Position Drift Research Articles

Trajectory shaping guidance for impact angle control of planetary hopping robots

This paper presents a novel optimal trajectory-shaping control concept for a planetary hopping robot. The hopping robot suffers from uncontrolled in-flight and undesired after-landing motions, leading to a position drift at landing. The proposed concept thrives on the Generalized Vector Explicit (GENEX) guidance, which can generate and shape the optimal trajectory and satisfy the end-point constraints like the impact angle of the velocity vector. The proposed concept is used for a thruster-based hopping robot, which achieves a range of impact angles, reduces the position drift at landing due to the undesired in-flight and after-landing motions, and handles the error in initial hopping angles. The proposed approach’s conceptual realization is illustrated by lateral acceleration generated using thruster orientation control. Extensive simulations are carried out on horizontal and sloped surfaces with different initial and impact angle conditions to demonstrate the effect of impact angle on the position drift error and the viability of the proposed approach.

Numerical study of vehicle motion during water exit under combined lifting force and wave action

During the retrieval process of the deep-sea mining vehicle (DSMV), the stability of the retrieval system is strongly influenced by the interaction between the vehicle body and the surrounding seawater due to the vehicle's complex shape and wave motion. Naturally, the negative side effects of significant changes in the vehicle's attitude and the water exit position can only increase retrieval's challenge. To investigate the characteristic of the flow field of the DSMV, this study employs the computational fluid dynamics method based on the Reynolds-averaged Navier–Stokes equations integrating the volume-of-fluid multiphase flow model with a fifth-order Stokes-wave model to explore the attitude and displacement changes of the vehicle during the water exit process in the ocean wave environment. The results indicate that the wave phase and lifting force are the major effect factors in the DSMV's water exit process. An appropriate lifting force under a specific wave phase can effectively reduce attitude changes and positional drift of the DSMV during water exit, thereby enhancing recovery efficiency and stability.

Algebraic symmetries of the observables on the sky: Variable emitters and observers

In this paper we prove a number of exact relations between optical observables, such as trigonometric parallax, position drift, and the proper motion of a luminous source, in addition to the variations of redshift and the viewing angle. These relations are valid in general relativity for any spacetime, and they are of potential interest for astrometry and precise cosmology. They generalize the well-known Etherington’s reciprocity relation between the angular diameter distance and the luminosity distance. Similar to the Etherington’s relation, they hold independently of the spacetime metric, the positions, and the motions of a light source or an observer. We show that those relations follow from the symplectic property of the bilocal geodesic operator, i.e., the geometric object that describes the light propagation between two distant regions of a spacetime. The set of relations we present is complete in the sense that no other relations between those observables should hold, in general. In the meantime, we develop the mathematical machinery of the bilocal approach to light propagation in general relativity and its corresponding Hamiltonian formalism. Published by the American Physical Society 2024

An enhanced stochastic error modeling using multi-Gauss–Markov processes for GNSS/INS integration system

Angular random walk (ARW), rate random walk (RRW), and bias instability (BI) are the main noise types in inertial measurement units (IMUs) and thus determine the navigation performance of IMUs. BI is the flicker noise, which determines the noise level of an inertial sensor. The traditional error modeling approach involves modeling the ARW and BI processes as RRW or Gauss–Markov (GM) processes, and this approach is applied as a suboptimal filter in the global navigation satellite system (GNSS)/inertial navigation system (INS) extended Kalman filter (EKF). In this paper, the random error identification processes for white noise and colored noise for inertial sensors are separated using the Allan variance and power spectral density methods and the equivalence of the stochastic process differential equations of bias instability and a combination of multiple first-order GM processes are derived. A colored noise compensation method is proposed based on the enhanced EKF model. Experimental results demonstrate that, compared to traditional error models, our proposed model reduces positional drift error in dynamic testing from 195 to 49 m, enhancing positional accuracy by 40.2%. These findings confirm the potential and superiority of our method in complex navigation environments.

Autonomous Navigation UAVs for Enhancing Information Freshness for Reconnaissance

The demand for autonomous navigation UAVs in reconnaissance is steadily increasing. One crucial aspect of these missions is the freshness of reconnaissance information, which serves as a vital indicator of mission effectiveness. However, there is a lack of targeted investigation in the research on autonomous single/multi-UAV missions and joint path planning. Furthermore, the use of visual–inertial odometry (VIO) in rotary-wing UAVs can lead to significant positional drift during flight, which may result in the loss of the UAV and mission failure. This paper investigates joint planning problems in single/multi-UAV reconnaissance missions under both GPS-available and GPS-unavailable scenarios and proposes an integrated data collection and beacon-assisted localization approach. Finally, the numerical results demonstrate the effectiveness of the proposed scheme in enhancing the freshness of reconnaissance information.

A miniature prototype of Time Projection Chambers for CSR External-Target Experiment

A miniature prototype of time projection chamber for the cooling storage ring external-target experiment (CEE) at the Heavy Ion Research Facility in Lanzhou (HIRFL) was designed for three-dimensional tracking of charged particles produced in heavy-ion collisions at the target region and for particle identification. The prototype consists of a TPC detector chamber with active volume of 9 cm (height), 10 cm (length) and 10 cm (width), gas electron multiplier (GEM) detector readout and SAMPA-based electronics. This work describes design, track reconstruction method and performance of the prototype. The energy resolution is 11.03% sigma using a 55Fe x-ray source. Using heavy-ion beam of 150 MeV/u Fe, the test results show that the two-dimensional position resolution and drift time resolution of the prototype are better than 400 μm and 20 ns, respectively.

Investigation on centrifugal deformation effect of magnetic levitation superconducting rotor

The high-speed rotating superconducting rotor can be made into a high-precision inertial device. Centrifugal deformation is one of the key factors affecting the drift speed of the rotating superconducting rotor's polar axis. The larger the drift speed, the worse the accuracy of the inertial device. Applying magnetic torque to the superconducting rotor to compensate for the magnetic disturbance torque generated by centrifugal deformation is one of the effective methods to improve the measurement accuracy of the inertial device made of a superconducting rotor. To compensate for the magnetic disturbance torque caused by centrifugal deformation accurately, we studied the centrifugal deformation effect of the rotating superconducting rotor. In this paper, the centrifugal deformation of the superconducting rotor is analyzed first. And then the influence of centrifugal deformation of the superconducting rotor on magnetic force was studied by Finite Element Method (FEM). The results show that centrifugal deformation can reduce the magnetic levitation force of the superconducting rotor, leading to suspension position drift. Finally, the drift speed of the superconducting rotor's polar axis caused by centrifugal deformation is investigated, including the drift speed caused by centrifugal deformation of the rotating superconducting rotor, as well as the drift speed generated by the coupling of centrifugal deformation and suspension position drift of the superconducting rotor. The research results provide a reference for more accurate compensation of magnetic disturbance torque caused by centrifugal deformation and further improve the accuracy of superconducting rotor inertial devices.

Visual Feature Tuning Properties of Short-Latency Stimulus-Driven Ocular Position Drift Responses during Gaze Fixation.

Ocular position drifts during gaze fixation are significantly less well understood than microsaccades. We recently identified a short-latency ocular position drift response, of ∼1 min arc amplitude, that is triggered within <100 ms by visual onsets. This systematic eye movement response is feature-tuned and seems to be coordinated with a simultaneous resetting of the saccadic system by visual stimuli. However, much remains to be learned about the drift response, especially for designing better-informed neurophysiological experiments unraveling its mechanistic substrates. Here we systematically tested multiple new feature tuning properties of drift responses. Using highly precise eye tracking in three male rhesus macaque monkeys, we found that drift responses still occur for tiny foveal visual stimuli. Moreover, the responses exhibit size tuning, scaling their amplitude (both up and down) as a function of stimulus size, and they also possess a monotonically increasing contrast sensitivity curve. Importantly, short-latency drift responses still occur for small peripheral visual targets, which additionally introduce spatially directed modulations in drift trajectories toward the appearing peripheral stimuli. Drift responses also remain predominantly upward even for stimuli exclusively located in the lower visual field and even when starting gaze position is upward. When we checked the timing of drift responses, we found it was better synchronized to stimulus-induced saccadic inhibition than to stimulus onset. These results, along with a suppression of drift response amplitudes by peristimulus saccades, suggest that drift responses reflect the rapid impacts of short-latency and feature-tuned visual neural activity on final oculomotor control circuitry in the brain.

Computer vision defect detection on unseen backgrounds for manufacturing inspection

Visual defect detection is an important aspect of quality inspection. High performance manufacturing can benefit from automating defect detection, and deep learning techniques are the current state of the art for this computer vision task. The premise of deep learning is that an over parameterized model learns to generalize in performing a task such as classification or object detection by exposure to a wide variety of training examples. In the canonical example, a classifier that has seen thousands of pictures of dogs and cats in different situations and backgrounds will be able to generalize to tell one animal from the other in a newly obtained photo. The assumption that the training data contains great variety is not met in typical manufacturing data sets. Data is highly repetitive and mostly represents defect-free products, meaning there are few images of defects or deviations to learn from. In this training regime, deep learning models are easily over-fit to the training data and can fail to detect defects in the face of variations such as position, lighting, or data drift. In this work, we explore training defect detection models with images of defects on different backgrounds and in different locations, in order to approximate the exposure to highly diverse data sets that is an assumption of a well-trained deep learning model. We demonstrate how models trained on diverse images containing a common defect type can pick defects out in new circumstances. Such generic models could be more robust to new defects not found in data collected for training, and can reduce data collection impediments to implementing visual inspection on production lines. Additionally, we demonstrate that object detection models trained to predict a label and bounding box outperform classifiers that predict a label only on held out test data typical of manufacturing inspection tasks. Finally, we studied the factors that affect generalization in order to train models that work under a wider range of conditions.

Investigating focal spot position drift in a mobile imaging system equipped with a monobloc-based x-ray generator.

Misalignment or double-contouring artifacts can appear in high-resolution 3D cone beam computed tomography (CBCT) images, potentially indicating geometric accuracy issues in the projection data. Such artifacts may go unnoticed in low-resolution images and could be associated with changes in the focal spot (FS)position. High-resolution 3D-CBCT imaging by a mobile imaging device with a large gantry clearance offers more versatility for clinical workflows in image-guided brachytherapy (IGBT), intraoperative radiation therapy (IORT), and spinal, as well as maxillofacial surgery. However, misalignment or double-contouring artifacts hinder workflow advancements in these domains. This paper introduces intrinsic calibration and geometrical correction methods as extensions to a well-established technique for addressing geometrical deviations resulting from factors such as gravity or mechanical inconsistencies. These extensions cover shifts and drifts of the FS depending on FS size selection, temperature, tube current, and tube potential. The proposed methods effectively mitigate artifacts in high-resolution CBCT images stemming from geometrical inaccuracies in projection data, without requiring additional equipment like a pinholedevice. Geometrical offsets and drifts of the x-ray tube FS were characterized on a mobile multi-purpose imaging system, the ImagingRing-m. A pinhole-like experiment was simulated by adjusting the movable collimation unit to a small rectangular aperture within the FS size range. The influence of filament selection, that is, FS size, temperature, the relatively low tube currents, as well as tube potential settings have been studied on two different monobloc types sharing the same x-ray tube insert. The Catphan 504 and an Alderson head phantom were used to assess resulting imageartifacts. Switching the FS size to one different from what was used for geometrical (gravitation, mechanical variations) calibration induced the most notable position changes of the x-ray FS, resulting in double-contouring artifacts and blurring of high-resolution 3D-CBCT images. Incorporating these shifts into a geometrical correction method effectively minimized these artifacts. Thermal drifts exhibited the second largest geometrical changes, comparable to FS size shifts across the thermal operating conditions of the x-ray system. The proposed thermal drift compensation markedly reduced thermal drift effects. Tube current and potential had little impact within the range of available tube currents, eliminating the need for compensation in currentapplications. Augmenting the geometrical calibration pipeline with proposed FS drift compensations yielded significant enhancements in image quality for high-resolution reconstructions. While compensation for thermal effects posed challenges, it proved achievable. The roles of tube current and potential were found to benegligible.

Implementing dispersion measurement as part of scanning proton therapy commissioning and quality assurance

Introduction. Dispersion in an accelerator quantifies the deviation of the proton trajectory when there is a momentum deviation. We present for the first time a safe method of measuring dispersion in the clinic, using a scintillator detector and the momentum deviations within a spill. This is an important accelerator quantity as we found that this is the reason behind the large dose fluctuation in our absolute dosimetry measurement. Methods. Dispersions are measured for nine energies in a Hitachi ProBeat system at three locations (isocenter and at two profile monitors) and at two gantry angles (0 and 90 degrees) by first measuring the spot position and momentum drift within a spill. The spot position drift is measured by the XRV-4000 at the isocenter, and by the two profile monitors located at 0.57 and 2.27 m from the isocenter. The momentum drift is calculated from the intra-spill range drift which is measured using the Ranger accessory. The dispersion at isocenter and its gradient are calculated using the weighted least square regression on the measured dispersions at the three locations. A constraint is formulated on the dispersion and its gradient to ensure minimal intra-spill spot position deviation around the isocenter. Results. The measured intra-spill range and spot positional drift at isocenter are less than and respectively. The momentum spread calculated from the range drift are less than 0.08%. The dispersion at the isocenter ranged from to and the zero-crossing happens upstream of isocenter for all energies. 2 of the 9 energies (168.0 and 187.5 MeV) violated the constraint and has an intra-spill spot positional deviation greater than within from the isocenter. Conclusion. This measurement is recommended as part of commissioning and annual quality assurance for accelerator monitoring and to ensure intra-spill spot deviations remain low.

Demonstrating long-term stable optical coupling between single-mode optical fibers and well-defined energy photons emitted from nitrogen impurity centers in GaAs flakes

A key challenge in realizing scalable optical quantum information technology is not only to obtain stable single photons coupled to single-mode fibers but also to match the emission energy between remote emitters. We have fabricated an energy-matching favorable and long-term stable structure by coupling a nitrogen impurity center with a well-defined emission energy to a single-mode optical fiber core. The nitrogen-doped GaAs microflakes sandwiched between the two FC (acronym for "ferrule connector" or "fiber channel") connectors yielded sharp emission peaks due to nitrogen isoelectronic traps. Although some emitters showed spectral diffusion, the unaffected emitters showed stable emission and were able to generate photons stably for over 20 h continuously without photodegradation. In addition, the photoluminescence spectrum does not change in shape and intensity after more than 3 years, indicating that the photon source with this structure is resistant to thermal cycling and positional drift and has excellent long-term stability.

SparsePoser: Real-time Full-body Motion Reconstruction from Sparse Data

Accurate and reliable human motion reconstruction is crucial for creating natural interactions of full-body avatars in Virtual Reality (VR) and entertainment applications. As the Metaverse and social applications gain popularity, users are seeking cost-effective solutions to create full-body animations that are comparable in quality to those produced by commercial motion capture systems. In order to provide affordable solutions though, it is important to minimize the number of sensors attached to the subject’s body. Unfortunately, reconstructing the full-body pose from sparse data is a heavily under-determined problem. Some studies that use IMU sensors face challenges in reconstructing the pose due to positional drift and ambiguity of the poses. In recent years, some mainstream VR systems have released 6-degree-of-freedom (6-DoF) tracking devices providing positional and rotational information. Nevertheless, most solutions for reconstructing full-body poses rely on traditional inverse kinematics (IK) solutions, which often produce non-continuous and unnatural poses. In this article, we introduce SparsePoser, a novel deep learning-based solution for reconstructing a full-body pose from a reduced set of six tracking devices. Our system incorporates a convolutional-based autoencoder that synthesizes high-quality continuous human poses by learning the human motion manifold from motion capture data. Then, we employ a learned IK component, made of multiple lightweight feed-forward neural networks, to adjust the hands and feet toward the corresponding trackers. We extensively evaluate our method on publicly available motion capture datasets and with real-time live demos. We show that our method outperforms state-of-the-art techniques using IMU sensors or 6-DoF tracking devices, and can be used for users with different body dimensions and proportions.

Development and comparison of algorithms for beam stabilization in ultrashort pulsed laser equipped on a six-axis robot

We demonstrate an innovative beam stabilization concept consisting of complementary metal-oxide semiconductor cameras and piezo actuators for a six-axis articulated ultrashort pulsed laser robot system. The beam stabilization system is fixed on robot axes 4 and 5. Moving robot axis 5 in an angular range between 0° and 90°, the laser beam position coupled to the actual robot position is monitored by two cameras integrated on robot axis 5 and used for laser beam characterization and model generation. A mathematical description and models generated with machine learning methods, namely, linear regression and neural network, are compared for predicting the beam position drift as a function of robot axis motion, where the neural network model shows a low prediction tolerance of about 7 pixels. In addition, a stand-alone time-triggered beam correction algorithm is developed and implemented on the system, which shows an excellent correction performance for large beam position drifts (below 500 pixels).

Neural Network Learning Algorithms for High-Precision Position Control and Drift Attenuation in Robotic Manipulators

In this paper, different learning methods based on Artificial Neural Networks (ANNs) are examined to replace the default speed controller for high-precision position control and drift attenuation in robotic manipulators. ANN learning methods including Levenberg–Marquardt and Bayesian Regression are implemented and compared using a UR5 robot with six degrees of freedom to improve trajectory tracking and minimize position error. Extensive simulation and experimental tests on the identification and control of the robot by means of the neural network controllers yield comparable results with respect to the classical controller, showing the feasibility of the proposed approach.

Machine learning reveals the waggle drift's role in the honey bee dance communication system.

The honey bee waggle dance is one of the most prominent examples of abstract communication among animals: successful foragers convey new resource locations to interested followers via characteristic "dance" movements in the nest, where dances advertise different locations on different overlapping subregions of the "dance floor." To this day, this spatial separation has not been described in detail, and it remains unknown how it affects the dance communication. Here, we evaluate long-term recordings of Apis mellifera foraging at natural and artificial food sites. Using machine learning, we detect and decode waggle dances, and we individually identify and track dancers and dance followers in the hive and at artificial feeders. We record more than a hundred thousand waggle phases, and thousands of dances and dance-following interactions to quantitatively describe the spatial separation of dances on the dance floor. We find that the separation of dancers increases throughout a dance and present a motion model based on a positional drift of the dancer between subsequent waggle phases that fits our observations. We show that this separation affects follower bees as well and results in them more likely following subsequent dances to similar food source locations, constituting a positive feedback loop. Our work provides evidence that the positional drift between subsequent waggle phases modulates the information that is available to dance followers, leading to an emergent optimization of the waggle dance communication system.

Long-Distance GNSS-Denied Visual Inertial Navigation for Autonomous Fixed-Wing Unmanned Air Vehicles: SO(3) Manifold Filter Based on Virtual Vision Sensor

This article proposes a visual inertial navigation algorithm intended to diminish the horizontal position drift experienced by autonomous fixed-wing UAVs (unmanned air vehicles) in the absence of GNSS (Global Navigation Satellite System) signals. In addition to accelerometers, gyroscopes, and magnetometers, the proposed navigation filter relies on the accurate incremental displacement outputs generated by a VO (visual odometry) system, denoted here as a virtual vision sensor, or VVS, which relies on images of the Earth surface taken by an onboard camera and is itself assisted by filter inertial estimations. Although not a full replacement for a GNSS receiver since its position observations are relative instead of absolute, the proposed system enables major reductions in the GNSS-denied attitude and position estimation errors. The filter is implemented in the manifold of rigid body rotations or SO(3) in order to minimize the accumulation of errors in the absence of absolute observations. Stochastic high-fidelity simulations of two representative scenarios involving the loss of GNSS signals are employed to evaluate the results. The authors release the C++ implementation of both the visual inertial navigation filter and the high-fidelity simulation as open-source software.

Design of a head-support device for a novel head-only MRI scanner

This study reports on the development of a user-adjustable head support device for a head-only MRI scanner with a seated patient. To avoid motion artifacts in head MRI, patients must move <1 ​mm during a typical 120 ​s image acquisition, often necessitating head-support devices. Previous studies have investigated user-adjustable head support for MRI when lying supine and patient-specific head support for seated MEG. A formative evaluation process was used with 22 participants in total across three phases: (1) determination of head support points whilst seated; (2) testing a simple inflatable bladder to provide head support; (3) evaluation of two fully resolved inflatable prototypes. Compared to an unsupported head, the final inflatable designs reduce positional diffusivity and drift over 120 ​s intervals by a factor of 2. The final two prototypes give acceptable user experience but require further refinement to fully minimise head movement. This research is applicable for any seated diagnostic imaging application where head motion must be minimized.

A New Temperature Correction Method for NaI(Tl) Detectors Based on Pulse Deconvolution.

To overcome the temperature effect of NaI(Tl) detectors for energy spectrometry without additional hardware, a new correction method was put forward based on pulse deconvolution, trapezoidal shaping and amplitude correction, named DTSAC. To verify this method, actual pulses from a NaI(Tl)-PMT detector were measured at various temperatures from -20 °C to 50 °C. Pulse processing and spectrum synthesis showed that the position drift of the 137Cs 662 keV peak was less than 3 keV, and the corresponding resolution at 662 keV of the sum spectra ranged from 6.91% to 10.60% with the trapezoidal width set from 1000 ns to 100 ns. The DTSAC method corrects the temperature effect via pulse processing, and needs no reference peak, reference spectrum or additional circuits. The method solves the problem of correction of pulse shape and pulse amplitude at the same time, and can be used even at a high counting rate.

A yaw correction method for pedestrian positioning using two low-cost MIMUs

In pedestrian inertial navigation system, the traditional zero-velocity update (ZUPT) method establishes a velocity constraint. Based on the Kalman filter algorithm, the velocity error is utilized to correct the position drift. However, the yaw error cannot be observed. In this study, two low-cost magnetometers/inertial measurement units (MIMUs) are tied to the foot and shank, respectively. To improve the positioning accuracy, two error correction methods are developed. Firstly, the yaw angle coordinate is chosen as a Kalman filter measurement vector. Secondly, a geometric constraint is proposed. It includes degree of freedom constraint, angle constraint, and position vector constraint. Finally, different indoor and outdoor experiments are employed to verify the effectiveness of the proposed method. The average distance error percentage of the proposed method is less than 2%, and the heading bias is less than 5° within 5.05 min. Thus, the estimated paths are more in line with the actual trajectories.