Position Error Research Articles

Prescribed finite-time tracking control with input buffer for a manipulator system

The prescribed finite-time trajectory tracking control problem with input buffer is investigated for a linkage manipulator system. The backstepping method is used to design the prescribed finite-time trajectory tracking controller. A new prescribed finite-time performance function is proposed, which can more easily regulate control performance. With the help of this performance function, a constraint control law is designed for the tracking error, and a better transient performance than the existing prescribed finite-time performance function can be obtained under the same parameter conditions for the trajectory tracking process of the manipulator system. At the same time, a control input buffer function is designed, which solves both the overvoltage problem of control input caused by the overlarge position error of the manipulator and the problem that the manipulator system is difficult to start under heavy load. The simulation experiments verify the effectiveness and superiority of the proposed controller.

Strong Robust LIO System Based on Event Camera Assistance

Abstract. At present, the LiDAR system can achieve high precision and is extensively used for indoor and outdoor mobile positioning and mapping. However, LiDAR systems still face issues in cluttered environments where strong features are absent, leading to a degradation of the LiDAR-based solution. When the carrier movement involves high-speed or prolonged exposure to the mirror wall, it can cause severe degradation issues or even positioning failures in the laser slam system. Event cameras are vision sensors inspired by biology that exhibit strong robustness in high dynamic and low texture environments, potentially leading to better performance in such environments. However, some issues with event cameras remain unresolved. In this paper, a multi-source fusion method based on EVIO, LIO and IMU trajectory layer post-processing method is proposed, which will fully consider the robustness of event camera in high dynamic environment and the high precision advantage of Lidar in conventional environment, and use an algorithm based on normalized uncertainty. The elastic multi-source fusion of event camera and LiDAR is realized and tested in realde environment. Experimental results show that the proposed algorithm can effectively improve the accuracy of event camera and LiDAR. Compared to the current more advanced algorithms, the proposed algorithm effectively addresses the problem of LIO in degraded environments. Additionally, it mitigates the scale inaccuracy and divergence of the EVIO trajectory to some extent. Compared to LIO, the algorithm can reduce the maximum position error by approximately 30% and increase the overall position accuracy by 32%. Additionally, it can significantly constrain the divergence of errors in the Y direction, improving its accuracy by about 75% and 65% compared to the LIO and EVIO algorithms, respectively.

Priority-based intelligent resolution method of multi-aircraft flight conflicts

Abstract The rising demand for air traffic will inevitably result in a surge in both the number and complexity of flight conflicts, necessitating intelligent strategies for conflict resolution. This study addresses the critical challenges of scalability and real-time performance in multi-aircraft flight conflict resolution by proposing a comprehensive method that integrates a priority ranking mechanism with a conflict resolution model based on the Markov decision process (MDP). Within this framework, the proximity between aircraft in a multi-aircraft conflict set is dynamically assessed to establish a conflict resolution ranking mechanism. The problem of multi-aircraft conflict resolution is formalised through the MDP, encompassing the design of state space, discrete action space and reward function, with the transition function implemented via simulation prediction using model-free methods. To address the positional uncertainty of aircraft in real-time scenarios, the conflict detection mechanism introduces the aircraft’s positional error. A deep reinforcement learning (DRL) environment is constructed incorporating actual airspace structures and traffic densities, leveraging the Actor Critic using Kronecker-factored Trust Region (ACKTR) algorithm to determine resolution actions. The experimental results indicate that with 20–30 aircraft in the airspace, the success rate can reach 94% for the training set and 85% for the test set. Furthermore, this study analyses the impact of varying aircraft numbers on the success rate within a specific airspace scenario. The outcomes of this research provide valuable insights for the automation of flight conflict resolution.

Design and kinematics of a novel rigid-flexible coupling hybrid robot for aeroengine blades in situ repair

PurposeThis paper aims to present the mechanical design and kinematics of a novel rigid-flexible coupling hybrid robot to develop a promising aeroengine blades in situ repair technology.Design/methodology/approachAccording to requirements analysis, a novel rigid-flexible coupling hybrid robot is proposed by combining a three degrees of freedom (DOF) parallel mechanism with a flexible continuum section. Then the kinematics models of both parallel mechanism and flexible continuum section are derived respectively. Finally, based on equivalent joint method, a two-step numerical iterative inverse kinematics algorithm is proposed for the whole robot: (1) the flexible continuum section is equivalently transformed to a 2-DOF spherical joint, thus the approximate analytical inverse kinematic solution can be obtained; (2) the accurate solution is derived by an iterative derivation of both parallel mechanism and flexible continuum section.FindingsTo verify structure scheme and the proposed kinematics modeling method, numerical simulations and prototype experiments are implemented. The results show that the proposed kinematics algorithm has sufficient accuracy and computational efficiency in the whole available workspace, that is end-effector position error and orientation error are less than 0.2 mm and 0.01° respectively, and computation time is less than 0.22s.Originality/valueA novel rigid-flexible coupling hybrid robot for aeroengine blades in situ repair is designed. A two-step numerical iterative inverse kinematics algorithm is proposed for this unique hybrid robots, which has good accuracy and computational efficiency.

Kinematic calibration of a 5-DOF double-driven parallel mechanism with sub-closed loop on limbs

Abstract This paper proposes a kinematic calibration method of a novel 5-degree-of-freedom double-driven parallel mechanism with the sub-closed loop on limbs. At first, considering the introduction of a sub-closed loop significantly increased the complexity and difficulty of kinematic error modeling, an equivalent transformation method is proposed for the limb with a sub-closed loop. Then kinematic error model of the parallel mechanism is established based on the closed-loop vector method and parasitic motion analysis, which is verified by virtual prototype technology. Because the full kinematic error model is generally redundant, error parameter identifiability analysis is carried out by QR decomposition of the identification Jacobian matrix, and the redundant parameters are removed. Additionally, the Sequence Forward Floating Search algorithm is utilized to optimize measurement configurations to reduce the influence of measurement noise. Finally, with a laser tracker as the measuring device, numerical simulations and experiments are implemented to verify the proposed kinematic calibration method. The experiment results show that average position and orientation errors are reduced from 2.778 mm and 1.115° to 0.263 mm and 0.176°, respectively, within the prescribed workspace.

Human Fall Motion Prediction: Fall Motion Forecasting and Detection with GRU

The human fall motion prediction system is a preventive tool aimed at reducing the risk of falls. In our research, we developed a deep learning model that utilizes pose estimation to track human body posture and integrated this with a Gated Recurrent Unit (GRU) to forecast human motion and predict falls. GRU, an enhancement of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) models offers improved memorization and more efficient memory usage and performance. Our study presents the human fall motion prediction, which combines the forecasting and classification of potential falls.The CAUCAFall dataset is used as the benchmark of this study, which contains the image sequences of single human motion with ten actions conducted by ten actors. We employed the YOLOv8 Pose model to track the 2D human body pose as the input in our system. A thorough evaluation of the CAUCAFall dataset highlights the effectiveness of our proposed system. Evaluation using the CAUCAFall dataset demonstrates that the model achieved a Mean Per Joint Position Error (MPJPE) of 4.65 pixels from the ground truth, with a 70% accuracy rate in fall prediction. However, the model also exhibited a Mean Relative Error (MRE) of 0.3, indicating that 30% of the predictions were incorrect. These findings underscore the potential of the GRU-based system in fall prevention

Human-Centered Coordination for Robot-Assisted Equipment Transport

Abstract This work explores how to use an unmanned ground vehicle (UGV) to offload the physical burdens of equipment from humans. This work formulates Dynamic Alignment Following and compares it to Position-Based Following techniques. We describe the control strategies of both following methods and implement them in a dynamic simulation and a physical prototype. We test the performance of the two following methods and show that Dynamic Alignment Following can reduce robot positional error and interaction force between the human and the robot. We then analyze the energetics and the performance of the human-UGV team for candidate transportation tasks. The presence of the robot can make some tasks take longer to perform. Nonetheless, the results show that for the candidate tasks, the robot can reduce human average metabolic power and average overall task energy.

Table tennis ball landing control in a robotic system by cameras

Abstract Controlling the landing position of a spinning ball is difficult when using a table tennis robot. A complete physical model requires the factoring in of aerodynamic elements and object collisions, and inaccurate environmental coefficients would increase the landing position error. This study proposed a landing position control method based on a cascade neural network (CNN) that consists of forward and recurrent neural networks (RNNs). The forward NNs are used to estimate the velocity of the outgoing ball according to the velocity and acceleration of the incoming ball captured by cameras and the desired velocity of the outgoing ball. The RNN is employed to reverse-predict ball displacement based on the state of the incoming ball, desired landing point, and ball flight duration. The experiments verified that the method proposed in this study achieved control of differently spinning balls more effectively than the locally weighted regression (LWR)-based model did. The success rate of the CNN at two of six desired landing points was 25.9% and 32.9% higher, respectively, compared with use of the LWR-based model.

High precision DSRC and LiDAR data integration positioning method for autonomous vehicles based on CNN

In order to improve the safe driving and automatic positioning capability of autonomous vehicles, a high-precision DSRC and LiDAR data integration positioning technology for autonomous vehicles based on CNN is proposed. Import the data of Dedicated Short Range Communications and Light Detection and Ranging for automatic driving vehicle positioning, carry out kinematic analysis of autonomous driving vehicles under multi-sensor fusion, and transform the data of DSRC and LiDAR sensors into tightly coupled coordinate systems; The CNN depth learning method is used to compensate the position and attitude tracking estimation error under the overall time stamp synchronization of the sensor through adaptive information tracking; The first and second order feedforward compensation is made for the positioning parameters of the autonomous driving vehicle using the PID model, and the point cloud feature matching model is fused to complete the estimation of the positioning attitude parameters of the autonomous driving vehicle. In order to eliminate the noise interference under the DSRC communication mechanism, the Kalman filter function is used to automatically optimize the constraint parameters in the point cloud feature detection model, and the positioning error parameters are dynamically filtered and adjusted; Kinematics analysis is carried out for the driving state of the vehicle, and the positioning error in the vehicle movement is controlled through the difference technology to achieve high-precision DSRC and LiDAR data integration positioning. The simulation results show that this method can integrate and locate the high-precision DSRC and LiDAR data of the autonomous vehicle, and the attitude estimation and positioning accuracy of the vehicle is good, while the error of the attitude parameter estimation of the autonomous vehicle is low.

Research on Surface Defect Positioning Method of Air Rudder Based on Camera Mapping Model

(1) Background: Air rudders are used to control the flight attitude of aircraft, and their surface quality directly affects flight accuracy and safety. (2) Method: Traditional positioning methods can only obtain defect location information at the image level but cannot determine the defect’s physical surface position on the air rudder, which lacks guidance for subsequent defect repair. We propose a defect physical surface positioning method based on a camera mapping model. (3) Results: Repeated positioning experiments were conducted on three typical surface defects of the air rudder, with a maximum absolute error of 0.53 mm and a maximum uncertainty of 0.26 mm. Through hardware systems and software development, the real-time positioning function for surface defects on the air rudder was realized, with the maximum axial positioning error for real-time defect positioning being 0.38 mm. (4) Conclusions: The proposed defect positioning method meets the required accuracy, providing a basis for surface defect repair in the air rudder manufacturing process. It also offers a new approach for surface defect positioning in similar products, with engineering application value.

Transit-guided radiation therapy: a novel patient monitoring approach

Background and purposeTransit-Guided Radiation Therapy (TGRT) is a novel technique that uses the transit portal images (TPIs) acquired with Electronic Portal Image Devices (EPID) to quantify patient position errors during the treatment. It has been validated using anthropomorphic phantoms but a validation in a clinical setting was lacking. A pilot clinical study is presented to confirm our previous results. Materials and methodsA prospective study was conducted between June and December 2022 with patients who received whole-brain or breast radiotherapy treatments. The selected treatments were composed of radiation fields using skin-flash, where the body contour projected a sharp edge on the EPID which has been used as a surrogate of the true patient position. Daily imaging procedures were applied as scheduled before running the one- and two-parameter model (1PM and 2PM) of the TGRT formalism on the acquired TPIs to independently estimate the patient position errors. Results43 patients and 1015 TPIs have been assessed. The 2PM showed a better correlation with the true position errors (R2 = 0.76 vs. 0.73), a lower detection threshold (0.77 mm vs. 1.24 mm), and a lower overcorrection risk above the detection threshold (7.0 % vs. 11.1 %) than the 1PM. Overall, the 2PM would have significantly reduced the true position errors by a factor of 0.58 (0.49 – 1.27) (p < 0.0001). ConclusionThe TGRT technique has confirmed the ability to reduce the position errors in a clinical setting, demonstrating the potential to enhance the patient position monitoring without increasing treatment time or patient dose.

Innovation Adaptive UKF Train Location Method Based on Kinematic Constraints

To address the issue of reduced positioning accuracy caused by satellite signal interruptions when trains pass through long tunnels, a novel train positioning method based on an innovative adaptive unscented Kalman filter (UKF) under kinematic constraints is proposed. This method aims to improve the accuracy of the location of trains during operation. By considering the dynamic characteristics of the train, a dynamic kinematic-constrained inertial navigation system (INS)/odometer (ODO) combination positioning system is established. This system utilizes kinematic constraints to correct the accumulated errors of the INS. Additionally, the algorithm incorporates real-time estimation of the measurement noise covariance using innovation sequences. The updated adaptive estimation algorithm is applied within the UKF framework for nonlinear filtering, forming the innovative adaptive UKF algorithm. At each time step, the difference between the ODO sensor data and the INS output is used as the measurement input for the innovative adaptive UKF algorithm, enabling global estimation. This process ultimately yields the actual positioning result for the train. Simulation results demonstrate that the innovative adaptive UKF train positioning method, incorporating kinematic constraints, effectively mitigates the impact of satellite signal interruptions. Compared with the traditional INS/ODO positioning method, the innovative adaptive UKF method reduces position errors by 34.35% and speed errors by 36.33%. Overall, this method enhances navigation accuracy, minimizes train positioning errors, and meets the requirements of modern train positioning systems.

Accuracy and efficiency of 2D/3D single-vertebra spine navigation registration method based on dual-view feature fusion

Objective: To investigate the accuracy and efficiency of spine 2D/3D preoperative CT and intraoperative X-ray registration through a framework for spine 2D/3D single-vertebra navigation registration based on the fusion of dual-position image features. Methods: The preoperative CT and intraoperative anteroposterior (AP) and lateral (LAT) X-ray images of 140 lumbar spine patients who visited Huashan Hospital Affiliated to Fudan University from January 2020 to December 2023 were selected. In order to achieve rapid and high-precision single vertebra registration in clinical orthopedic surgery, a designed transformation parameter feature extraction module combined with a lightweight module of channel and spatial attention (CBAM) was used to accurately extract the local single vertebra image transformation information. Subsequently, the fusion regression module was used to complement the features of the anterior posterior (AP) and lateral (LAT) images to improve the accuracy of the registration parameter regression. Two 1×1 convolutions were used to reduce the parameter calculation amount, improve computational efficiency, and accelerate intraoperative registration time. Finally, the regression module outputed the final transformation parameters. Comparative experiments were conducted using traditional iterative methods (Opt-MI, Opt-NCC, Opt-C2F) and existing deep learning methods convolutional neural network (CNN) as control group. The registration accuracy (mRPD), registration time, and registration success rate were compared among the iterative methods. Results: Through experiments on real CT data, the image-guided registration accuracy of the proposed method was verified. The method achieved a registration accuracy of (0.81±0.41) mm in the mRPD metric, a rotational angle error of 0.57°±0.24°, and a translation error of (0.41±0.21) mm. Through experimental comparisons on mainstream models, the selected DenseNet alignment accuracy was significantly better than ResNet as well as VGG (both P<0.05). Compared to existing deep learning methods [mRPD: (2.97±0.99) mm, rotational angle error: 2.64°±0.54°, translation error: (2.15±0.41) mm, registration time: (0.03±0.05) seconds], the proposed method significantly improved registration accuracy (all P<0.05). The registration success rate reached 97%, with an average single registration time of only (0.04±0.02) seconds. Compared to traditional iterative methods [mRPD: (0.78±0.26) mm, rotational angle error: 0.84°±0.57°, translation error: (1.05±0.28) mm, registration time: (35.5±10.5) seconds], registration efficiency of the proposed method was significantly improved (all P<0.05). The dual-position study also compensated for the limitations in the single-view perspective, and significantly outperforms both the front and side single-view perspectives in terms of positional transformation parameter errors (both P<0.05). Conclusion: Compared to existing methods, the proposed CT and X-ray registration method significantly reduces registration time while maintaining high registration accuracy, achieving efficient and precise single vertebra registration.

Proprioception and its relationship with range of motion in hypermobile and normal mobile children.

To investigate differences in proprioception using four proprioceptive tests in children with and without hypermobility. Additionally, it was tested if the results on one proprioceptive test predict the results on the other tests. Of the children (8-11years), 100 were classified as normal mobile (Beighton score 0-4) and 50 as hypermobile (Beighton score 5-9). To test proprioception, in the upper extremity the unilateral and bilateral joint position reproduction tasks were used and for the lower extremity the loaded and unloaded wedges task. No differences were found in any of the proprioception tests between the two groups. Estimating the height of the wedges was easier in the loaded position (mean penalty in standing and sitting position, 4.78 and 6.19, respectively). Recalling the elbow position in the same arm resulted in smaller errors compared to tasks reproducing the position with the contralateral arm. Of the four angles used (110°, 90°, 70°, 50°), the position recall in the 90° angle had the smallest position error (1.8°). Correlations between the proprioception tests were weak (Loaded and Unloaded (r 0. 28); Uni and Bilateral (r 0.39), Upper and Lower extremity not significant). No indication of poorer proprioception was found in children with hypermobile joints compared to their normal mobile peers. Loading gives extra information that leads to fewer errors in the wedges task performed while standing, but this effect is independent of joint mobility. Proprioception test outcomes are dependent on the test used; upper extremity results do not predict lower extremity outcomes or vice versa.

The Correlation Between Cervical Proprioception and Scapular Dyskinesis in Patients With Neck Pain: A Case-Control Study.

Introduction Neck pain is common among office workers, and the assessment of cervical proprioception and scapular dyskinesis is key in the management of patients with neck pain. While some studies have shown the relevance of both factors in neck pain patients, the correlation between the two parameters has not yet been investigated. Hence, this study aimed to determine the correlation between cervical proprioception and scapular dyskinesis in workplacecomputer userswithout neck pain (control group) versus those with neck pain (case group). Methodology A case-control, correlational study was performed within office settings; based on the selection criteria, 88 participants were included and categorized into two groups consisting of 44 workplacecomputer users without neck pain in the control group (Group A) and 44 workplacecomputer users with neck pain in the case group (Group B). Cervical proprioception was evaluated by using the joint position error (JPE) test and scapular dyskinesis was assessed using the modified lateral scapular slide test (MLSST). Results There was a statistically significant difference in cervical proprioception and scapular dyskinesis between the case and control groups (p<0.001). Workplacecomputer users with neck pain showed greater JPEcompared to those without neck pain, and scapular dyskinesis was observed in the case group. Moreover, Spearman's correlation coefficient showed a significant correlation between cervical proprioception and scapular dyskinesis in workplacecomputer users with neck pain. Conclusions The present study provides guidance on the assessment as well as management of JPE with different positions of scapular dyskinesia. The evaluation of scapular dyskinesis is frequent clinically; given its positive correlation, managing JPE in neck pain patients is feasible.

Accuracy of robotic arm-assisted versus computed tomography-based navigation in total hip arthroplasty using the direct anterior approach: a retrospective study

BackgroundA robotic arm-assisted and a computed tomography (CT)- based navigation system have been reported to improve the accuracy of component positioning in total hip arthroplasty (THA). However, no study has compared robotic arm-assisted THA (rTHA) to CT-based navigated THA (nTHA) concerning accuracy of cup placement and acetabular fractures using the direct anterior approach (DAA). This study aimed to compare the accuracy of cup placement and the presence of intraoperative acetabular fractures between rTHA and nTHA using DAA in the supine position.MethodsWe retrospectively investigated 209 hips of 188 patients who underwent rTHA or nTHA using DAA (rTHA using the Mako system: 85 hips of 79 patients; nTHA: 124 hips of 109 patients). After propensity score matching for age and sex, each group consisted of 73 hips. We evaluated clinical and radiographic outcomes, comparing postoperative cup orientation and position, measured using a three-dimensional templating software, to preoperative CT planning. Additionally, we investigated the prevalence of occult acetabular fracture.ResultsClinical outcomes were not significantly different between the groups at 1 year postoperatively. The mean absolute error of cup orientation was significantly smaller in the rTHA group than in nTHA (inclination: 1.4° ± 1.2° vs. 2.7° ± 2.2°, respectively; p = 0.0001, anteversion: 1.5° ± 1.3° vs. 2.2° ± 1.7°, respectively; p = 0.007). The cases within an absolute error of 5 degrees in both RI and RA were significantly higher in the rTHA (97.3%) than in nTHA group (82.2%) (p = 0.003). The absolute error of the cup position was not significantly different between the two groups. The prevalence of occult acetabular fracture did not differ significantly between the two groups (rTHA: n = 0 [0%] vs. nTHA: n = 1 [1.4%]).ConclusionCup placement using DAA in the supine position in rTHA was more accurate with fewer outliers compared to nTHA. Therefore, rTHA performed via DAA in a supine position would be useful for accurate cup placement.

Coupled error super-twisted sliding mode active fault-tolerant control for robotic system with actuator fault.

This paper focuses on a robotic system with actuator fault and presents a sliding mode active fault-tolerant control method based on coupled position error. The actuator fault is first detected by a fault alerter with a predetermined threshold. After the fault is successfully detected, the fault is estimated by a fault observer. Coupling the position error with the weighted position error of different joints, a non-singular fast terminal sliding mode surface is constructed, and a super-twisted algorithm is introduced to design a coupled error super-twisted sliding mode controller (CESSMC). Furthermore, the fault estimation is incorporated with the CESSMC to accomplish the coupled error super-twisted sliding mode active fault-tolerant control. The stability and the finite-time convergence of the system are theoretically proved. Simulations and experiments are conducted to verify the effectiveness of the proposed method. The proposed method can achieve the position error convergence in finite time and reduce chattering in the control input. With the tight coupling between position error and weighted position error, the fault compensation effect and trajectory tracking accuracy can be improved.

Peak-CNN: improved particle image localization using single-stage CNNs

AbstractAn important step in the application of Lagrangian particle tracking (LPT) or in general for image-based single particle identification techniques is the detection of particle image locations on the measurement images and their sub-pixel accurate position estimation. In case of volumetric measurements, this constitutes the first step in the process of recovering 3D particle positions, which is usually performed by triangulation procedures. For two-component 2D measurements, the particle localization results directly serve as input to the tracking algorithm. Depending on the quality of the image, the shape and size of the particle images and the amount of particle image overlap, it can be difficult to find all, or even only the majority, of the projected particle locations in a measurement image. Advanced strategies for 3D particle position reconstruction, such as iterative particle reconstruction (IPR), are designed to work with incomplete 2D particle detection abilities but even they can greatly benefit from a more complete detection as ambiguities and position errors are reduced. We introduce a convolutional neural network (CNN) based particle image detection scheme that significantly outperforms current conventional approaches, both on synthetic and experimental data, and enables particle image localization with a vastly higher completeness even at high image densities.

Adaptive positioning of large-scale aircraft components using a simplified image-based visual servoing via online measurement of ball-head center

In digital alignment systems, ensuring fast and accurate alignment between the ball head on large aircraft components and the ball socket of the positioner is the key to aircraft assembly efficiency and quality. Image-based visual servoing is one of the most advanced techniques with high efficiency and positioning accuracy, but it does not account for measuring the ball-head position. Moreover, variable target positions, similar feature interference, and poor lighting conditions pose great challenges to the fast and robust visual measurement of ball-head position. Hence, an online visual measurement method is introduced to measure the absolute position of the ball head in real time. An adaptive region of interest extraction algorithm and an improved edge following method are introduced to extract the elliptical contour from the ball-head image, and ellipse detection is achieved through a simple triplet selection algorithm. A geometric model based on the perspective projection of the sphere is established for estimating the ball-head center. Then, the simplified image-based visual servoing method is developed by constantizing the image Jacobian matrix. Next, an adaptive positioning method of large-scale aircraft components with multiple ball heads is proposed from a holistic perspective. Finally, experiments show that the final positioning accuracy of each ball head in the X/Y plane is less than ± 0.05 mm, with an efficiency almost ten times that of the conventional method. After adaptive adjustment, the position and angle errors of the simulated component are reduced within 0.6 mm and 0.3°, respectively.

Enhancing accuracy of surgical stylus-tip tracking: A comparative calibration study

Accurate determination of the position of surgical stylus-tip is crucial for informed decision-making in computer-assisted surgery. This study assesses the accuracy of various calibration methods for stylus-tip tracking. A novel template-based calibration method is proposed, offering a practical protocol for assessing surgical stylus-tip accuracy. Relative position and orientation errors are measured using two custom-made phantoms and compared with those from existing techniques. Experimental results shows that our proposed method achieves greater positional accuracy (mean: 1.73 mm; range: 0.53-3.42 mm) compared to the geometry-based method (mean: 3.78 mm; range: 2.52-5.05 mm) and the algebraic-based method (mean: 2.47 mm; range: 1.52-3.52 mm). In terms of orientation accuracy, our method also performs slightly better (mean: 0.95 deg; range: 0-3.41 deg) than the geometry-based method (mean: 1.03 deg; range: 0-3.36 deg) and the algebraic-based method (mean: 1.01 deg; range: 0-3.22 deg). The advantages of our method are highlighted, demonstrating its potential for application in the operating room.