Iterative trajectory learning for highly accurate optical satellite tracking systems

Within impedance control framework, since force tracking accuracy is affected by the robotic tracking error, an adaptive force control scheme combined with an iterative learning algorithm is proposed in this paper. First, the unknown environment stiffness and location are observed in real time based on the actual contact force and robot position, and the adaptive reference trajectory is obtained by the estimated environment information. Then, position-based impedance control scheme is adopted to adjust the dynamical relationship between the end-effect or position and the contact force. Considering the effect of the position tracking error on the force tracking accuracy, an iterative learning control method is utilized to compensate the motion error effectively by the force error. The proposed method can effectively reduce the steady state force error by simply combining the indirectly adaptive reference trajectory generation technique with position tracking error regulation method. Stability and convergence conditions are presented with a Lyapunov function and frequency domain method. Simulations and experiments are conducted on a biaxial platform to demonstrate the effectiveness of the proposed method.

Purpose: The goal of this work is development of an endoscopic system for three-dimensional localization of tissue surfaces during image-guided surgery. The system has the potential to track tissue movement in real time and to measure the shape of organ surfaces within the endoscopes' fields of view via localization of thousands of surface points. The system is intended to provide real-time localization of surface points as a means to guide coregistration with preoperative anatomic image sets and to provide the constraints required by deformable tissue models. Method and Materials: A benchtop prototype of the system consisting of a pair of conventional endoscopes, a computer-controlled laser scanner, a high-speed CCD camera, and a commercial optical tracking system has been constructed in Creare's laboratory. Calibration of the prototype has been performed by verifying the accuracy of the optical tracking system used to measure the position of the endoscopes, measuring the repeatability of the laser scanner when projecting the laser through an endoscope, characterization of the distortion present in the endoscope optics, and the measurement of the maximum usable field-of-view. The accuracy of the system has been determined based on the difference between three-dimensional coordinates measured by our system with those measured by the commercial optical tracking system. Results: The repeatability of the system to consistently localize a fixed point was determined by measuring the location of 25 points while maintaining fixed positions of the endoscopes and laser mirrors. The mean three-dimensional repeatability of the localizations was 0.08 mm with a standard deviation of 0.05 mm. The accuracy of the system was ascertained relative to measurements made with the optical tracking system and a standard six LED probe, known to have an accuracy on the order of 0.1 mm. Using a planar test phantom with machined semicircular holes, the projecting endoscope laser was steered to the center of each hole, and the position of the illuminated spot determined using the system. The position of each hole was also measured with the six LED probe, and the resulting three-dimensional error found to be 1.9 mm with a standard deviation of 0.3 mm. The system can localize approximately 400 points per second. Conclusion: The use of a scanning laser endoscope has the potential to provide cost-effective, real-time localization of any point within the surgical field that can be simultaneously observed by two endoscopes. The initial results of our work show that points can be localized accurately and rapidly. Educational Objectives: 1. Calibration techniques for endoscopic laser projection. 2. Discussion of endoscopic localization as a means of assisting image- guided surgery.

In this paper, iterative learning identification algorithms are proposed to estimate the time-varying parameters in multi-input-single-output (MISO) Wiener nonlinear time-varying systems. The regression model of the Wiener system is built by using the polynomial expansion of the nonlinear inverse function. Then, two iterative learning algorithms, including iterative learning gradient identification and iterative learning least squares identification, are presented to estimate the time-varying parameters of the regression model. The convergence performance of the iterative learning identification algorithms is analyzed, and numerical simulations are provided to verify the effectiveness of the proposed algorithms.

Optical motion tracking systems are widely employed in virtual- and mixed reality applications enabling users to directly interact with 3D space. Important efforts have been made to make such technologies available to a larger group of users by significantly reducing their deployment costs. In this poster we introduce the result of a German-Brazilian cooperation: The first commercial low-cost marker-based optical tracking system developed in South-America. Despite using cheaper technologies and being low-cost which usually imply worse performance and accuracy, we developed a system with sufficient accuracy for professional use. There have been significant research efforts to develop cheaper tracking devices to provide interaction capabilities in virutal- and mixed reality. The Augmented Reality Tool Kit (ARToolkit) [1] is a good example of a very popular low-cost academic optical tracker working with any type of webcam. As a non-academic example of affordable tracking systems, Nintendo's latest video game console (Nintendo Wii) comes with the Wiimote interaction device containing a built-in motion tracker that allows for immersive interaction metaphors in home environments. However, most such available low-cost solutions lack in suitability for professional applications due to their usually insufficient accuracy, tracking range or latency time. The starting point of this project was PTrack [2], an academic marker-based single-camera tracker developed at Fraunhofer IGD. PTrack development has shown that an accurate optical tracking system can be built from components that are affordable for a much wider group of users, from the academic AR/VR community to companies’ developers. The performance and accuracy of PTrack were assessed through a comprehensive set of experiments which have proved the suitability of the system for professional applications. One of the main topics raised by PTrack as further research was an extension of the system into a multiple stereo camera configuration, which led to the present work.

Aiming at the problem of a great deal of blindly searching the proportional, differential, integral parameters in the process of designing PID controller, the new algorithm combining genetic algorithm with iterative learning algorithm is named as an iterative learning genetic algorithm (ILGA), which can be used to optimize three controller parameters, thus the optimal parameters can be achieved swiftly by virtue of less iterative learning times, and the design of the PID controller is simplified. As the simulation results shown, the effectiveness of the method is verified in optimizing the PID controller parameters

This paper discusses a complex-valued Hopfield associative memory with an iterative incremental learning algorithm. The mathematical proofs derive that the weight matrix is approximated as a weight matrix by the complex-valued pseudo inverse algorithm. Furthermore, the minimum number of iterations for the learning sequence is defined with maintaining the network stability. From the result of simulation experiment in terms of memory capacity and noise tolerance, the proposed model has the superior ability than the model with a complex-valued pseudo inverse learning algorithm.

The authors show that a local iterative learning algorithm of Diederich and Opper (1987) converges for any set of patterns, including linearly dependent sets, and that the resulting coupling constants for a given set of patterns are the same as those given by the pseudoinverse rule for any maximal linearly independent subset of the given set. Thus, the matrix of couplings produced by the algorithm is the desired projection matrix onto the subspace spanned by the training set.

This paper presents the development and field validation of an iterative learning-based admittance control algorithm for autonomous excavation in fragmented rock using robotic wheel loaders. An admittance control strategy is augmented with iterative learning, which automatically updates control parameters based on the error between a target bucket fill weight and the measured fill weight at the end of each excavation pass. The algorithm was validated through full-scale autonomous excavation experiments with a 14-tonne capacity load-haul-dump (LHD) machine and two different types of excavation materials: fragmented rock and gravel. In both excavation scenarios, the iterative learning algorithm is able to update the admittance control parameters for a specified target bucket fill weight, eliminating the need to manually re-tune control parameters as material characteristics change. These results have practical significance for increasing the autonomy of robotic wheel loaders used in mining and construction.

This article studies collaborative human- robot output tracking when the desired output is only known to the human but not to the robot controller. The main contribution of this article is to propose and establish convergence conditions for an iterative learning algorithm that updates the robot input using (i) the effect of the human action on the combined human-robot output tracking (which includes the effect of the human-response dynamics) and (ii) data-inferred human-robot models. This allows the iterative learning control (ILC) to be personalized for each individual human operator. Additionally, experimental results are presented to illustrate the iterative learning approach. Results show that, with the proposed approach, the robot can learn to collaboratively track the output with 10.0% error, which is close to twice the robot noise of 4.6% of the desired output. Furthermore, the data-inferred models provided evidence of the effect of the human operator’s dynamics on the co-tracking task.

This project aimed to create an immersive digital twin laboratory that incorporates advanced tracking and visualization capabilities. In collaboration with Fort Lewis College, NREL designed a state-of-the-art physical visualization laboratory, developed a software platform to enable interaction with tracked physical objects in the laboratory, and provided proof-of-concept curricula that included manipulating these tracked objects. The project was initiated to address the growing need for innovative educational tools in engineering education. As renewable energy systems, particularly solar installations, become more complex, there is a pressing need to bridge the gap between theoretical knowledge and practical application. Traditional methods of teaching solar engineering concepts often fall short of providing students with a comprehensive, hands-on understanding. This immersive digital twin laboratory was conceived to fill that gap by creating a safe, non-energized setting where students can interact with augmented solar installation objects, gaining valuable insights into system performance, design, and maintenance. The project utilized extended reality (XR) technologies, including head-mounted displays (HMDs) and a whole-room optical motion tracking system, to connect physical objects with their digital twins in real time. The laboratory was equipped with MagicLeap 2 HMDs, supported by a Vicon Vero 2.2 Optical Tracking System, which provided precise 6-degrees-of-freedom (6-DOF) tracking. We developed a software platform to manage the interaction between the tracked physical objects and their virtual counterparts, enabling real-time data synchronization, object recognition, and virtual overlays. We designed the system to be flexible and extendable, allowing for future integration of additional objects and curriculum. This research advances the field of engineering education by demonstrating the potential of immersive digital twin environments. The laboratory provides a dynamic learning space where students can experiment, collaborate, and learn without the risks associated with live experimentation. The ability to simulate and manipulate solar installation objects under various conditions has broad implications for workforce development, particularly in renewable energy. The project also highlights the economic feasibility of using XR technologies in educational settings, offering a cost-effective solution for institutions looking to enhance their curriculum. By fostering a deeper understanding of solar energy systems, this work contributes to the broader goal of supporting the global energy transition and preparing the next generation of engineers and technicians.

Navigation with optical tracking sometimes makes it difficult to establish a line-of-sight in cluttered operating theatres. On the other hand, the accuracy of electromagnetic tracking is influenced by ferromagnetic surgical equipment. We compared electromagnetic with optical tracking under controlled conditions for the lateral skull base. Six anatomical specimens were dissected to measure the target registration error (TRE) in and around the petrous bone in a wet laboratory to simulate an intraoperative setting. Specimens were registered with passive optical and electromagnetic tracking. Overall accuracy was better using optical tracking than electromagnetic tracking (0.22 mm; 0.07-0.48 vs 0.99 mm; 0.56-1.27 mm; median, lower and upper quartiles, respectively; p < 0.001). The accuracy of optical tracking was near the resolution of the camera system, whereas the accuracy of electromagnetic tracking was lower. Only optical tracking allows for an application accuracy of considerably less than 1 mm in high-resolution datasets.

In this paper, a new iterative learning algorithm is proposed for repetitive nonlinear systems. The control system employs a combination of state feedback and iterative learning control (ILC) in which the coefficients of states are learned similar to ILC methods. The control system is in a closed loop format both in iteration domain (because of ILC) and in time domain (because of feedback control) which improves the robustness of the conventional ILC. The convergence of the control algorithm is also proved. Finally, simulation results for a 5-DOF manipulator have been presented to illustrate that the proposed algorithm is more robust than a first order P type ILC method at the presence of white Gaussian noise as a nonrepeating disturbance.

Gimbal-less dual axis point-to-point (quasistatic) MEMS mirrors have very wide bandwidths for laser beam steering, however users are often limited to only a third of the bandwidth due to the high Q-factor and use of low-pass filters in open-loop operation to avoid overshoot and oscillation. Closed-loop driving enables the use of the full bandwidth with additional complexity in optics and electronics, which can be undesirable in some low SWaP-C systems. But for many applications which require scanning repetitive patterns, such as LiDAR and biomedical imaging, bandwidth utilization and linearity can be greatly increased without any real-time feedback control by training the device and finding an optimal driving waveform using an iterative learning algorithm. The algorithm drives the device with a trial waveform, measures the scan on a Position Sensing Device (PSD), calculates the error between the desired waveform and the measured position, and adjusts the drive waveform for the next iteration based on an approximate linear device model. This is repeated until the error is reduced to below an acceptable specification. The waveform is then saved in the MEMS Controller and can be reliably used for extended periods of operation. Multiple such drive signals can be trained and stored on the controller to perform different types of scans. Several MEMS mirrors, including single- and dual-axis designs, were studied and three are reported here. Overall, in all cases a high accuracy of optical scans is achieved, typically to within ±0.025° of nominal. Repeatability after training, then running in open loop is better than ±0.01° - however, this measurement was limited by the lower resolution of the position detecting sensor. Scan rates achieved vary based on mirror design, but in each case are greatly improved from those achievable with basic driving approaches. Each mirror demonstrated higher quality vector graphics content at faster refresh rates and stable linear rasters at rates below resonance where lines are scanned with uniform velocity. Additionally, each mirror could achieve stable fast rasters with the line-scanning axis rates just below resonance, giving sinusoidal scans with line rates of ~1.6fres. Finally, each mirror was also demonstrated achieving rasters with rates above resonance, giving line rates of ~2.5fres. In all of those cases the other axis could scan linear and sharp sawtooth or triangle waveforms. Based on the symmetry of the MEMS design, we demonstrated the same performance at ddegerent angles, e.g. rastering at a 45° angle.

Constrained Point-to-Point Iterative Learning Control

In this work, we present a new machine learning-based framework for the accurate estimation of the shot-noise limited photon-counting Poisson channel by developing a state-of-art iterative unsupervised learning algorithm for intelligent optical communications. By accurate estimation, we mean that it achieves very low estimation error even when the effect of the received data is unpredictable. We consider a realistic situation where modulated symbols are assumed to be hidden or unobserved latent variables, thereby making conventional estimation algorithms based on maximum likelihood approach unsuitable or inefficient. In particular, we consider a probabilistic model and assume that the received data is not labeled. With this unpredictable data considered, a novel iterative machine learning framework is developed based on an expectation and maximization algorithm. The proposed algorithm avoids the need to choose an appropriate step size as required in gradient method based algorithms. It is shown that it significantly outperforms the least square and the Viterbi detection technique.