Robot Tracking Research Articles

Feasibility Study of Removable Fiducial Marker Implantation with the Intelligent Navigation Bronchoscopy Technology in Robotic Radiosurgery System Tracking

<h3>Purpose/Objective(s)</h3> To investigate the feasibility of removable fiducial marker implantation with the intelligent navigation bronchoscopy technology in a frameless robotic radiosurgery system tracking. <h3>Materials/Methods</h3> CT scans of an inflatable anti-corruption pig lung were acquired, and two simulated tumor lesion sites were designed in the left and right lung lobes by intelligent navigation software, respectively. According to the principle of fiducial marker implantation, 24 removable fiducial markers were implanted around each simulated tumor lesion via bronchus after intelligent guiding. Simulating the end-expiratory state, the pig lung was scanned again to obtain CT images of the implanted removable fiducial markers and the number of successfully implanted fiducial markers was recorded. Six deliverable treatment plans were designed using the planning system, and then the pig lung plans were delivered based on the simulated respiratory movements. After the treatment, the implanted removable fiducial markers were retrieved through bronchoscopy, and the number of successful retrievals was recorded. The rigid body error and translation error were extracted and analyzed from the log file. <h3>Results</h3> No removed fiducial markers slipped and fell off during the experiment. 24 removable fiducial markers were successfully implanted under bronchoscopic intelligent magnetic navigation guidance, with a 100% implantation success rate (24/24) and 100% removable success rate (24/24). The fiducial marker tracking rate was 83.33% (20/24), with 4 fiducial markers being too far away from the simulated tumor location to be used during the tracking process. The data from the log file displayed that the mean rigid body error for 6 groups was 1.45 mm, the mean translation error was 1.78 mm. <h3>Conclusion</h3> The technology of bronchoscopy intelligent navigation and implantation of removable fiducial marker is feasible, and the tracking accuracy of the frameless robotic radiosurgery system can meet the clinical requirements, which has a good clinical application and teaching application prospect.

Real-World Dose Delivery Accuracy of Prostate SBRT with Tomotherapy Based Target Tracking

<h3>Purpose/Objective(s)</h3> The frameless robotic radiosurgery system target tracking system was recently adapted to Tomotherapy. While a previous dosimetric study has demonstrated comparable prostate SBRT plans between the two systems, we report here our real-world dose delivery accuracy of synchronization technology motion compensation for prostate SBRT and the initial clinical outcomes. <h3>Materials/Methods</h3> Patients undergo insertion of 3 fiducials and hydrogel before planning CT and MRI. Prostate and proximal seminal vesicles were included in the CTV. A PTV margin of 3 to 5 mm was utilized. Prescription was 40 Gy in 5 fractions. Patient motion files were analyzed and the maximum shift and gradient in each direction calculated to allow for motions to be categorized. Eight patients were enrolled in the TPS which uses daily imaging to deform VOI's and accumulate delivered doses based on organ volume and position changes. <h3>Results</h3> 14 patients with Gleason 6/7 prostate cancer were included with median prostate volume of 32.3 cm³ (17.7 - 99.6 cm³). Patient motion files showed 20 deliveries had motions <2 mm, 26 with slow position changes ≥ 2 mm. 10 deliveries had rapid position changes <5 mm and 14 had rapid changes ≥5 mm. For most OARs delivered D_max was within 2% of the planned dose. Larger differences were found for OARs located superiorly or inferiorly to the target, due to the rapid dose fall off in this direction. Large reductions in the D_max and D50 to the bladder and sigmoid are seen in patients who were asked to increase their bladder fill to reduce bowel dose. Mean dose to the CTV and PTV was found to agree to within 1.5% and 2.0% respectively. From 12 patients with median follow up time of 1 month (1 – 9 months), 3 reported grade 1 GU toxicities. 2 patients reported grade 1 GI toxicities and grade 1 fatigue. <h3>Conclusion</h3> The range of motions measured highlight the need for compensation techniques for precise dose deposition with Tomotherapy. Daily OAR variations resulted in dose delivery accuracy within 2% of planned dose.

A VS ultrasound diagnostic system with kidney image evaluation functions.

An inevitable feature of ultrasound-based diagnoses is that the quality of the ultrasound images produced depends directly on the skill of the physician operating the probe. This is because physicians have to constantly adjust the probe position to obtain a cross section of the target organ, which is constantly shifting due to patient respiratory motions. Therefore, we developed an ultrasound diagnostic robot that works in cooperation with a visual servo system based on deep learning that will help alleviate the burdens imposed on physicians. Our newly developed robotic ultrasound diagnostic system consists of three robots: an organ tracking robot (OTR), a robotic bed, and a robotic supporting arm. Additionally, we used different image processing methods (YOLOv5s and BiSeNet V2) to detect the target kidney location, as well as to evaluate the appropriateness of the obtained ultrasound images (ResNet 50). Ultimately, the image processing results are transmitted to the OTR for use as motion commands. In our experiments, the highest effective tracking rate (0.749) was obtained by YOLOv5s with Kalman filtering, while the effective tracking rate was improved by about 37% in comparison with cases without such filtering. Additionally, the appropriateness probability of the ultrasound images obtained during the tracking process was also the highest and most stable. The second highest tracking efficiency value (0.694) was obtained by BiSeNet V2 with Kalman filtering and was a 75% improvement over the case without such filtering. While the most efficient tracking achieved is based on the combination of YOLOv5s and Kalman filtering, the combination of BiSeNet V2 and Kalman filtering was capable of detecting the kidney center of gravity closer to the kidney's actual motion state. Furthermore, this model could also measure the cross-sectional area, maximum diameter, and other detailed information of the target kidney, which meant it is more practical for use in actual diagnoses.

Distributed adaptive formation tracking using quantized feedback communication for networked mobile robots with unknown wheel slippage

In this study, we consider a quantized-feedback-communication-based control design problem for the distributed adaptive formation tracking of multiple nonholonomic mobile robots with unknown slippage constraints under capacity-limited network control environments. Uniform-hysteretic quantizers are employed to quantize all the inputs and states of robots and the quantized position information of each robot is only transmitted to neighboring robots through directed networks. Compared with existing literature related to the robot formation, the primary contribution of this paper lies in establishing a novel local adaptive control design methodology to deal with the discontinuity problem caused by using the quantized states of each follower and the quantized position communication of neighboring robots. In the proposed strategy, the communication of the orientations and velocities of neighboring robots is not required for the local control design of follower robots. Moreover, quantized-states-based adaptive compensation schemes are constructed for the effects of signal quantization and wheel slippage. Based on the analysis of quantization errors, the practical stability strategy of the overall closed-loop formation system is derived with the convergence of local tracking errors. Simulation results clarify the proposed formation strategy.

Situational Graphs for Robot Navigation in Structured Indoor Environments

Mobile robots should be aware of their <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">situation</i> , comprising the deep understanding of their surrounding environment along with the estimation of its own state, to successfully make intelligent decisions and execute tasks autonomously in real environments. 3D scene graphs are an emerging field of research that propose to represent the environment in a joint model comprising geometric, semantic and relational/topological dimensions. Although 3D scene graphs have already been combined with SLAM techniques to provide robots with situational understanding, further research is still required to effectively deploy them on-board mobile robots. To this end, we present in this paper a novel, real-time, online built Situational Graph ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S-Graph</i> ), which combines in a single optimizable graph, the representation of the environment with the aforementioned three dimensions, together with the robot pose. Our method utilizes odometry readings and planar surfaces extracted from 3D LiDAR scans, to construct and optimize in real-time a three layered <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S-Graph</i> that includes (1) a robot tracking layer where the robot poses are registered, (2) a metric-semantic layer with features such as planar walls and (3) our novel topological layer constraining the planar walls using higher-level features such as corridors and rooms. Our proposal does not only demonstrate state-of-the-art results for pose estimation of the robot, but also contributes with a metric-semantic-topological model of the environment.

Trajectory tracking control strategy of the gantry welding robot under the influence of uncertain factors

In this paper, aiming at the problem of high-precision trajectory tracking of the gantry welding robot system under the influence of uncertain factors, a composite adaptive fuzzy compensation controller based on the computed torque control strategy has been proposed. The controller is composed of an adaptive fuzzy feedback control strategy and a dead zone adaptive fuzzy control strategy, which realizes high-precision trajectory tracking of the gantry welding robot, improves the solving speed of the control algorithm, and its tracking errors has been reduced. Screw theory and Lie group lie algebra are used to solve the problem of high algorithm complexity of controllers in multi-degree-of-freedom robot systems due to Newton-Euler dynamics modeling. Based on the computed torque control, the adaptive fuzzy feedback control strategy is adopted to compensate for the modeling errors and external disturbances in the gantry welding robot system, and the dead zone adaptive fuzzy control strategy is designed to compensate for the nonlinear dead zone structure with unknown parameters. The Lyapunov equation is introduced to prove the stability of the controller. Finally, the controller designed in this paper is compared with the conventional controller through simulation and experiment on the gantry welding robot platform, which verifies the effectiveness and superiority of the controller.

A nonlinear zeroing neural network and its applications on time-varying linear matrix equations solving, electronic circuit currents computing and robotic manipulator trajectory tracking

A nonlinear zeroing neural network and its applications on time-varying linear matrix equations solving, electronic circuit currents computing and robotic manipulator trajectory tracking

A reinforcement learning fuzzy system for continuous control in robotic odor plume tracking

AbstractIn dynamic outdoor environments characterized by turbulent airflow and intermittent odor plumes, robotic odor plume tracking remains challenging, because existing algorithms heavily rely on manually tuning or learning from expert experience, which are hard to implement in an unknown environment. In this paper, a multi-continuous-output Takagi–Sugeno–Kang fuzzy system was designed and tuned with reinforcement learning to solve the robotic odor source localization problem in dynamic odor plumes. Based on the Lévy Taxis plume tracking controller, the proposed fuzzy system determined the parameters of the controller based on the robot’s observation and guided the robot to turn and move towards the odor source at each searching step. The trained fuzzy system was tested in simulated filament-based odor plumes dispersed by a changing wind field. The results showed that the performance of the proposed fuzzy system-based controller trained with reinforcement learning can achieve a similar success rate and higher efficiency compared with a manually tuned and well-designed fuzzy system-based controller. The fuzzy system-based plume tracking controller was also validated through real robotic experiments.

Seam tracking system based on laser vision and CGAN for robotic multi-layer and multi-pass MAG welding

A robotic seam tracking system based on laser vision and conditional generative adversarial networks (CGAN) was proposed to address the problem of low welding precision for the multi-layer and multi-pass MAG welding process. The seam tracking system consisted of three modules, i.e., laser-vision (LV), server-terminal (ST), and robot-terminal (RT), and the real-time seam tracking for multi-layer and multi-pass welding was realized though the seam feature points extraction, coordinate conversion, deviation calculation and welding torch position correction based on the KUKA robot sensor interface (RSI). Experimental results showed that the proposed restoration and extraction network (REN) based on the CGAN principle could not only restore the seam feature information but also extract the seam feature points accurately. The welding torch could run smoothly in the strong noise environment, and there were no obvious correction marks in the weld appearance. The average correction error was less than 0.6 mm, and the adjustment process of the welding torch position can be completed within 1 s, indicating that the accuracy and speed of the proposed seam tracking system were acceptable.

Space robotics hybrid conceptual model for tracking and estimation using IoRT and AI-based control system

Robotics is an amalgamation of mechanical engineering and computer science. Mechanical engineering helps to design and develop mechanical parts and devices for control systems in robots. Space robots and robotics are known as devices that can enhance the manipulation, functionalities, and control exercised by astronauts, and thus can be called their artificial assistants for real-time exploration of space conditions. Control systems in robots like gestures and action become predominant for control, thus human-robot interaction comes into existence. AI and reinforcement learning have been used to control functionality in robots in various fields, but IoRT (Internet of robotic Things) is an emerging area of IoT that has the capabilities to monitor a variety of courses of action in robots. In this paper, we are proposing a conceptual framework based on an IoRT control system integrated with AI and reinforcement learning algorithms, which has been accessed based on multiple papers in the same domain, and it will help future researchers develop and simulate such a prototype. We are also using AKF (Adaptive Kalman Filtering) for robot tracking and noise deduction in sensors integrated with the A* algorithm. Overall, this is a concept-based framework to be designed and simulated.

Taekwondo Trajectory Tracking Based on Multitarget Detection Algorithm

In order to further improve the effect of fuzzy PID controller applied to joint robot trajectory tracking control, a Taekwondo trajectory tracking method based on multitarget detection algorithm is proposed in this study. Firstly, a fuzzy PID controller for trajectory tracking of joint robot is designed. Secondly, considering the two optimization objectives of controller output torque and trajectory tracking control deviation, an improved multiobjective PSO algorithm is designed to optimize the membership function and fuzzy rules of fuzzy PID controller. Finally, the multiobjective PSO algorithm and the improved multiobjective PSO algorithm are used to optimize the trajectory tracking fuzzy PID controller. The vector sets of the two optimization objectives are obtained, and the optimization results are compared and analyzed. The simulation results show that the simulation step length is t = 0.01 s, and the total simulation time is 10 s. Using the controller, the simulation values and output torque values of each joint angle at each time can be obtained, which are used to calculate the two objective function values in the optimization algorithm. According to the process, the optimization algorithm parameters are set, and the control simulation system designed above is run to complete the improved multiobjective PSO optimization of the trajectory tracking fuzzy PID controller of the joint robot. Conclusion. The improved multiobjective PSO algorithm has better nondominated solution set, which verifies the effectiveness and superiority of the algorithm to optimize the robot trajectory tracking fuzzy PID controller.

Wireless power transmission of mobile robot for target tracking

&lt;span lang="EN-US"&gt;The system is novel approach to combine wireless power transmission system (WPT) and automated guided vehicle (AGV). The wireless power transfer for charging the mobile robot is implemented using inductive coupling method. The system setup consists of transmitter coils whose switching action is controlled through transistors, receiver coil connected to full ridge rectifier and mobile robot. The track consists of number of transmitter coil which transmits power in form of electromagnetic waves. The receivers in the robot, which receives these waves and converts it back to electric power to charge the battery. The robot tracks its target destination based on the user command from the smart phone through Bluetooth. Very few theoretical researches are available on this field. A prototype was developed and tested based on the researches. The system achieves good range but falls short in efficiency to charge a battery, charging of battery takes longer time than regular charging time. Further research and extensive exploration can bring this technology from theory to practice.&lt;/span&gt;

Adaptive Sliding Mode Controller for Robotic Manipulator Tracking Control with Fuzzy Design

This paper introduces an adaptive sliding mode controller design based on fuzzy compensation for efficient robotic manipulator tracking control. This work introduces design of Adaptive Fuzzy Controller based on sliding control principles for Robotic Manipulators. In the work, an adaptive fuzzy sliding mode control algorithm is proposed for tracking control of robot manipulators. The fuzzy system uses a set of fuzzy rules, the parameters of which are modified in real-time by adaptive laws, to approximate unknown nonlinearities. This makes it easier to direct the nonlinear system's output to follow a specific trajectory. An adaptive control algorithm based on the adaptive fuzzy model is created using the Lyapunov approach. Both the chattering and the stable performance are assured.

Comparison of Graph Fitting and Sparse Deep Learning Model for Robot Pose Estimation.

The paper presents a simple, yet robust computer vision system for robot arm tracking with the use of RGB-D cameras. Tracking means to measure in real time the robot state given by three angles and with known restrictions about the robot geometry. The tracking system consists of two parts: image preprocessing and machine learning. In the machine learning part, we compare two approaches: fitting the robot pose to the point cloud and fitting the convolutional neural network model to the sparse 3D depth images. The advantage of the presented approach is direct use of the point cloud transformed to the sparse image in the network input and use of sparse convolutional and pooling layers (sparse CNN). The experiments confirm that the robot tracking is performed in real time and with an accuracy comparable to the accuracy of the depth sensor.

The design of a neural network-based adaptive control method for robotic arm trajectory tracking

The design of a neural network-based adaptive control method for robotic arm trajectory tracking

An inertial neural network approach for loco-manipulation trajectory tracking of mobile robot with redundant manipulator

This paper proposes a novel constrained optimization model to address the loco-manipulation problem of mobile robot with redundant manipulator for trajectory tracking. To alleviate the accumulative error of the end-effector’s position, a new control law is designed to eliminate the negative effect from the deviation of the initial position, leading to better performance than existing ones. To deal with the locomotion constraints in the loco-manipulation problem, the optimization model is converted to an augmented Lagrangian primal–dual problem. Furthermore, an inertial neural network approach is used to solve the problem and the corresponding Lyapunov proof guarantees the convergence of variables. The numerical simulations show that the proposed approach is more suitable for application since the model is more effective and the algorithm has better convergence rate.

Redundant Robot with Pneumatic Artificial Muscles for Rehabilitation Works Using Iterative Learning Control

This study aims to develop a multi-functional robotic arm with a multi-degree of freedom using a pneumatic artificial muscle cylinder as the main actuator, with a wearable function. While wearing the robotic arm, it has four degrees of freedom and can be used as an upper limb rehabilitation aid to perform rehabilitation exercise tasks. The rotating axes are driven by pneumatic artificial muscle cylinders to ensure the safety and flexibility of the robot interaction. By integrating multiple pneumatic control valves and sensors with embedded interface cards, the control of pneumatic artificial muscle cylinders is performed, and the rehabilitation trajectory commands are planned through the kinematics of the robotic arm, and a closed-loop control system is established to enable the robotic arm to achieve the task of rehabilitation trajectory tracking. In the single-axis robotic arm controller experiment, it was found that the LMS-PID is superior to the conventional PID control method. In the control experiment of the robotic arm under the multi-axis recovery trajectory, the result showed that the training of drawing a circle on the wall could be successfully planned. In the multi-axis robotic tracking experiment under the rehabilitation trajectory, the result showed that the RMSE of the tracking trajectory decreased to 0.2444 and 0.2853. In the robotic arm joining/loading experiment, it was shown that the method of this study can withstand some loading effects. Moreover, this study introduces iterative learning control to improve the non-linear compensation and phase lag problems of the PID controller so that the robot arm can have a certain tracking accuracy under the round-trip robust trajectory.

VLP Landmark and SLAM-Assisted Automatic Map Calibration for Robot Navigation with Semantic Information

With the rapid development of robotics and in-depth research of automatic navigation technology, mobile robots have been applied in a variety of fields. Map construction is one of the core research focuses of mobile robot development. In this paper, we propose an autonomous map calibration method using visible light positioning (VLP) landmarks and Simultaneous Localization and Mapping (SLAM). A layout map of the environment to be perceived is calibrated by a robot tracking at least two landmarks mounted in the venue. At the same time, the robot’s position on the occupancy grid map generated by SLAM is recorded. The two sequences of positions are synchronized by their time stamps and the occupancy grid map is saved as a sensor map. A map transformation method is then performed to align the orientation of the two maps and to calibrate the scale of the layout map to agree with that of the sensor map. After the calibration, the semantic information on the layout map remains and the accuracy is improved. Experiments are performed in the robot operating system (ROS) to verify the proposed map calibration method. We evaluate the performance on two layout maps: one with high accuracy and the other with rough accuracy of the structures and scale. The results show that the navigation accuracy is improved by 24.6 cm on the high-accuracy map and 22.6 cm on the rough-accuracy map, respectively.

Weld Seam Identification and Tracking of Inspection Robot Based on Deep Learning Network

The weld seams of large spherical tank equipment should be regularly inspected. Autonomous inspection robots can greatly enhance inspection efficiency and save costs. However, the accurate identification and tracking of weld seams by inspection robots remains a challenge. Based on the designed wall-climbing robot, an intelligent inspection robotic system based on deep learning is proposed to achieve the weld seam identification and tracking in this study. The inspection robot used mecanum wheels and permanent magnets to adsorb metal walls. In the weld seam identification, Mask R-CNN was used to segment the instance of weld seams. Through image processing combined with Hough transform, weld paths were extracted with a high accuracy. The robotic system efficiently completed the weld seam instance segmentation through training and learning with 2281 weld seam images. Experimental results indicated that the robotic system based on deep learning was faster and more accurate than previous methods, and the average time of identifying and calculating weld paths was about 180 ms, and the mask average precision (AP) was about 67.6%. The inspection robot could automatically track seam paths, and the maximum drift angle and offset distance were 3° and 10 mm, respectively. This intelligent weld seam identification system will greatly promote the application of inspection robots.

Hyperbolic tangent variant-parameter robust ZNN schemes for solving time-varying control equations and tracking of mobile robot

Time-varying Lyapunov and Stein matrix equations (TVLSMEs) are very ubiquitous in engineering applications. To solve TVLSMEs, many zeroing neural networks (ZNNs) activated by nonlinear activation functions have been proposed. In the conventional ZNN schemes, the designed convergence parameters (DCPs) before the nonlinear activation functions are extraordinarily important because DCPs determine their convergent rates basically. However, the DCPs are usually stated to be constant, which is not practical because the DCPs are usually time-varying in realistic hardware situations, especially the external noises injected. Hence, many varying-parameter ZNNs (VP-ZNNs) with time-varying DCPs have been proposed previously. Compared with finite-parameter ZNNs, these traditional VP-ZNNs are proved to have better convergence, however their downside is that DCPs usually increases over time, and becomes even infinite eventually. Evidently, infinity large DCPs would be lack of robustness are unacceptable in practice, especially the external noises injected. Additionally, even if VP-ZNNs converge over time, the growth of DCPs will result in a huge waste of computing resources. Inspired by that, a new hyperbolic tangent varying-parameter ZNNs (HTVP-ZNNs) with time-varying DCPs are proposed in this paper. Considering the noisy environment, we also develop the robust HTVP-ZNNs (HTVPR-ZNNs). Both of them are able to solve the time-variant Lyapunov and Stein matrix equations in prescribed-time. Theoretically the convergent prescribed-time of the HTVPR-ZNN and the upper time threshold of the DCPs are analyzed mathematically. And numerical experiments and trajectory tracking tasks of the mobile robot substantiate the outstanding convergence of the HTVPR-ZNN schemes.