Robot Tracking Research Articles

Pose Tracking of Supermicrosurgical Robot Towards Multi-User Teleoperation.

This paper presents pose tracking experiments using a supermicrosurgical robot designed to consider teleoperation with multiple surgeons. Currently, existing supermicrosurgical robots assist only the primary surgeon. However, both primary and assistant surgeons need a high-precision motion for critical tasks that can easily damage microtissue. To assist multiple surgeons in supermicrosurgery with a surgical robot, dynamic collision avoidance becomes a critical issue due to the operation in a narrow surgical site. As a milestone to overcome this issue, we first developed a pose tracking algorithm by analyzing the inverse kinematics based on null-space control and a weighting matrix. Moreover, we also developed a control framework based on fully open-source software to run the pose tracking algorithm. Finally, we validated the proposed pose tracking algorithm by performing line tracing and rubber ring transferring experiments.

A method of tracking and positioning of industrial robots

In this paper, an improved tracking and localization algorithm of an omni-directional mobile industrial robot is proposed to meet the high positional accuracy requirement, improve the robot’s repeatability positioning precision in the traditional trilateral algorithm, and solve the problem of pose lost in the moving process. Laser sensors are used to identify the reflectors, and by associating the reflectors identified at a particular time with the reflectors at a previous time, an optimal triangular positioning method is applied to realize the positioning and tracking of the robot. The experimental results show that positioning accuracy can be satisfied, and the repeatability and anti-jamming ability of the omni-directional mobile industrial robot will be greatly improved via this algorithm.

Face-tracking algorithm for large-amplitude head motions with a 7-DOF manipulator

AbstractThe collection of facial action data is essential for the accurate evaluation of a patient’s condition in the intensive care unit, such as pain evaluation. An automatic face-tracking system is demanded to reduce the burden of data collection on the medical staff. However, many previous studies assume that the optimal trajectory of a robotic tracking system is reachable which is inapplicable for large-amplitude head motions. To tackle this problem, we propose a region-based face-tracking algorithm for large-amplitude head motion with a 7-DOF manipulator. A configuration-based optimization algorithm is proposed to trade-off between theoretical optimal pose and workspace constraints through the assignment of importance weights. To increase the probability of recapturing the face exceeding the reachable workspace of the manipulator, the camera is directed toward the center of the head, named the facial orientation center (FOC) constraint. Furthermore, a region-based tracking approach is designed to stabilize the manipulator for small amplitude head motions and smooth the tracking trajectory by adjusting the joint angle in the null space of the 7-DOF manipulator. Experimental results demonstrate the effectiveness of the proposed algorithm in tracking performance and finding an appropriate configuration for the unreachable theoretical optimal configuration. Moreover, the proposed algorithm with FOC constraint can successfully follow the head motion as losing 33.2% of the face during the tracking.

Adaptive neural network based compensation control of quadrotor for robust trajectory tracking

SummaryNeural network (NN) methods have become increasingly efficient and applicable in control. This article presents a novel nested control strategy based on adaptive radial basis function neural networks (RBFNN) and NN supervised control embedded with integrator back stepping (IBS) for a robust position and attitude trajectory tracking of quadrotor aerial robot in presence of modeling uncertainties, sensing noise, and external bounded disturbance. The control philosophy is derived from the decentralized inverse dynamics, where the adaptive RBFNN is adopted for the outer loop to approximate the unknown external disturbance, and adaptively compensate for their effect. In conjunction with a supervised control that stabilizes the attitude in the inner loop by adaptively compensating the unknown and unmodeled uncertainties, avoiding initial instability of the attitude during NN convergence. Furthermore, noise is attenuated by an adaptive extended Kalman filter. Stability analysis was elaborated using Lyapunov stability theory. Simulation of adaptive RBFNN control philosophy proved robustness and effectiveness compared to proportional integral derivative, IBS, and offline decentralized convolution neural network algorithms as it generates the control law by guarantying a fast convergence of parameters, better external disturbance compensation, and noise attenuation. The proposed scheme of robust tracking was validated.

Dynamic robotic tracking of underwater targets using reinforcement learning.

To realize the potential of autonomous underwater robots that scale up our observational capacity in the ocean, new techniques are needed. Fleets of autonomous robots could be used to study complex marine systems and animals with either new imaging configurations or by tracking tagged animals to study their behavior. These activities can then inform and create new policies for community conservation. The role of animal connectivity via active movement of animals represents a major knowledge gap related to the distribution of deep ocean populations. Tracking underwater targets represents a major challenge for observing biological processes in situ, and methods to robustly respond to a changing environment during monitoring missions are needed. Analytical techniques for optimal sensor placement and path planning to locate underwater targets are not straightforward in such cases. The aim of this study was to investigate the use of reinforcement learning as a tool for range-only underwater target-tracking optimization, whose promising capabilities have been demonstrated in terrestrial scenarios. To evaluate its usefulness, a reinforcement learning method was implemented as a path planning system for an autonomous surface vehicle while tracking an underwater mobile target. A complete description of an open-source model, performance metrics in simulated environments, and evaluated algorithms based on more than 15 hours of at-sea field experiments are presented. These efforts demonstrate that deep reinforcement learning is a powerful approach that enhances the abilities of autonomous robots in the ocean and encourages the deployment of algorithms like these for monitoring marine biological systems in the future.

Tracking Control of Wheeled Mobile Robot Based on MMPC

In order to solve the problems of low tracking accuracy and large steady-state error in track tracking of wheeled mobile robots, a MPC control scheme combining feedforward and feedback control is designed on the basis of Traditional Model Predictive Control (TMPC). First, when establishing the WMR kinematic error model, the linearization error is retained, and the control input and incremental constraints are considered. Secondly, a feedforward controller is designed for a given desired trajectory of wheeled mobile robot; At the same time, the multi constraint model predictive control (MMPC) strategy with speed optimization is added to design the feedback controller. Then, the stability of the designed MMPC controller is analyzed based on Lyapunov theory. Finally, MMPC control scheme is compared with TMPC control scheme and PID control scheme. The results show that compared with TMPC control scheme, MMPC control scheme reduces the lateral error by 17.87%, the longitudinal error by 17.12%, and the heading angle error by 9.81%; Compared with the PID control scheme, the lateral error is reduced by 20.82%, the longitudinal error is reduced by 23.26%, and the heading angle error is reduced by 5.87%.

Fully Distributed Fixed-Time Control of Cross-Domain Swarm Robots for Non-Cooperative Target Tracking

This brief focuses on solving the tracking problem of swarm robots with a non-cooperative target, where two core issues namely non-cooperative target and cross-domain collaboration are both taken into account. In order to solve the former issue, the trajectory of the non-cooperative target is constructed by a network model. This is more superior than the target system matrix observer-based method that adopted extensively in literature, as the latter one will cause the self-loop problem. Besides, the target information is no longer required in solving the regulation equation. Furthermore, a fixed-time cross-domain control method merely relying on outputs of a robot and its neighbours is proposed. Namely, the network-based fixed-time control method proposed in this brief is fully distributed without self-loop and requirement of the non-cooperative target’s system matrix. With the proposed controller, the cross-domain robots are able to successfully track a non-cooperative target within a fixed time, which is also verified by some numerical simulations.

A new class of robust and predefined-time consensus protocol based on noise-tolerant ZNN models

Zeroing neural network (ZNN) is a powerful tool in designing suitable control schemes since it is a systematic approach. It has been used in fields like robot manipulator control and tracking control, but few researchers have investigated the possible application of the ZNN in multi-agent systems. Based on the elegant zeroing neural network (ZNN) scheme, in this paper, two novel noise-tolerant ZNN (NTZNN) models are proposed to achieve consensus, which is a crucial problem in the field of cooperative control of the multi-agent systems. Besides, the novel noise-tolerant sign-bi-power (NTSBP) and noise-tolerant sign-exp-power (NTSEP) activation functions are used in this study. The NTZNN models activated by NTSBP and NTSEP are more robust than traditional ZNN models activated by the traditional sign-bi-power (SBP) and sign-exp-power (SEP) activation functions, respectively. The detailed mathematical analysis is presented to prove the robustness and predefined-time stability of the NTZNN models, and the upper bounds of the settling-time function are also estimated by a novel method based on improper integral. Combining the traditional Polyakov method and the proposed method based on improper integral, we can estimate the upper bounds of the settling-time function in a more precise way. Then, the robustness of the NTZNN models under both dynamic bounded vanishing noise and dynamic bounded non-vanishing noise is further evaluated by numerical experiments, and results show that the models are effective at both situations. We also present several practical examples of formation control, and parallel experiments are provided to further demonstrate that our results are general. All the theoretical and numerical verification results show that the NTZNN models are more robust than the traditional ZNN models activated by SBP or SEP activation functions, and it is also predefined-time stable.

Multifunctional Actuator Based on Graphene/PDMS Composite Materials with Shape Programmable Configuration and High Photothermal Conversion Capability.

Photoresponsive smart actuators based on carbon materials are attracting increasing attention. However, the low content of carbon materials currently limits the development of carbon material actuators. In this work, we designed and prepared a multifunctional bilayer composite actuator with controllable structures and high photothermal conversion efficiency. The actuator consists of a graphene/polydimethylsiloxane (PDMS) composite layer and a PDMS layer. With an ultrahigh graphene mass fraction (30%), the actuator exhibits a good hydrophobicity, unexpectedly high photothermal conversion performance (from room temperature to 120 °C within 1 s), and rapid photo-response capability. By thermal regulation, ultraviolet laser cutting, and assembly, the actuator can achieve shape programmable configuration in three-dimensional directions. Bionic crawling robots achieve a crawling speed of 0.065 mm/s, and liquid tracking robots achieve a rotational motion of 106°/s, a linear motion of 8.42 mm/s, and a complex "W"-shaped trajectory motion. This work provides a simple and effective method for the preparation and realization of multifunctional actuators based on graphene composite materials.

Towards Higher-Order Zeroing Neural Networks for Calculating Quaternion Matrix Inverse with Application to Robotic Motion Tracking

The efficient solution of the time-varying quaternion matrix inverse (TVQ-INV) is a challenging but crucial topic due to the significance of quaternions in many disciplines, including physics, engineering, and computer science. The main goal of this research is to employ the higher-order zeroing neural network (HZNN) strategy to address the TVQ-INV problem. HZNN is a family of zeroing neural network models that correlates to the hyperpower family of iterative methods with adjustable convergence order. Particularly, three novel HZNN models are created in order to solve the TVQ-INV both directly in the quaternion domain and indirectly in the complex and real domains. The noise-handling version of these models is also presented, and the performance of these models under various types of noises is theoretically and numerically tested. The effectiveness and practicality of these models are further supported by their use in robotic motion tracking. According to the principal results, each of these six models can solve the TVQ-INV effectively, and the HZNN strategy offers a faster convergence rate than the conventional zeroing neural network strategy.

A Novel Robotic GWO LDI Modeling and Control for Nonlinear Systems

This works aims to develop a new and improved GWO (Grey Wolf Optimizer), the so-called Robotic GWO (RGWO). First, to improve GWO's update formula position with an optimal learning strategy, we adapt the algorithm to real mobile environments, including robots, so that tracking robots can move prey toward targets. Then, the nonlinear active suspension (AS) control system is linearized by a neural network (NN) based linear differential inclusion (LDI) using feedback and feedforward linearization. In theory, it is found that the general SM (Sliding Mode) optimal control cannot provide sudden optimal results for the active linearized suspension system, so a method is proposed to improve the shortcomings of the active linearized suspension system. By constructing an extended SM-optimal manifold function, an improved SM-optimal controller is designed, which incorporates information on the entire structure and the expected performance of the suspension. For comparison purposes, the performance of three kinds of controls: SM optimal refinement control, logic-fuzzy SM control, and PS (passive suspension), shows the proposed controller's advantages . Finally, our improved SM optimal control for nonlinear AS systems, in general, can achieve the actual nominal optimal suspension performance, as confirmed by the simulation results. The results also show that the improved SM optimal control method provides better robustness even when the operating conditions or parameters of the structure vary.

Predefined Time Active Disturbance Rejection for Nonholonomic Mobile Robots

This article studies the fast path tracking problem for nonholonomic mobile robots with unknown slipping and skidding. Firstly, considering the steering problem, a new mathematical model of nonholonomic mobile robot is derived. Secondly, to estimate the unknown slipping and skidding of a nonholonomic mobile robot quickly and accurately, new predefined time observers, which can attain a predefined settling time, are established. Thirdly, based on the observers and the sliding mode approaches, predefined time active controllers are proposed to achieve high precision control performance of the nonholonomic mobile robot tracking. The method proposed in this article can achieve uniformly global stability within a predefined time, which makes the adjustment of the convergence time of the nonholonomic mobile robot easier and convenient. Finally, the simulation results validated the theoretical results.

Comparing end-effector position and joint angle feedback for online robotic limb tracking.

Somatosensation greatly increases the ability to control our natural body. This suggests that supplementing vision with haptic sensory feedback would also be helpful when a user aims at controlling a robotic arm proficiently. However, whether the position of the robot and its continuous update should be coded in a extrinsic or intrinsic reference frame is not known. Here we compared two different supplementary feedback contents concerning the status of a robotic limb in 2-DoFs configuration: one encoding the Cartesian coordinates of the end-effector of the robotic arm (i.e., Task-space feedback) and another and encoding the robot joints angles (i.e., Joint-space feedback). Feedback was delivered to blindfolded participants through vibrotactile stimulation applied on participants' leg. After a 1.5-hour training with both feedbacks, participants were significantly more accurate with Task compared to Joint-space feedback, as shown by lower position and aiming errors, albeit not faster (i.e., similar onset delay). However, learning index during training was significantly higher in Joint space feedback compared to Task-space feedback. These results suggest that Task-space feedback is probably more intuitive and more suited for activities which require short training sessions, while Joint space feedback showed potential for long-term improvement. We speculate that the latter, despite performing worse in the present work, might be ultimately more suited for applications requiring long training, such as the control of supernumerary robotic limbs for surgical robotics, heavy industrial manufacturing, or more generally, in the context of human movement augmentation.

Fuzzy-proportional-integral-derivative-based controller for object tracking in mobile robots

<span lang="EN-US">This paper aims at designing and implementing an intelligent controller for the orientation control of a two-wheeled mobile robot. The controller is designed in LabVIEW and based on analyzed image parameters from cameras. The image program calculates the distance and angle from the camera to the object. The fuzzy controller will get these parameters as crisp input data and send the calculated velocity as crisp output data to the right and left wheel motor for the robot tracking the target object. The results show that the controller gives a fast response and high reliability and quickly carries out data recovery from system faults. The system also works well in the uncertainties of process variables and without mathematical modeling.</span>

Epistemic planning for multi-robot systems in communication-restricted environments.

Many real-world robotic applications such as search and rescue, disaster relief, and inspection operations are often set in unstructured environments with a restricted or unreliable communication infrastructure. In such environments, a multi-robot system must either be deployed to i) remain constantly connected, hence sacrificing operational efficiency or ii) allow disconnections considering when and how to regroup. In communication-restricted environments, we insist that the latter approach is desired to achieve a robust and predictable method for cooperative planning. One of the main challenges in achieving this goal is that optimal planning in partially unknown environments without communication requires an intractable sequence of possibilities. To solve this problem, we propose a novel epistemic planning approach for propagating beliefs about the system's states during communication loss to ensure cooperative operations. Typically used for discrete multi-player games or natural language processing, epistemic planning is a powerful representation of reasoning through events, actions, and belief revisions, given new information. Most robot applications use traditional planning to interact with their immediate environment and only consider knowledge of their own state. By including an epistemic notion in planning, a robot may enact depth-of-reasoning about the system's state, analyzing its beliefs about each robot in the system. In this method, a set of possible beliefs about other robots in the system are propagated using a Frontier-based planner to accomplish the coverage objective. As disconnections occur, each robot tracks beliefs about the system state and reasons about multiple objectives: i) coverage of the environment, ii) dissemination of new observations, and iii) possible information sharing from other robots. A task allocation optimization algorithm with gossip protocol is used in conjunction with the epistemic planning mechanism to locally optimize all three objectives, considering that in a partially unknown environment, the belief propagation may not be safe or possible to follow and that another robot may be attempting an information relay using the belief state. Results indicate that our framework performs better than the standard solution for communication restrictions and even shows similar performance to simulations with no communication limitations. Extensive experiments provide evidence of the framework's performance in real-world scenarios.

Design and Implementation of the Trajectory Tracking and Dynamic Obstacle Avoidance of Wheeled Mobile Robot Based on T–S Fuzzy Model

Design and Implementation of the Trajectory Tracking and Dynamic Obstacle Avoidance of Wheeled Mobile Robot Based on T–S Fuzzy Model

Research on Surface Tracking and Constant Force Control of a Grinding Robot

To improve the quality and efficiency of robot grinding, a design and a control algorithm for a robot used for grinding the surfaces of large, curved workpieces with unknown parameters, such as wind turbine blades, are proposed herein. Firstly, the structure and motion mode of the grinding robot are determined. Secondly, in order to solve the problem of complexity and poor adaptability of the algorithm in the grinding process, a force/position hybrid control strategy based on fuzzy PID is proposed which greatly improves the response speed and reduces the error of the static control strategy. Compared with normal PID, fuzzy PID has the advantages of variable parameters and strong adaptability; the hydraulic cylinder used to adjust the angle of the manipulator can control the speed offset within 0.27 rad/s, and the grinding process can be carried out directly without obtaining the specific model of the surface to be machined. Finally, the experiments are carried out, the grinding force and feed speed are maintained within the allowable error range of the expected value, and the results verify the feasibility and effectiveness of the position tracking and constant force control strategy in this paper. The surface roughness of the blade is maintained within Ra = 2~3 μm after grinding, which proves that the grinding quality meets the requirements of the best surface roughness required for the subsequent process.

Path planning in continuous adjacent farmlands and robust path-tracking control of a rice-seeding robot in paddy field

During rice seeding, paddy fields are segmented by embankments to hold water and evenly supply nutrients to the rice. Reasonable path planning from the entry to the exit of continuous adjacent sublands and accurate path tracking in harsh paddy fields are crucial for efficient and high quality work by an autonomous rice-seeding robot. In this study, four entering and exiting modes with continuous adjacent sublands are defined, and the corresponding planning paths for each mode are provided and analyzed. The optimal working path is obtained under the constraints of full coverage without overlap or skip between rows, minimum blank operation areas at the headland and minimum turns while working; The requirements for the width of standardized farmland and the number the farmland is divided into are given. A kinematic model of the rice-seeding robot embodying slip and side slip is built to achieve reliable and accurate path tracking under adverse conditions of paddy fields. The equivalent disturbances caused by slip and side slip are estimated using a nonlinear observer. Combined with a nonlinear observer, a fast terminal sliding-mode controller is designed for the path tracking of the rice-seeding robot in paddy fields. The control algorithm is validated through simulations and field tests.

Automatic Alignment of Fractured Femur: Integration of Robot and Optical Tracking System

This paper presents Robossis, a surgical robotic system for automated femur fracture alignment. Robossis is the integration of an optical tracking system with a 6-degree-of-freedom (6-DOF) 3-armed parallel robot that satisfies the clinical and mechanical design requirements for femur alignment surgery. In real-time, the optical tracking system obtains the spatial position of the distal and proximal parts of the fractured femur. Then, an auto-alignment algorithm uses this data to guide the robot to automatically and accurately align the bone fragments while overcoming the muscle payload surrounding the femur. A graphical user interface (GUI) provides a real-time visual guide for the surgeon to check the deviation from alignment while the robot is automatically bringing the bone into an aligned position. To demonstrate the capabilities of Robossis, laboratory and cadaver experiments are performed, showing that Robossis can successfully manipulate bone in 6 DOFs in space, achieving alignment with submillimeter accuracy.

Trajectory Planning for Coal Gangue Sorting Robot Tracking Fast-Mass Target under Multiple Constraints.

Aiming at the problems of grab failure and manipulator damage, this paper proposes a dynamic gangue trajectory planning method for the manipulator synchronous tracking under multi-constraint conditions. The main reason for the impact load is that there is a speed difference between the end of the manipulator and the target when the manipulator grabs the target. In this method, the mathematical model of seven-segment manipulator trajectory planning is constructed first. The mathematical model of synchronous tracking of dynamic targets based on a time-minimum manipulator is constructed by taking the robot's acceleration, speed, and synchronization as constraints. The model transforms the multi-constraint-solving problem into a single-objective-solving problem. Finally, the particle swarm optimization algorithm is used to solve the model. The calculation results are put into the trajectory planning model of the manipulator to obtain the synchronous tracking trajectory of the manipulator. Simulation and experiments show that each joint of the robot's arm can synchronously track dynamic targets within the constraint range. This method can ensure the synchronization of the position, speed, and acceleration of the moving target and the target after tracking. The average position error is 2.1 mm, and the average speed error is 7.4 mm/s. The robot has a high tracking accuracy, which further improves the robot's grasping stability and success rate.