of the spherical robot is established with a simplified Boltzmann-Hamel equation, based on which a trajectory tracking controller is designed by using the back-stepping method. The convergence of the controller is proved with the Lyapunov stability theory. Numerical simulations show that with the controller the robot can globally and asymptotically track desired trajectories, both linear and circular.

An adaptive switching control approach for trajectory tracking of robotic manipulators

In order to design a suitable controller which can achieve accurate trajectory tracking and a good control performance, and guarantee the stability and robustness of a robot system due to external disturbances error and internal parameter variations, an adaptive switching control strategy is proposed. The proposed scheme is designed under the condition of bounded distances and consists of an adaptive switching law and a PD controller. Based on the Lyapunov stability theory, it is proved that the proposed scheme can guarantee the tracking performance of the robotic manipulator and is adapted to varying unknown loads. Simulations are carded out on a two-link robotic manipulator, which illustrate the feasibility and validity of the proposed control scheme and the robustness for variational payloads.

Trajectory Tracking of Autonomous Vehicle with the Fusion of DYC and Longitudinal–Lateral Control

The current research of autonomous vehicle motion control mainly focuses on trajectory tracking and velocity tracking. However, numerous studies deal with trajectory tracking and velocity tracking separately, and the yaw stability is seldom considered during trajectory tracking. In this research, a combination of the longitudinal–lateral control method with the yaw stability in the trajectory tracking for autonomous vehicles is studied. Based on the vehicle dynamics, considering the longitudinal and lateral motion of the vehicle, the velocity tracking and trajectory tracking problems can be attributed to the longitudinal and lateral control. A sliding mode variable structure control method is used in the longitudinal control. The total driving force is obtained from the velocity error in order to carry out velocity tracking. A linear time-varying model predictive control method is used in the lateral control to predict the required front wheel angle for trajectory tracking. Furthermore, a combined control framework is established to control the longitudinal and lateral motions and improve the reliability of the longitudinal and lateral direction control. On this basis, the driving force of a tire is allocated reasonably by using the direct yaw moment control, which ensures good yaw stability of the vehicle when tracking the trajectory. Simulation results indicate that the proposed control strategy is good in tracking the reference velocity and trajectory and improves the performance of the stability of the vehicle.

Adaptive Trajectory Tracking Control for a Nonholonomic Mobile Robot

As one of the core issues of the mobile robot motion control, trajectory tracking has received extensive attention. At present, the solution of the problem only takes kinematic or dynamic model into account separately, so that the presented strategy is difficult to realize satisfactory tracking quality in practical application. Considering the unknown parameters of two models, this paper presents an adaptive controller for solving the trajectory tracking problem of a mobile robot. Firstly, an adaptive kinematic controller utilized to generate the command of velocity is designed based on Backstepping method. Then, in order to make the real velocity of mobile robot reach the desired velocity asymptotically, a dynamic adaptive controller is proposed adopting reference model and Lyapunov stability theory. Finally, through simulating typical trajectories including circular trajectory, fold line and parabola trajectory in normal and perturbed cases, the results illustrate that the control scheme can solve the tracking problem effectively. The proposed control law, which can tune the kinematic and dynamic model parameters online and overcome external disturbances, provides a novel method for improving trajectory tracking performance of the mobile robot.

Stabilization and trajectory tracking of autonomous airship's planar motion

The stabilization and trajectory tracking problems of autonomous airship＇s planar motion are studied. By defining novel configuration error and velocity error, the dynamics of error systems are derived. By applying Lyapunov stability method, the state feedback control laws are designed and the close-loop error systems are proved to be uniformly asymptotically stable by Matrosov theorem. In particular, the controller does not need knowledge on system parameters in the case of set-point stabilization, which makes the controller robust with respect to parameter uncertainty. Numerical simulations illustrate the effectiveness of the controller designed.

Neural Network Based Robust Controller for Trajectory Tracking of Underwater Vehicles

A robust neural network controller （NNC） is presented for tracking control of underwater vehicles with uncertainties. The controller is obtained by using backstepping technique and Lyapunov function design in combination with neural network identification. Modeling errors and environmental disturbances are considered in the mathematical model. A twolayer neural network is introduced to compensate the modeling errors, while H∞ control strategy is used to achieve the L2-gain performance. The uniformly ultimately bounded （UUB） stabilities of tracking errors and NN weights are guaran- teed through the proposed controller. An on-line NN weights tuning algorithm is also propesed. Good performances of the tracking control system are illustrated bv the results of numerical simulations.

Optimal Control of a Mackerel-Mimicking Robot for Energy Efficient Trajectory Tracking

A robotic fish, BASEMACK1, is designed and fabricated by mimicking the shape of a live mackerel. Three DC servo-motors are serially linked together and actuated to mimic the mackerel＇s Carangiform motion. Hydrodynamic characteristics of a fish-mimetic test model are experimentally identified and utilized in order to numerically simulate fish swimming. The discrete set of kinematic and dynamic parameters are obtained by considering required horizontal and lateral forces and minimum energy consumption. Using the optimized parameter set, optimal control of the robot is studied.

Modeling and Trajectory Tracking Control for Flapping-Wing Micro Aerial Vehicles

This paper studies the trajectory tracking problem of flapping-wing micro aerial vehicles(FWMAVs)in the longitudinal plane.First of all,the kinematics and dynamics of the FWMAV are established,wherein the aerodynamic force and torque generated by flapping wings and the tail wing are explicitly formulated with respect to the flapping frequency of the wings and the degree of tail wing inclination.To achieve autonomous tracking,an adaptive control scheme is proposed under the hierarchical framework.Specifically,a bounded position controller with hyperbolic tangent functions is designed to produce the desired aerodynamic force,and a pitch command is extracted from the designed position controller.Next,an adaptive attitude controller is designed to track the extracted pitch command,where a radial basis function neural network is introduced to approximate the unknown aerodynamic perturbation torque.Finally,the flapping frequency of the wings and the degree of tail wing inclination are calculated from the designed position and attitude controllers,respectively.In terms of Lyapunov's direct method,it is shown that the tracking errors are bounded and ultimately converge to a small neighborhood around the origin.Simulations are carried out to verify the effectiveness of the proposed control scheme.