Real-time Trajectory Tracking Research Articles

Adaptive Swarm Fuzzy Logic Controller of Multi-Joint Lower Limb Assistive Robot

The idea of developing a multi-joint rehabilitation robot is to satisfy the demands for recovery of lower limb functionality in hemiplegic impairments and assist the physiotherapists with their therapy plans. This work aims at to implement the Lyapunov Adaptive and Swarm-Fuzzy Logic Control (LASFC) strategy of 4-degree of freedom (4-DoF) Lower Limb Assistive Robot (LLAR) application, in which the control law is an integration of swarm-fuzzy logic control (SFLC) and Lyapunov adaptive control (LAC) with particle swarm optimization (PSO). The controller is established based on the sliding filtered steady-state error for SFLC. Its parameters are tuned by using PSO for the mathematical model of LLAR. The fuzzy defuzzification membership is set based on the tuned parameters for the real-time control system. LAC strategy is determined using stability analysis of the system to choose the controller’s parameters by observation of the system’s output and reference. The control law implemented in LLAR is the integration of SFLC and LAC to adjust the input voltage of joints. The parameters tuned by PSO are compared with the genetic algorithm (GA) statistically. In addition, the real-time trajectory tracking of the proposed controller for each joint is compared with LAC and SFLC separately. The experiment revealed that the LASFC has superior performance to the other two methods in trajectory tracking. For example, the average error for left hip by LASFC is 53.57% and 68% lower than SFLC and LAC, respectively. By the statistical analysis, it can be ascertained that the LASFC strategy performed efficiently for real-time control of the joint trajectory tracking.

The 3D Deburring Processing Trajectory Recognition Method and Its Application Base on Random Sample Consensus

As of 2022, most automatic deburring trajectories are still generated using offline programming methods. The trajectories generated using these methods are often suboptimal, which limits the precision of the robotic arms used to perform automatic deburring and, in turn, results in workpiece dimensional errors. Therefore, despite advances in automated deburring trajectory generation, deburring is still mostly performed manually. However, manual deburring is a time-consuming, labor-intensive, and expensive process that results in small profit margins for organizational equipment manufacturers (OEMs). To address these problems and the obstacles to the implementation of automated deburring in the robotics industry, the present study developed an online automated deburring trajectory generation method that uses 2D contouring information obtained from linear contour scanning sensors, a CAD model, and curve fitting to detect burrs and generate appropriate trajectories. The method overcomes many of the limitations of common deburring methods, especially by enabling real-time trajectory tracking. When the method was tested using bicycle forks, work that originally took three to four people 8–12-h to complete was completed by one person in 30 min, and the production cost was reduced by 70%.

Joint-Space Kinematic Control of a Bionic Continuum Manipulator in Real-Time by Using Hybrid Approach

Continuum manipulators are a type of robot used for delicate applications, including safe human-robot interactions. Controlling these manipulators for an accurate trajectory, especially in the case of pneumatic actuation, is extremely challenging. Thus, this article proposes a real-time kinematic trajectory control of a pneumatically actuated multi-segment bionic continuum manipulator with a mobile base by combining a neural network and analytical model with a cascaded controller to overcome this challenge. The inverse kinematics solution of the multi-segment manipulator is developed by using a neural network and an inverse piecewise constant curvature approach. The neural network is trained by using a separate learning algorithm. Although hybrid inverse modeling gives better solutions than existing techniques, significant residual positional error of the manipulator tip remains due to inherent material hysteresis. Thus, a cascaded PI-controller is utilized to compensate for the residual positional error. The controller gains are updated in each step by predictions of the actuator length, where the Jacobian entries are computed from the neural network model. The proposed procedure is validated on Festo Didactics’ elephant trunk-like two-segment continuum manipulator, Robotino-XT. Three different cases are considered for real-time trajectory tracking, where the OptiTrack vision system is used for validation by tracking the manipulator tip pose. For the trajectory points outside the manipulator workspace, simultaneous trunk and base movements are used. In experimental validation, the proposed scheme is shown to give much reduced manipulator tip trajectory error as compared to the existing methods.

Exploring the Risky Travel Area and Behavior of Car-hailing Service

Recent years have witnessed the rapid development of car-hailing services, which provide a convenient approach for connecting passengers and local drivers using their personal vehicles. At the same time, the concern on passenger safety has gradually emerged and attracted more and more attention. While car-hailing service providers have made considerable efforts on developing real-time trajectory tracking systems and alarm mechanisms, most of them only focus on providing rescue-supporting information rather than preventing potential crimes. Recently, the newly available large-scale car-hailing order data have provided an unparalleled chance for researchers to explore the risky travel area and behavior of car-hailing services, which can be used for building an intelligent crime early warning system. To this end, in this article, we propose a Risky Area and Risky Behavior Evaluation System (RARBEs) based on the real-world car-hailing order data. In RARBEs, we first mine massive multi-source urban data and train an effective area risk prediction model, which estimates area risk at the urban block level. Then, we propose a transverse and longitudinal double detection method, which estimates behavior risk based on two aspects, including fraud trajectory recognition and fraud patterns mining. In particular, we creatively propose a bipartite graph-based algorithm to model the implicit relationship between areas and behaviors, which collaboratively adjusts area risk and behavior risk estimation based on random walk regularization. Finally, extensive experiments on multi-source real-world urban data clearly validate the effectiveness and efficiency of our system.

Real-time trajectory tracking control of Stewart platform using fractional order fuzzy PID controller optimized by particle swarm algorithm

PurposeThe purpose of this paper is to address a fractional order fuzzy PID (FOFPID) control approach for solving the problem of enhancing high precision tracking performance and robustness against to different reference trajectories of a 6-DOF Stewart Platform (SP) in joint space.Design/methodology/approachFor the optimal design of the proposed control approach, tuning of the controller parameters including membership functions and input-output scaling factors along with the fractional order rate of error and fractional order integral of control signal is tuned with off-line by using particle swarm optimization (PSO) algorithm. For achieving this off-line optimization in the simulation environment, very accurate dynamic model of SP which has more complicated dynamical characteristics is required. Therefore, the coupling dynamic model of multi-rigid-body system is developed by Lagrange-Euler approach. For completeness, the mathematical model of the actuators is established and integrated with the dynamic model of SP mechanical system to state electromechanical coupling dynamic model. To study the validness of the proposed FOFPID controller, using this accurate dynamic model of the SP, other published control approaches such as the PID control, FOPID control and fuzzy PID control are also optimized with PSO in simulation environment. To compare trajectory tracking performance and effectiveness of the tuned controllers, the real time validation trajectory tracking experiments are conducted using the experimental setup of the SP by applying the optimum parameters of the controllers. The credibility of the results obtained with the controllers tuned in simulation environment is examined using statistical analysis.FindingsThe experimental results clearly demonstrate that the proposed optimal FOFPID controller can improve the control performance and reduce reference trajectory tracking errors of the SP. Also, the proposed PSO optimized FOFPID control strategy outperforms other control schemes in terms of the different difficulty levels of the given trajectories.Originality/valueTo the best of the authors’ knowledge, such a motion controller incorporating the fractional order approach to the fuzzy is first time applied in trajectory tracking control of SP.

A single camera 360‐degree real time vision‐based localization method with application to mobile robot trajectory tracking

A method is proposed for real-time vision-based localization in the 360° area around a three-dimensional (3D) reference object with a single camera. The problem is split into three subproblems. First, 360° 3D object recognition is proposed, in which a computer vision solution can recognize a reference object from all possible 360° locations. Second, 360° pose estimation is presented, in which the pose of a robot at all locations is estimated. Third, a 360° localization application is integrated with a closed-loop real-time trajectory tracking controller. The successful results of simulations and real experiments of trajectory tracking are also presented.

Multilayered PtSe2/pyramid-Si heterostructure array with light confinement effect for high-performance photodetection, image sensing and light trajectory tracking applications

A multilayered PtSe2/pyramid-Si heterostructure-based photodetector array consisting of 8×8 device units shows excellent optoelectrical performance with potential applications in NIR image sensing and real-time light trajectory tracking.

Real-time trajectory tracking control of a parallel robot with flexible links

Real-time end-effector trajectory tracking is applied to a newly developed parallel robotic system with two highly flexible links. In contrast to previous works, which are typically based on offline precalculations or rigid model inversion, a dynamic flexible multibody model is inverted online. As the underactuated system is non-minimum phase, the inverse model needs to be rendered stable for real-time integration. Therefore, three approaches are developed for the considered robot. Firstly, output redefinition is applied by directly weighting the elastic deformations and rotations of the links. Secondly, a small counter weight is attached at an advantageous location on the robot having only a minor influence on the eigenfrequencies. Lastly, the rotational degree of freedom of the end-effector is used to stabilize the internal dynamics of the inverse model with a small motion of a rotary motor. The concepts and real-time applicability are validated within experiments which extends the related literature being highly based on theoretical investigations. The experimental end-effector tracking performance based on the three minimum phase approaches is close to the desired trajectory and clearly outperforms classical rigid body inversion.

A Sensor Fusion Based Nonholonomic Wheeled Mobile Robot for Tracking Control.

In this paper, a detail design procedure of the real-time trajectory tracking for the nonholonomic wheeled mobile robot (NWMR) is proposed. A 9-axis micro electro-mechanical systems (MEMS) inertial measurement unit (IMU) sensor is used to measure the posture of the NWMR, the position information of NWMR and the hand-held device are acquired by global positioning system (GPS) and then transmit via radio frequency (RF) module. In addition, in order to avoid the gimbal lock produced by the posture computation from Euler angles, the quaternion is utilized to compute the posture of the NWMR. Furthermore, the Kalman filter is used to filter out the readout noise of the GPS and calculate the position of NWMR and then track the object. The simulation results show the posture error between the NWMR and the hand-held device can converge to zero after 3.928 seconds for the dynamic tracking. Lastly, the experimental results show the validation and feasibility of the proposed results.

High-Order Differential Feedback Control for Quadrotor UAV: Theory and Experimentation

A model-independent control strategy called high-order differential feedback control (HODFC) is applied to a quadrotor unmanned aerial vehicle (QUAV) based on a semi-autopilot indoor optical positioning system. The affine system form of the quadrotor model is provided to facilitate the design of the HODFC. A fifth-order high order differentiator (HOD) is introduced to estimate with high precision the derivatives of the reference input and the QUAV system’s states. A filtering signal of the control output is incorporated in the control law to overcome the system model’s unknown part in the HODFC scheme. The stability of both the HODFC and the HOD are proved. The physical and straightforward parameters are provided to make the HODFC scheme for the QUAV easy to operate. The real-time trajectory tracking experiments with varied reference trajectories and disturbances are carried out to illustrate the superior performance of the HODFC versus the proportional-integral-derivative (PID) method, in terms of the mean of absolute error, the integral of absolute error and the integral of the time-weighted absolute error. The results also demonstrate that the HODFC has superiority in static and dynamic trajectory tracking, especially when the system is disturbed.

Research on a LADRC Strategy for Trajectory Tracking Control of Delta High-Speed Parallel Robots

In order to better track the planned trajectory of Delta high-speed parallel robot, this paper proposes a dynamics control strategy for Delta high-speed parallel robots based on the linear active disturbance rejection control (LADRC) strategy which realizes decoupling control through observing and compensating the coupling and internal and external disturbances between the three joints. Firstly, the structure and dynamics model of the Delta high-speed parallel robot are analyzed, respectively. Secondly, the control scheme of the Delta high-speed parallel robot dynamic LADRC strategy is constructed, and then, the system stability is analyzed. Taking a representative 8-shaped space helical variance trajectory as a given input of the system and a triangular wave as an external disturbance as given disturbance input of the system, simulations are carried out to demonstrate the effectiveness of the proposed LADRC strategy; results indicate that the system with the LADRC strategy has a good quick and precise real-time trajectory tracking and strong robustness.

High-performance light trajectory tracking and image sensing devices based on a γ-In2Se3/GaAs heterostructure

A high-performance γ-In2Se3/GaAs heterostructure-based photodetector linear array shows potential in optoelectronic applications such as real-time light trajectory tracking and image sensing.

High order differential feedback controller design and implementation for a Stewart platform

Stewart platform or other parallel manipulators with a Stewart structure are commonly used in flight simulators, surgical operations, medical rehabilitation processes, machine tools, industrial applications, etc. Therefore, researchers have paid attention to position control of these manipulators in addition to their design and development process. In this study, a developed Stewart platform and its inverse kinematic analysis are presented first. Then, a model-free control scheme called a high order differential feedback controller scheme is designed for the Stewart platform in order to improve its trajectory tracking performance and robustness against to different reference trajectories. Real-time trajectory tracking experiments with varied reference trajectories are carried out to show the robustness and effectiveness of the high order differential feedback controller scheme compared to the traditional proportional–integral–derivative controller of which the parameters are optimally tuned. The obtained visual trajectory tracking results and numerical performance results based on error-based performance measurement metrics such as integral of absolute error, integral of squared error, and integral of time-weighted absolute error are provided for both the proposed high order differential feedback controller scheme and the optimal tuned proportional–integral–derivative controller. Experimental results show that the proposed high order differential feedback controller scheme is more robust than the proportional–integral–derivative controller. Furthermore, the high order differential feedback controller scheme has superiority in both transient and steady-state responses and even the parameters of the proportional–integral–derivative controller are optimally tuned.

EXPERIMENTAL VERIFICATION STUDY ON TRAJECTORY TRACKING OF A NOVEL REHABILITATION EXOSKELETON SHOULDER JOINT

The trajectory linearization control (TLC) was applied to design an autonomous nonlinear trajectory tracking controller for a novel rehabilitation exoskeleton shoulder joint in this paper. TLC is a relatively new control method which was applied in aircraft and mobile robot, which had good performance on real-time trajectory tracking, anti-jamming and universality. As a new application in the exoskeleton shoulder joint controller design, the controller in this research contained two loops that separately based on the inverse kinematics and pseudo-inverse dynamics models of the exoskeleton shoulder. Two PI controllers as the error regulator can reduce the tracking error. The position and angular velocity error feedback were employed to constitute the closed-loops. Since the controller was based on model and linearization, it can adapt to both linear and nonlinear control processes. The simulation of three different trajectories for single degree of freedom movement of shoulder joint (shoulder flexion and extension, abduction and extension), and the movement of both the two degrees were given. The simulation results showed that the TLC controller can follow the exoskeleton shoulder trajectory steadily and accurately.

A software-in-the-loop implementation of adaptive formation control for fixed-wing UAVs

This paper discusses the design and software-in-the-loop implementation of adaptive formation controllers for fixed-wing unmanned aerial vehicles &#x0028 UAVs &#x0029 with parametric uncertainty in their structure, namely uncertain mass and inertia. In fact, when aiming at autonomous flight, such parameters cannot assumed to be known as they might vary during the mission &#x0028 e.g. depending on the payload &#x0029. Modelingg and autopilot design for such autonomous fixed-wing UAVs are presented. The modeling is implemented in Matlab, while the autopilot is based on ArduPilot, a popular open-source autopilot suite. Specifically, the ArduPilot functionalities are emulated in Matlab according to the Ardupilot documentation and code, which allows us to perform software-in-the-loop simulations of teams of UAVs embedded with actual autopilot protocols. An overview of realtime path planning, trajectory tracking and formation control resulting from the proposed platform is given. The software-in-the-loop simulations show the capability of achieving different UAV formations while handling uncertain mass and inertia.

Dynamic Trajectory Planning and Tracking for Autonomous Vehicle With Obstacle Avoidance Based on Model Predictive Control

In this study, an obstacle avoidance controller based on nonlinear model predictive control is designed in autonomous vehicle navigation. The reference trajectory is predefined using a sigmoid function in accordance with road conditions. When obstacles suddenly appear on a predefined trajectory, the reference trajectory should be adjusted dynamically. For dynamic obstacles, a moving trend function is constructed to predict the obstacle position variances in the predictive horizon. Furthermore, a risk index is constructed and introduced into the cost function to realize collision avoidance by combining the relative position relationship between vehicle and obstacles in the predictive horizon. Meanwhile, lateral acceleration constraint is also considered to ensure vehicle stability. Finally, trajectory dynamic planning and tracking are integrated into a single-level model predictive controller. Simulation tests reveal that the designed controller can ensure real-time trajectory tracking and collision avoidance.

Real-Time Implementation of Sliding Mode Control Technique for Two-DOF Industrial Robotic Arm

This study presents a real-time implementation of the angle trajectory tracking control for the twoDOF industrial robotic manipulators. For real-time trajectory tracking control, the classical proportional–integral–derivative (PID) control and the nonlinear Sliding Mode Control techniques have been considered. The SMCtechnique takes into account the complete dynamic model of the two DOF robot to increase the trajectory trackingperformance of the manipulator. The experimental results demonstrate that the nonlinear SMC method has improvedthe trajectory tracking performance compared with the PID method.

Model-Free Trajectory Tracking Control of Two-Particle Magnetic Microrobot

We report a novel model-free trajectory tracking control scheme for two-particle magnetic microrobot (TPMM), which is capable of performing two-dimensional locomotion on a surface in fluid. In the control scheme, the dynamic model of the microrobot and external disturbances are lumped together and treated as the generalized disturbance, which eliminates the complex dynamics modeling of the microrobot due to the hydrodynamics and boundary effect. By employing an extended state observer, the motion states of the microrobot (i.e., position and velocity) are estimated, and the generalized disturbance is compensated. Then, a linear trajectory tracking controller is designed by utilizing the estimated motion states. Furthermore, a visual servoing control system is implemented and extensive real-time trajectory tracking experiments are conducted. The experimental results show the effectiveness of the proposed control scheme for realizing high-precision trajectory tracking for TPMM samples with different body-lengths and surrounding fluids.

Constrained generalized predictive control for obstacle avoidance in a quadcopter

SUMMARYThis work proposes a strategy for position control and obstacle avoidance in a quadcopter based on constrained generalized predictive control and geometric attitude control. The approach allows real-time trajectory tracking using optimal control actions and avoids collisions with static obstacles whose position is known. An experimental validation of the proposed controller is presented.

Real Time Trajectory Tracking Controller based on Lyapunov Function for Mobile Robot

Real Time Trajectory Tracking Controller based on Lyapunov Function for Mobile Robot