Robot Tracking Research Articles

Reconfigurable System-on-Chip Architectures for Robust Visual SLAM on Humanoid Robots

Visual Simultaneous Localization and Mapping (vSLAM) is the method of employing an optical sensor to map the robot’s observable surroundings while also identifying the robot’s pose in relation to that map. The accuracy and speed of vSLAM calculations can have a very significant impact on the performance and effectiveness of subsequent tasks that need to be executed by the robot, making it a key building component for current robotic designs. The application of vSLAM in the area of humanoid robotics is particularly difficult due to the robot’s unsteady locomotion. This paper introduces a pose graph optimization module based on RGB (ORB) features, as an extension of the KinectFusion pipeline (a well-known vSLAM algorithm), to assist in recovering the robot’s stance during unstable gait patterns when the KinectFusion tracking system fails. We develop and test a wide range of embedded MPSoC FPGA designs, and we investigate numerous architectural improvements, both precise and approximation, to study their impact on performance and accuracy. Extensive design space exploration reveals that properly designed approximations, which exploit domain knowledge and efficient management of CPU and FPGA fabric resources, enable real-time vSLAM at more than 30 fps in humanoid robots with high energy-efficiency and without compromising robot tracking and map construction. This is the first FPGA design to achieve robust, real-time dense SLAM operation targeting specifically humanoid robots. An open source release of our implementations and data can be found in [ 1 ].

Enhancing Disturbance Rejection of PID Controllers for DC Joint Motors of Trajectory Tracking Robots Using Disturbance Observer

In this paper, disturbance rejection of DC motor PID trajectory control systems is enhanced for independent joint control of robot arms. The concept of disturbance observer is invoked to propose a linear auxiliary control that augments existing PID controllers. The design of the auxiliary control is developed using a state space approach rather than transfer function approaches commonly employed in many existing designs derived from the concept of disturbance observer. This provides new insight and leads to a compact design requiring only two design parameters. While many of the existing DC motor trajectory control systems assume the availability of current feedback from a motor coil, the proposed auxiliary control does not. This can highly facilitate its applications in the lacking situation. Realizing that the stability of the resulting control systems could be inconvenient to assert due to increased system dimension resulting from incorporating disturbance observer, compact criteria for asserting robust stability using readily available results is given explicitly. To evaluate the capability of the auxiliary control for disturbance rejection, experimental results on a DC joint motor of an articulated robot arm are given. In presence of smooth and abrupt loading variations due to gravity, it appears that the tracking error of the enhanced system can be approximately 67% of that of the unenhanced system. This result is consistent in all three rounds of experiments.

A noise tolerant parameter-variable zeroing neural network and its applications

Time-varying problems frequently arise in the territories of science and engineering, and most of the time-varying problems can be described by dynamic matrix equations. As a powerful tool for solving dynamic matrix equations, the zeroing neural network (ZNN) develops fast in recent years. Convergence and robustness are two main performance indicators of the ZNN model. However, the development of the ZNN is focused on the improvement of its convergence in the past, and its robustness to noises is rarely considered. In order to achieve fast convergence and robustness of the ZNN model, a novel activation function (NAF) is presented in this paper. Based on the NAF, a noise-tolerant parameter-variable ZNN (NTPVZNN) model for solving dynamic Sylvester matrix equations (DSME) is realized, and its fixed-time convergence and robustness to noises are verified by rigorous mathematical analysis and numerical simulation results. Besides, two examples of electrical circuit currents computing and robotic manipulator trajectory tracking using the proposed NTPVZNN model in noisy environment further demonstrates its practical application ability.

Vision-Based Autonomous Landing of a Hybrid Robot on a Powerline

In recent years, various types of inspection robots have been developed to automate powerline inspection. The hybrid robot combines the advantages of climbing and flying robots and has a promising prospect in powerline inspection. But landing a hybrid robot on the target powerline among multiple ones is challenging. Flights require robust detection of powerlines and stable tracking of the target powerline. We propose a complete solution for the autonomous landing of a hybrid robot on a powerline. First, a special feature extraction operator and the corresponding density-based feature recognition algorithm are designed to detect multiscale powerlines. Second, a binocular vision-based depth estimation method for the landing point in the powerline is described. Third, two spatio-temporal dictionaries are established to track the target one in multiple powerlines. Meanwhile, landing strategies and control methods are presented to achieve a stable landing task. Finally, a hybrid robot is designed to validate the proposed method. The experiment results demonstrate the accuracy of the powerline detection and depth estimation algorithm, as well as the effectiveness of the robot in tracking and landing tasks.

Non-Linear Prediction-Based Trajectory Tracking for Non-Holonomic Mobile Robots

Non-Linear Prediction-Based Trajectory Tracking for Non-Holonomic Mobile Robots

Research on Automatic Flame Recognition and Tracking of Intelligent Fire Robot Based on STC89C52

Research on Automatic Flame Recognition and Tracking of Intelligent Fire Robot Based on STC89C52

Smart Solar Agriculture Grass Cutting Robot

The agricultural industry has benefited from increased automation, especially in the field of automation. In the past, people cut their own lawn by hand with grass cutters. Because the use of petroleum and diesel engines causes pollutants and degradation of energy. Therefore, automated cutters should be used in place of the conventional ones, with batteries acting as the system's power source and allowing it to operate as directed. This research suggests a smart solar agriculture grass cutting robot that is IoT based. A Bluetooth module, Arduino Uno microcontroller, Node MCU, battery, solar panel, and other components are used to do this. Rechargeable batteries can be charged using a power source or a solar panel. The DC motor's rotational speed and direction are controlled by the motor driver, and Node MCU monitors the DC motor as the system's brain. The Bluetooth module keeps track of the grass cutter robot's movements. This paper is entirely powered by automation and clean energy sources.

Design and Motion Control of Spherical Robot With Built-In Four-Wheel Omnidirectional Mobile Platform

The objective of this paper is to propose a novel spherical robot with built-in four-wheel omnidirectional mobile platform. The employment of four-wheel structure helps to improve the friction coefficient and thus reduce the slipping motion. In addition, it increases the velocity augmentation factor, and thus compensation methods can be effectively implemented into the system to decrease the slipping motion further. Moreover, the traction force is increased and thus the loading capacity can be improved correspondingly. First, the schematic structure along with the operating principle of the proposed spherical robot is introduced. Then, the kinematic and dynamic models are formulated analytically. The slipping error between the omnidirectional wheels and the inner wall of the sphere is analyzed. The slip observer and the motion controller are designed, and their convergence is proved theoretically. Subsequently, simulation is conducted to validate the proposed slip observer that is in turn implemented into the motion controller. The trajectory tracking of the spherical robot on straight line and circle is verified. Following that, one research prototype of the spherical robot with built-in four-wheel omnidirectional mobile platform is developed. Experimental investigation shows that the research prototype embedded with the slip observer can achieve the trajectory tracking performance very well.

Trajectory Tracking Strategy for Sliding Mode Control With Double Closed-Loop for Lawn Mowing Robot Based on ESO

The mowing robots work with a multivariable strong coupling underactuated system that is mostly troubled by difficulty controlling and unsatisfactory accuracy. Especially, the frequent external disturbances and parameter changes are likely to get missed and heavy cutting. In this paper, a new trajectory tracking control method based on extended state observer (ESO) is introduced with a particular focus on dual closed-loop sliding mode. Firstly, from the perspective of kinematics, a speed assistant controller was designed to generate the speed control quantity, and secondly, a sliding mode control algorithm based on the improved Fractional Power Rate Reaching Law (IFPRRL) was programmed to control the drive motor that tracked the speed control quantity. By means of comparison, our improved algorithm presented faster arrival time and better robustness along with similar jittering. At the same time, the robustness of the system was further enhanced with the help of an optimized ESO to tackle unmodeled disturbances and uncertain disturbances during the operation. Finally, the experimental analysis of the motor drive circuit and the trajectory tracking control system of the lawn mowing robot were both carried out respectively. The analysis shows that the performance of the proposed reaching law sliding mode control algorithm had some new pleasing changes, such as adjustment time and robustness. The circular trajectory and the detour mowing trajectory were respectively tracked in the double closed-loop sliding mode designed in this paper. The experimental goal was to ensure that the error vector <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$P_{e} =$ </tex-math></inline-formula> ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$x$ </tex-math></inline-formula> Axis position error <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$x_{e}$ </tex-math></inline-formula> , <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$y$ </tex-math></inline-formula> Axis position error <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$y_{e}$ </tex-math></inline-formula> , Angle error <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\theta _{e}$ </tex-math></inline-formula> ) all remaining at (0.01m, 0.01m, 0.01rad) were 5.34s and 5.36s, respectively, and both could be finally converged to 0. The results show that the newly developed controller based on ESO presented smaller arrival time and stronger robustness. The dual-closed-loop control of sliding-mode trajectory tracking method was capable to meet the real-time and precision requirements of the lawnmower robot for quick trajectory tracking.

Research on mobile robot path planning and tracking control

Research on mobile robot path planning and tracking control

Computing quaternion matrix pseudoinverse with zeroing neural networks

&lt;abstract&gt;&lt;p&gt;In recent years, it has become essential to compute the time-varying quaternion (TVQ) matrix Moore-Penrose inverse (MP-inverse or pseudoinverse) to solve time-varying issues in a range of disciplines, including engineering, physics and computer science. This study examines the problem of computing the TVQ matrix MP-inverse using the zeroing neural network (ZNN) approach, which is nowadays considered a cutting edge technique. As a consequence, three new ZNN models are introduced for computing the TVQ matrix MP-inverse in the literature for the first time. Particularly, one model directly employs the TVQ input matrix in the quaternion domain, while the other two models, respectively, use its complex and real representations. In four numerical simulations and a real-world application involving robotic motion tracking, the models exhibit excellent performance.&lt;/p&gt;&lt;/abstract&gt;

Research on mobile robot path planning and tracking control

Research on mobile robot path planning and tracking control

A Bidirectional Guided Filter Used for RGB-D Maps

The RGB-D cameras are widely used in the fields of robotic spray painting, packaging and tracking. However, the quality of the depth maps of RGB-D maps is relatively low. Guided filters utilizing the color image as a guide map is a common method to deal with these problems. However, the noise in guidance will make the filtering output contains unexpected noise that doesn’t contain before filtering. In this paper, based on the classical guided filter, a bidirectional guided filter is proposed to filter the filtering input and the guidance simultaneously. The basic equations are proposed and the filter kernel is derived. The properties of the proposed bidirectional guided filter such as edge-preserving filtering property, insensitivity to the noise of guidance and filtering effects on guidance is investigated. Four different groups of experiments are performed to further verify the effectiveness of the proposed method. The classical guided filter and the bidirectional guided filter are compared to verify that the proposed bidirectional guided filter can reduce the impact of the noise of the guidance; and the bidirectional guided filter and the box mean filter are compared to verify the filtering effect on guidance of the proposed bidirectional guided filter.

Vision Navigation Based PID Control for Line Tracking Robot

In a controlled indoor environment, line tracking has become the most practical and reliable navigation strategy for autonomous mobile robots. A line tracking robot is a self-mobile machine that can recognize and track a painted line on the floor. In general, the path is set and can be visible, such as a black line on a white surface with high contrasting colors. The robot’s path is marked by a distinct line or track, which the robot follows to move. Several scientific contributions from the disciplines of vision and control have been made to mobile robot vision-based navigation. Localization, automated map generation, autonomous navigation and path tracking is all becoming more frequent in vision applications. A visual navigation line tracking robot should detect the line with a camera using an image processing technique. The paper focuses on combining computer vision techniques with a proportional-integral-derivative (PID) controller for automatic steering and speed control. A prototype line tracking robot is used to evaluate the proposed control strategy.

하이브리드 주행모드의 휠-트랙 로봇

In this paper, we propose a method to design a wheel track robot that can change the driving mode depending on the terrain. The proposed wheel-track robot has a mechanism to change the driving mode, with a parallelogram four-joint link structure that uses wheels to drive on flat ground and tracks to drive on rough ground. In addition, a gyro sensor, a laser sensor, and an ultrasonic sensor are used to recognize the surrounding environment of the robot. An algorithm was developed to allow the robot to change the driving mode by itself and overcome rough terrain, where it was controlled using Arduino Mega 2560. Wheel-track robots were designed and manufactured, where driving tests confirmed that the robots can recognize the environment and change driving modes on their own.

Optimal Position Control of Nonlinear Muscle Based on Sliding Mode and Particle Swarm Optimization Algorithm

Rehabilitation is a way to care muscle and skeleton disorders and disease, using mechanical equipment. The aim of rehabilitation is that help disable persons who they reach to minimum physical activities. Since pneumatic actuators have high respect of power to weight, are cleanliness, reachable fluid, are to repair and low cost that can use to implement in robot which interact with human body parts. The other features that use on pneumatic actuators that this is comprisable fluid and variable stiffness traits. Due to importance and performance of these actuators in medical science and also they have high security and exact control which can preserve bodies. Therefore in this project, we search pneumatic actuators with high precision controller that can manufacture various mechanisms for rehabilitation process. Pneumatic actuators mathematical modeling is presented in this thesis. According to modeling of pneumatic actuator, differential equations of actuators are presented. In this research, we used solenoid valves, because proportional valves are expensive. We used sliding mode theory for position control of actuator. There are many importance on these systems and the main aim of this project, optimal path of 2 links mechanism for rehabilitation problems. So optimal tracking robot, we use particle swarm optimization for finding optimal gain controller in sliding mode control because we want to achieve minimum tracking errors in path planning of mechanism.

Mobile Robot Tracking with Deep Learning Models under the Specific Environments

Visual-based target tracking is one of the critical methodologies for the control problem of multi-robot systems. In dynamic mobile environments, it is common to lose the tracking targets due to partial visual occlusion. Technologies based on deep learning (DL) provide a natural solution to this problem. DL-based methods require less human intervention and fine-tuning. The framework has flexibility to be retrained with customized data sets. It can handle massive amounts of available video data in the target tracking system. This paper discusses the challenges of robot tracking under partial occlusion and compares the system performance of recent DL models used for tracking, namely you-only-look-once (YOLO-v5), Faster region proposal network (R-CNN) and single shot multibox detector (SSD). A series of experiments are committed to helping solve specific industrial problems. Four data sets are that cover various occlusion statuses are generated. Performance metrics of F1 score, precision, recall, and training time are analyzed under different application scenarios and parameter settings. Based on the metrics mentioned above, a comparative metric P is devised to further compare the overall performance of the three DL models. The SSD model obtained the highest P score, which was 13.34 times that of the Faster RCNN model and was 3.39 times that of the YOLOv5 model with the designed testing data set 1. The SSD model obtained the highest P scores, which was 11.77 times that of the Faster RCNN model and was 2.43 times that of the YOLOv5 model with the designed testing data set 2. The analysis reveals different characteristics of the three DL models. Recommendations are made to help future researchers to select the most suitable DL model and apply it properly in a system design.

A Study on Object Detection and Tracking of a Mobile Robot Using CIE L^* a^* b^* Color Space

Autonomous vehicles are increasingly used in daily life and industrial applications. Mobile robot technologies lead to autonomous architectures in these areas. The path planning methods of mobile robots contain differences in the purpose they realize. This trajectory planning from a determined starting point to the target point brings many techniques from image processing to artificial intelligence. In the study, an application with a unique design has been carried out on the tracking of circular objects with different diameters and colors by a mobile robot. Moving object is detected with CIE L^* a^* b^* color space with RGB-D camera by utilizing the ROS server-client architecture. The mobile robot tracks the detected object at a certain distance at a constant speed. Image filtering parameters are processed by the mobile robot in the Matlab environment together with the publisher-subscriber parameters. Thus, two circular objects with different colors, detected because of image processing and determined beforehand, are continuously followed by the mobile robot at a certain speed. Experiments were carried out using different diameter, size tolerance and color parameters in the image depending on the CIE L^* a^* b^* color space.

Path Tracking for Car-like Robots Based on Neural Networks with NMPC as Learning Samples

In the field of path tracking for car-like robots, although nonlinear model predictive control (NMPC) can handle the system constraints well, its real-time performance is poor. To solve this problem, a neural network control method with NMPC as the learning sample is proposed. The design process of this control method includes establishing the NMPC controller based on the time-varying local model, generating learning samples based on this NMPC controller, and training to obtain the neural network controller. The proposed controller is tested by a joint simulation of MATLAB and Carsim and compared with other controllers. According to the simulation results, the accuracy of the NN controller is close to that of the NMPC controller and far better than that of the Stanley controller. In all simulations, the absolute value of displacement error of the NN controller does not exceed 0.2854 m, and the absolute value of heading error does not exceed 0.2279 rad. In addition, the real-time performance of the NN controller is better than that of the NMPC controller. The maximum time cost and average time cost of the NN controller are, respectively, 40.91% and 22.37% smaller than those of the NMPC controller under the same conditions.

Numerical Investigation on ELF Electromagnetic Field Distribution of Pipeline Robot Tracking and Positioning System Using UAV

Pipeline robot, as a new type of equipment for pipeline operations such as pigging and detection, will play an increasingly important role in the operation and maintenance of oil and gas pipeline networks. The tracking and positioning technology during its operation process is one of the essential topics to improve the operating performance of pipeline robots and eliminate potential pipeline accidents. This paper presents the overall design of the wireless tracking and positioning system for pipeline robots based on extremely low frequency (ELF) electromagnetic method using the unmanned aerial vehicle (UAV). Starting from the classical electromagnetic theory, a mathematical model for the distribution of ELF electromagnetic field in buried metal pipeline environment is established. According to the characteristics of ELF electromagnetic wave transceiver and the model based on classical theory, the equivalent magnetic dipole transmission model is deduced. Based on the equivalent model, the attenuation characteristics of the ELF electromagnetic wave in the external space of the pipeline are analyzed by numerical simulation. The influence of the geometrical dimensions, environmental and working parameters, and other factors on the electromagnetic field intensity outside the pipeline is given at the same time. Finally, the distribution of ELF electromagnetic field in pipeline environment is discussed for horizontal-laying pipeline and inclined-laying pipeline, and the tracking method of pipeline robot is proposed on this basis. The proposed scheme is practical and effective, and it is suitable for real-time tracking and positioning of robot working in the pipelines whose slope is no more than 60 degrees with known or unknown distribution.