Robot Tracking Research Articles

Control of Trajectory Tracking for Mobile Manipulator Robot with Kinematic Limitations and Self-Collision Avoidance

In this paper, we propose an optimized differential evolution algorithm based on kinematic limitations and structural complexity constraints to solve the trajectory tracking problem for a mobile manipulator robot. The traditional method mainly involves obtaining the speed of the control variable based on the Jacobian inverse or linearization of the robot’s kinematic model, which cannot avoid the singularity position and/or self-collision phenomena. To address these problems, we directly design an optimized differential evolution algorithm to solve the trajectory planning problem for mobile manipulator robots. First, we analyze various constraints on the actual movement and describe them specifically using various equations or inequalities, including non-holonomic constraints on the mobile platform, the physical limitations of the driving motors, self-collision avoidance restriction, and smoothly traversing the singularity position. Next, we re-define the trajectory tracking of a mobile manipulator robot as an optimization problem under multiple constraints, including the trajectory tracking task and various constraints simultaneously. Then, we propose a new differential evolution (DE) algorithm by optimizing some critical operations to solve the optimization problem, such as improving the population’s distribution, limiting the population distribution range, and adding a success index. Additionally, we design two simple trajectories and two complex trajectories to determine the performance of the optimized DE algorithm in solving the trajectory tracking problem. The results demonstrate that the optimized DE algorithm can effectively realize the high-precision trajectory tracking task of a differential wheeled mobile manipulator robot through the consideration of kinematic limitations and self-collision avoidance.

Robust PID optimal tuning of a Delta parallel robot based on a hybrid optimization algorithm of particle swarm optimization and differential evolution

AbstractIn this paper, we propose an approach to tune optimal parameters of a robust PID controller by means of computed torque control (CTC) strategy for trajectory tracking of a Delta parallel robot, using a hybrid optimization algorithm of Particle Swarm Optimization (PSO) and differential evolution (DE). It differs from previous works that they propose robust PID controller parameters tuning based on conventional gradient-based optimization algorithms and apply them to process control. First, we reduce the tuning problem of a robust PID controller with CTC strategy satisfying requirements including robustness and disturbance attenuation to an optimization problem with nonlinear constraints by considering the nonlinear dynamic model of a Delta parallel robot. Second, we set up the design characteristics for the trajectory tracking of a Delta parallel robot. Then, we propose a robust PID controller in a way of obtaining the global optimization solution of the nonlinear optimization problem by running a PSO-DE hybrid optimization algorithm of finding the global optimal solution by maintaining the diversity of swarm during evolution based on the evolution of cognitive experience. Simulation and experimental results demonstrate that the proposed controller outperforms previous works with respect to robust performance and active disturbance attenuation when it is applied to tracking control of a Delta parallel robot.

Leader-Following Formation Tracking Control of Nonholonomic Mobile Robots Considering Collision Avoidance: A System Transformation Approach

In this paper, the obstacle avoidance problem-based leader–following formation tracking of nonholonomic wheeled mobile robots with unknown parameters of desired trajectory is investigated. First, the under-actuated system is transformed into a fully-actuated system by obtaining an auxiliary control variable using the transverse function. Second, by introducing a potential function for each obstacle, the influence of obstacles is considered in trajectory tracking, and the effect of the potential field on mobile robots is taken into account in the system tracking error. Third, the adaptive laws are designed to estimate the unknown parameters of the desired trajectory. Fourth, the results show that the formation error with respect to the actual position and orientation can be arbitrarily small by selecting appropriate design parameters. Finally, simulation examples are used to demonstrate that the proposed control scheme is effective.

Chaotic Synchronization in Mobile Robots

Chaos dynamics is an interesting nonlinear effect that can be observed in many chemical, electrical, and mechanical systems. The chaos phenomenon has many applications in various branches of engineering. On the other hand, the control of mobile robots to track unpredictable chaotic trajectories has a valuable application in many security problems and military missions. The main objective in this problem is to design a controller such that the robot tracks a desired chaotic path. In this paper, the concept of synchronization of chaotic systems is studied, and a new type-3 fuzzy system (T3FLS)-based controller is designed. The T3FLS is learned by some new adaptive rules. The new learning scheme of T3FLS helps to better stabilize and synchronize. The suggested controller has a better ability to cope with high-level uncertainties. Because, in addition to the fact that the T3FLSs have better ability in an uncertain environment, the designed compensator also improves the accuracy and robustness. Several simulations show better synchronization and control accuracy of the designed controller.

Research on Obstacle Avoidance and Environment Adaptability of Snake Robot Based on Deep Learning

With the development and popularization of computer artificial intelligence technology, more and more intelligent machines are gradually produced. These intelligent machines have brought great convenience to people's lives.This paper studies the control method of snake robots based on environment adaptability, which mainly explains the construction and stability of multi-modal CPG model. In addition, this paper also studies the trajectory tracking and dynamic obstacle avoidance of mobile robot based on deep learning.

Hierarchical model simulation based on EKF for mobile robots

With the technological advance grows the need for optimization in the tasks performed by mobile robots, these must be endowed with autonomy and other decisions, this is because the operating environments become more complex and with obstacles and objectives that change position, adding to this the interferences of the workspace such as disturbances in the environment and other system noises. To address this problem, the use of navigation algorithms, route creation, position estimation, path tracking, object recognition and others are used. In the present study, a spiral methodology was used with which a mathematical model based on the extended Kalman filter (EKF) was developed for trajectory tracking and position estimation of a differential robot using its kinematics. For the validation of the mathematical model Matlab was used together with CoppeliaSim where the different test scenarios were carried out, later the results of EKF are compared with those of Kevin Passino’s algorithm, finding superior in most aspects to the EKF except in the time when Kevin Passino’s algorithm achieves a shorter simulation time.

Trajectory Tracking and Obstacle Avoidance for Wheeled Mobile Robots Based on EMPC With an Adaptive Prediction Horizon.

This article develops an event-triggered model-predictive control (EMPC) strategy to realize trajectory tracking and obstacle avoidance for a wheeled mobile robot (WMR) subject to input constraints and external disturbances. In the EMPC strategy, a potential field is introduced in the cost function to guarantee a smooth path for the WMR. An event-triggered mechanism is designed to reduce the computational load of solving an optimal control problem (OCP). Moreover, an adaptive prediction horizon is utilized to further achieve computation reduction. Both recursive feasibility of the OCP and practical stability of the resulting closed-loop system are analyzed for the WMR with the input constraints and the external disturbances. Simulation results are provided to demonstrate the effectiveness and superiority of the proposed EMPC strategy.

Optimal trajectory tracking control for a wheeled mobile robot using backstepping technique

<span lang="EN-US">This work studies an optimal trajectory tracking of a wheeled mobile robot with the objective of minimizing energy consumption. First, the mathematical model, which takes into account the kinematic model of the mobile robot and the dynamic model of the actuators is presented. Then, a backstepping controller is designed and its parameters are tuned to satisfy several strict criteria such as rapid convergence, matching desired trajectory, and minimizing energy. For that, two cost functions were investigated and the best one has been selected. The significant reduction in energy losses achieved for all the proposed motion scenarios proves the effectiveness of our approach.</span>

Intravascular Tracking of Micro-Agents Using Medical Ultrasound: Towards Clinical Applications.

This study demonstrates intravascular micro-agent visualization by utilizing robotic ultrasound-based tracking and visual servoing in clinically-relevant scenarios. Visual servoing path is planned intraoperatively using a body surface point cloud acquired with a 3D camera and the vessel reconstructed from ultrasound (US) images, where both the camera and the US probe are attached to the robot end-effector. Developed machine vision algorithms are used for detection of micro-agents from minimal size of 250 μm inside the vessel contour and tracking with error recovery. Finally, real-time positions of the micro-agents are used for servoing of the robot with the attached US probe. Constant contact between the US probe and the surface of the body is accomplished by means of impedance control. Breathing motion is compensated to keep constant contact between the US probe and the body surface, with minimal measured force of 2.02 N. Anthropomorphic phantom vessels are segmented with an Intersection-Over-Union (IOU) score of 0.93 ± 0.05, while micro-agent tracking is performed with up to 99.8% success rate at 28-36 frames per second. Path planning, tracking and visual servoing are realized over 80 mm and 120 mm long surface paths. Experiments performed using anthropomorphic surfaces, biological tissue, simulation of physiological movement and simulation of fluid flow through the vessels indicate that robust visualization and tracking of micro-agents involving human patients is an achievable goal.

Research Progress of Robot Vision Sensing Weld Seam Tracking Technology

As the key technology of intelligent welding, seam tracking plays a vital role in the welding process. The development and application of visual sensing technology has laid the foundation for obtaining accurate weld position information. The intelligent combination of visual sensing and welding robots improves the stability and accuracy of welding seam tracking technology. This paper introduces the composition and principle of the welding seam tracking system, and the classification of visual sensing technology. This paper summarizes and analyses the welding seam image processing method and the research status of robot tracking control technology around the process of welding seam tracking. This paper summarizes the current problems of weld tracking technology and looks forward to the future development trend.

Research on a control method of tunnel cable inspection track robot based on intelligent obstacle crossing

In order to improve the resolution ability of the cable inspection robot in the dark environment of the tunnel and enable it to complete the autonomous obstacle avoidance control, a new intelligent obstacle crossing control method for the tunnel cable inspection track robot is studied, and the working environment of the inspection robot is explored, the Denavit-Hartenberg parameter method is used to establish the obstacle crossing model of the inspection robot, the joint variables of each robot are analyzed, and the position and posture of the end effector of the robot are obtained. The minimum value of the sum of moments is obtained by using the approximate solution method, and the winding deflection and torsion angle of the inspection robot is calculated by reverse solution. The robot obstacle-crossing motion workspace is analyzed by using the optimization algorithm, and the three-dimensional coordinate system is established. It is decomposed into the X-Y coordinate system, Y-Z coordinate system, and x-z coordinate system, so as to determine the maximum distance that the end gripper pops up on each axis, and judge whether the inspection robot can achieve obstacle crossing. According to the feedback results, intelligent control is carried out, and experiments are designed to verify the effectiveness of the control method. The results show that the voltage value of the inspection robot obtained by the control method is closer to the theoretical value, which can effectively help the robot to achieve obstacle-climbing operation accurately.

Efficient Hardware Accelerator Design of Non-Linear Optimization Correlative Scan Matching Algorithm in 2D LiDAR SLAM for Mobile Robots.

Simultaneous localization and mapping (SLAM) is the major solution for constructing or updating a map of an unknown environment while simultaneously keeping track of a mobile robot's location. Correlative Scan Matching (CSM) is a scan matching algorithm for obtaining the posterior distribution probability for the robot's pose in SLAM. This paper combines the non-linear optimization algorithm and CSM algorithm into an NLO-CSM (Non-linear Optimization CSM) algorithm for reducing the computation resources and the amount of computation while ensuring high calculation accuracy, and it presents an efficient hardware accelerator design of the NLO-CSM algorithm for the scan matching in 2D LiDAR SLAM. The proposed NLO-CSM hardware accelerator utilizes pipeline processing and module reusing techniques to achieve low hardware overhead, fast matching, and high energy efficiency. FPGA implementation results show that, at 100 MHz clock, the power consumption of the proposed hardware accelerator is as low as 0.79 W, while it performs a scan match at 8.98 ms and 7.15 mJ per frame. The proposed design outperforms the ARM-A9 dual-core CPU implementation with a 92.74% increase and 90.71% saving in computing speed and energy consumption, respectively. It has also achieved 80.3% LUTs, 84.13% FFs, and 20.83% DSPs saving, as well as an 8.17× increase in frame rate and 96.22% improvement in energy efficiency over a state-of-the-art hardware accelerator design in the literature. ASIC implementation in 65 nm can further reduce the computing time and energy consumption per scan to 5.94 ms and 0.06 mJ, respectively, which shows that the proposed NLO-CSM hardware accelerator design is suitable for resource-limited and energy-constrained mobile and micro robot applications.

A robust zeroing neural network and its applications to dynamic complex matrix equation solving and robotic manipulator trajectory tracking.

Dynamic complex matrix equation (DCME) is frequently encountered in the fields of mathematics and industry, and numerous recurrent neural network (RNN) models have been reported to effectively find the solution of DCME in no noise environment. However, noises are unavoidable in reality, and dynamic systems must be affected by noises. Thus, the invention of anti-noise neural network models becomes increasingly important to address this issue. By introducing a new activation function (NAF), a robust zeroing neural network (RZNN) model for solving DCME in noisy-polluted environment is proposed and investigated in this paper. The robustness and convergence of the proposed RZNN model are proved by strict mathematical proof and verified by comparative numerical simulation results. Furthermore, the proposed RZNN model is applied to manipulator trajectory tracking control, and it completes the trajectory tracking task successfully, which further validates its practical applied prospects.

Position Tracking of an Underwater Robot Based on Floating-Downing PI Control

A remotely operated underwater vehicle (ROV) is crucial in ocean exploration and underwater missions. An ROV is manipulated through a tether cable by an operator on shore or mother boat, and it can be used for underwater observations or as a robotic arm to take samples back. The position control and movement of an ROV are not stable due to buoyancy, ocean current, and surge waves. To overcome the influence of these disturbances on the ROV, we propose a switch proportional-integral (PI) controller combined with a buck-boost converter (BBC) to process the ROV’s position following. In this paper, a six-axis ROV was designed and implemented. The ROV controller was designed by a NI-roboRIO-based embedded system, which includes a pressure sensor, an accelerometer, six thrusters, and two webcams. The LabVIEW human–machine interface was designed to integrate the control system, sensors, and thrusters. The PI controller was employed to perform the station-keeping and trajectory following. Different PI control parameters were used for the ROV floating-up and diving-down in the sine-wave trajectory following. Experimental results showed that the proposed switch PI control scheme is robust for the position tracking of the underwater robot. The contribution of this study is that we proposed a novel switch proportional-integral controller combined with a buck-boost converter and applied the controller to a natural underwater vehicle, not a mathematical model. The experiments showed that the proposed controller can resist the disturbance of the aquatic environment.

Uncertainty Compensator and Fault Estimator-Based Exponential Supertwisting Sliding-Mode Controller for a Mobile Robot.

This work proposes a novel event-triggered exponential supertwisting algorithm (ESTA) for path tracking of a mobile robot. The proposed work is divided into three parts. In the first part, a fractional-order sliding surface-based exponential supertwisting event-triggered controller has been proposed. Fractional-order sliding surface improves the transient response, and the exponential supertwisting reaching law reduces the reaching phase time and eliminates the chattering. The event-triggering condition is derived using the Lipschitz method for minimum actuator utilization, and the interexecution time between two events is derived. In the second part, a fault estimator is designed to estimate the actuator fault using the Lyapunov stability theory. Furthermore, it is shown that in the presence of matched and unmatched uncertainty, event-trigger-based controller performance degrades. Hence, in the third part, an integral sliding-mode controller (ISMC) has been clubbed with the event-trigger ESTA for filtering of the uncertainties. It is also shown that when fault estimator-based ESTA is clubbed with ISMC, then the robustness of the controller increases, and the tracking performance improves. This novel technique is robust toward uncertainty and fault, offers finite-time convergence, reduces chattering, and offers minimum resource utilization. Simulations and experimental studies are carried out to validate the advantages of the proposed controller over the existing methods.

Adaptive Tracking and Stabilization of Nonholonomic Mobile Robots With Input Saturation

This article investigates the control problems of tracking and stabilization simultaneously for nonholonomic mobile robots subjected to input constraints and parameter uncertainties. A saturated time-varying controller is developed by applying a novel error state modification with bounded auxiliary variables. To improve the estimation performance of unknown kinematic and dynamic parameters, two projection-type adaptation laws are presented, while radial basis function approximations are used to estimate uncertain dynamics. Simulations are conducted to verify the effectiveness of the proposed controller.

Simultaneous Obstacle Avoidance and Target Tracking of Multiple Wheeled Mobile Robots With Certified Safety.

Collision avoidance plays a major part in the control of the wheeled mobile robot (WMR). Most existing collision-avoidance methods mainly focus on a single WMR and environmental obstacles. There are few products that cast light on the collision-avoidance between multiple WMRs (MWMRs). In this article, the problem of simultaneous collision-avoidance and target tracking is investigated for MWMRs working in the shared environment from the perspective of optimization. The collision-avoidance strategy is formulated as an inequality constraint, which has proven to be collision free between the MWMRs. The designed MWMRs control scheme integrates path following, collision-avoidance, and WMR velocity compliance, in which the path following task is chosen as the secondary task, and collision-avoidance is the primary task so that safety can be guaranteed in advance. A Lagrangian-based dynamic controller is constructed for the dominating behavior of the MWMRs. Combining theoretical analyses and experiments, the feasibility of the designed control scheme for the MWMRs is substantiated. Experimental results show that if obstacles do not threaten the safety of the WMR, the top priority in the control task is the target track task. All robots move along the desired trajectory. Once the collision criterion is satisfied, the collision-avoidance mechanism is activated and prominent in the controller. Under the proposed scheme, all robots achieve the target tracking on the premise of being collision free.

EXPERIMENTAL IN HEAD TRACKING CONTROL OF A FOUR OMNI WHEELED MOBILE ROBOT SYSTEM

This article describes an experimental investigation into the head tracking and control of a four-wheeled Omni-Robot. This study described the control scheme of the omnidirectional mobile robot. Additionally, the kinematics of the mobile robot is presented., A mobile robot with four Omni wheels was also developed on real hardware to verify the controller architecture design. The robot system is modeled and then implemented with the Arduino Microcontroller. This robot has two rotary encoders for running on the x-axis and y-axis, respectively. The gyro sensor (MPU-6050) is placed in the robot's middle and tracks data obtained by yaw angle. MATLAB Simulink was used to find the PD controller based on manual adjustment. The control architecture was deployed to complete and run the experiment on the mobile robot to evaluate the outcomes. The experiment results show the robot's motion control improvement in various scenarios.

Velocity-Based Gait Planning for Underactuated Bipedal Robot on Uneven and Compliant Terrain

This article develops a gait planning method for underactuated bipedal robot on uneven and compliant terrain. First, we employ a linear spring-damper model to describe foot-ground compliant contact, and establish a decoupled robot–ground three-dimensional dynamic mode. Second, based on a velocity-based bipedal stability definition and human walking characteristics, we propose a gait planning method to realize underactuated bipedal walking on uneven and compliant terrain. We decouple bipedal gait planning into sagittal and lateral master-slave ones. By planning the motion state of Center-of-Mass (CoM) of a robot, we make the movement of lateral and sagittal coincident such that bipedal walking is realized. Finally, underactuated bipedal walking with an average walking speed of 0.216 m/s and a step length of 183.9 mm is realized on uneven terrain where the maximum height of unevenness is 32 mm. The experimental results show that underactuated bipedal walking can be realized on uneven and compliant terrain by using the proposed method to control robot CoM and track its desired velocity.

A survey of catheter tracking concepts and methodologies.

Catheter tracking has become an integral part of interventional radiology. Over the last decades, researchers have significantly contributed to theoretical and technical catheter tracking solutions. However, most of the published work thus far focuses on a single application or a single tracking technology. This paper provides an exhaustive review of the state-of-the-art for catheter tracking in general by analyzing significant contributions in this field. We first present a historical overview that led to catheter tracking and continue with a survey of leading tracking technologies. These include image-based tracking, active and passive tracking, electromagnetic tracking, fiber optic shape sensing, bioelectric navigation, robotic tracking solutions, and hybrid tracking. As for imaging modalities, the focus is on x-ray based modalities, ultrasound, and magnetic resonance imaging. Finally, we review each tracking technology with respect to the imaging modality and establish the relation between the two and the underlying anatomy of interest.