737 research outputs found

    Uncertainty-Aware Hand–Eye Calibration

    Get PDF
    We provide a generic framework for the hand–eye calibration of vision-guided industrial robots. In contrast to traditional methods, we explicitly model the uncertainty of the robot in a stochastically founded way. Albeit the repeatability of modern industrial robots is high, their absolute accuracy typically is much lower. This uncertainty—especially if not considered—deteriorates the result of the hand–eye calibration. Our proposed framework does not only result in a high accuracy of the computed hand–eye pose but also provides reliable information about the uncertainty of the robot. It further provides corrected robot poses for a convenient and inexpensive robot calibration. Our framework is computationally efficient and generic in several regards. It supports the use of a calibration target as well as self-calibration without the need for known 3-D points. It optionally enables the simultaneous calibration of the interior camera parameters. The framework is also generic with regard to the robot type and, hence, supports antropomorphic as well as selective compliance assembly robot arm (SCARA) robots, for example. Simulated and real experiments show the validity of the proposed methods. An extensive evaluation of our framework on a public dataset shows a considerably higher accuracy than 15 state-of-the-art methods

    Orientation-Aware 3D SLAM in Alternating Magnetic Field from Powerlines

    Get PDF
    Identifying new sensing modalities for indoor localization is an interest of research. This paper studies powerline-induced alternating magnetic field (AMF) that fills the indoor space for the orientation-aware three-dimensional (3D) simultaneous localization and mapping (SLAM). While an existing study has adopted a uniaxial AMF sensor for SLAM in a plane surface, the design falls short of addressing the vector field nature of AMF and is therefore susceptible to sensor orientation variations. Moreover, although the higher spatial variability of AMF in comparison with indoor geomagnetism promotes location sensing resolution, extra SLAM algorithm designs are needed to achieve robustness to trajectory deviations from the constructed map. To address the above issues, we design a new triaxial AMF sensor and a new SLAM algorithm that constructs a 3D AMF intensity map regularized and augmented by a Gaussian process. The triaxial sensor’s orientation estimation is free of the error accumulation problem faced by inertial sensing. From extensive evaluation in eight indoor environments, our AMF-based 3D SLAM achieves sub-1m to 3m median localization errors in spaces of up to 500 m2 , sub-2° mean error in orientation sensing, and outperforms the SLAM systems based on Wi-Fi, geomagnetism, and uniaxial AMF by more than 30%

    Characterisation and State Estimation of Magnetic Soft Continuum Robots

    Get PDF
    Minimally invasive surgery has become more popular as it leads to less bleeding, scarring, pain, and shorter recovery time. However, this has come with counter-intuitive devices and steep surgeon learning curves. Magnetically actuated Soft Continuum Robots (SCR) have the potential to replace these devices, providing high dexterity together with the ability to conform to complex environments and safe human interactions without the cognitive burden for the clinician. Despite considerable progress in the past decade in their development, several challenges still plague SCR hindering their full realisation. This thesis aims at improving magnetically actuated SCR by addressing some of these challenges, such as material characterisation and modelling, and sensing feedback and localisation. Material characterisation for SCR is essential for understanding their behaviour and designing effective modelling and simulation strategies. In this work, the material properties of commonly employed materials in magnetically actuated SCR, such as elastic modulus, hyper-elastic model parameters, and magnetic moment were determined. Additionally, the effect these parameters have on modelling and simulating these devices was investigated. Due to the nature of magnetic actuation, localisation is of utmost importance to ensure accurate control and delivery of functionality. As such, two localisation strategies for magnetically actuated SCR were developed, one capable of estimating the full 6 degrees of freedom (DOFs) pose without any prior pose information, and another capable of accurately tracking the full 6-DOFs in real-time with positional errors lower than 4~mm. These will contribute to the development of autonomous navigation and closed-loop control of magnetically actuated SCR

    Navigation Sensor Stochastic Error Modeling and Nonlinear Estimation for Low-Cost Land Vehicle Navigation

    Get PDF
    The increasing use of low-cost inertial sensors in various mass-market applications necessitates their accurate stochastic modeling. Such task faces challenges due to outliers in the sensor measurements caused by internal and/or external factors. To optimize the navigation performance, robust estimation techniques are required to reduce the influence of outliers to the stochastic modeling process. The Generalized Method of Wavelet Moments (GMWM) and its Multi-signal extensions (MS-GMWM) represent the latest trend in the field of inertial sensor error stochastic analysis, they are capable of efficiently modeling the highly complex random errors displayed by low-cost and consumer-grade inertial sensors and provide very advantageous guarantees for the statistical properties of their estimation products. On the other hand, even though a robust version exists (RGMWM) for the single-signal method in order to protect the estimation process from the influence of outliers, their detection remains a challenging task, while such attribute has not yet been bestowed in the multi-signal approach. Moreover, the current implementation of the GMWM algorithm can be computationally intensive and does not provide the simplest (composite) model. In this work, a simplified implementation of the GMWM-based algorithm is presented along with techniques to reduce the complexity of the derived stochastic model under certain conditions. Also, it is shown via simulations that using the RGMWM every time, without the need for contamination existence confirmation, is a worthwhile trade-off between reducing the outlier effects and decreasing the estimator efficiency. Generally, stochastic modeling techniques, including the GMWM, make use of individual static signals for inference. However, it has been observed that when multiple static signal replicates are collected under the same conditions, they maintain the same model structure but exhibit variations in parameter values, a fact that called for the MS-GMWM. Here, a robust multi-signal method is introduced, based on the established GMWM framework and the Average Wavelet Variance (AWV) estimator, which encompasses two robustness levels: one for protection against outliers in each considered replicate and one to safeguard the estimation against the collection of signal replicates with significantly different behaviour than the majority. From that, two estimators are formulated, the Singly Robust AWV (SR-AWV) and the Doubly Robust (DR-AWV) and their model parameter estimation efficiency is confirmed under different data contamination scenarios in simulation and case studies. Furthermore, a hybrid case study is conducted that establishes a connection between model parameter estimation quality and implied navigation performance in those data contamination settings. Finally, the performance of the new technique is compared to the conventional Allan Variance in a land vehicle navigation experiment, where the inertial information is fused with an auxiliary source and vehicle movement constraints using the Extended and Unscented Kalman Filters (EKF/UKF). Notably, the results indicate that under linear-static conditions, the UKF with the new method provides a 16.8-17.3% improvement in 3D orientation compared to the conventional setting (AV with EKF), while the EKF gives a 7.5-9.7% improvement. Also, in dynamic conditions (i.e., turns), the UKF demonstrates an 14.7-17.8% improvement in horizontal positioning and an 11.9-12.5% in terms of 3D orientation, while the EKF has an 8.3-12.8% and an 11.4-11.7% improvement respectively. Overall, the UKF appears to perform better but has a significantly higher computational load compared to the EKF. Hence, the EKF appears to be a more realistic option for real-time applications such as autonomous vehicle navigation

    A Power Method for Computing the Dominant Eigenvalue of a Dual Quaternion Hermitian Matrix

    Full text link
    In this paper, we first study the projections onto the set of unit dual quaternions, and the set of dual quaternion vectors with unit norms. Then we propose a power method for computing the dominant eigenvalue of a dual quaternion Hermitian matrix, and show its convergence and convergence rate under mild conditions. Based upon these, we reformulate the simultaneous localization and mapping (SLAM) problem as a rank-one dual quaternion completion problem. A two-block coordinate descent method is proposed to solve this problem. One block subproblem can be reduced to compute the best rank-one approximation of a dual quaternion Hermitian matrix, which can be computed by the power method. The other block has a closed-form solution. Numerical experiments are presented to show the efficiency of our proposed power method.Comment: 32 pages, 3 figure

    Insect neuroethology of reinforcement learning

    Get PDF
    Historically, reinforcement learning is a branch of machine learning founded on observations of how animals learn. This involved collaboration between the fields of biology and artificial intelligence that was beneficial to both fields, creating smarter artificial agents and improving the understanding of how biological systems function. The evolution of reinforcement learning during the past few years was rapid but substantially diverged from providing insights into how biological systems work, opening a gap between reinforcement learning and biology. In an attempt to close this gap, this thesis studied the insect neuroethology of reinforcement learning, that is, the neural circuits that underlie reinforcement-learning-related behaviours in insects. The goal was to extract a biologically plausible plasticity function from insect-neuronal data, use this to explain biological findings and compare it to more standard reinforcement learning models. Consequently, a novel dopaminergic plasticity rule was developed to approximate the function of dopamine as the plasticity mechanism between neurons in the insect brain. This allowed a range of observed learning phenomena to happen in parallel, like memory depression, potentiation, recovery, and saturation. In addition, by using anatomical data of connections between neurons in the mushroom body neuropils of the insect brain, the neural incentive circuit of dopaminergic and output neurons was also explored. This, together with the dopaminergic plasticity rule, allowed for dynamic collaboration amongst parallel memory functions, such as acquisition, transfer, and forgetting. When tested on olfactory conditioning paradigms, the model reproduced the observed changes in the activity of the identified neurons in fruit flies. It also replicated the observed behaviour of the animals and it allowed for flexible behavioural control. Inspired by the visual navigation system of desert ants, the model was further challenged in the visual place recognition task. Although a relatively simple encoding of the olfactory information was sufficient to explain odour learning, a more sophisticated encoding of the visual input was required to increase the separability among the visual inputs and enable visual place recognition. Signal whitening and sparse combinatorial encoding were sufficient to boost the performance of the system in this task. The incentive circuit enabled the encoding of increasing familiarity along a known route, which dropped proportionally to the distance of the animal from that route. Finally, the proposed model was challenged in delayed reinforcement tasks, suggesting that it might take the role of an adaptive critic in the context of reinforcement learning

    Visual Guidance for Unmanned Aerial Vehicles with Deep Learning

    Full text link
    Unmanned Aerial Vehicles (UAVs) have been widely applied in the military and civilian domains. In recent years, the operation mode of UAVs is evolving from teleoperation to autonomous flight. In order to fulfill the goal of autonomous flight, a reliable guidance system is essential. Since the combination of Global Positioning System (GPS) and Inertial Navigation System (INS) systems cannot sustain autonomous flight in some situations where GPS can be degraded or unavailable, using computer vision as a primary method for UAV guidance has been widely explored. Moreover, GPS does not provide any information to the robot on the presence of obstacles. Stereo cameras have complex architecture and need a minimum baseline to generate disparity map. By contrast, monocular cameras are simple and require less hardware resources. Benefiting from state-of-the-art Deep Learning (DL) techniques, especially Convolutional Neural Networks (CNNs), a monocular camera is sufficient to extrapolate mid-level visual representations such as depth maps and optical flow (OF) maps from the environment. Therefore, the objective of this thesis is to develop a real-time visual guidance method for UAVs in cluttered environments using a monocular camera and DL. The three major tasks performed in this thesis are investigating the development of DL techniques and monocular depth estimation (MDE), developing real-time CNNs for MDE, and developing visual guidance methods on the basis of the developed MDE system. A comprehensive survey is conducted, which covers Structure from Motion (SfM)-based methods, traditional handcrafted feature-based methods, and state-of-the-art DL-based methods. More importantly, it also investigates the application of MDE in robotics. Based on the survey, two CNNs for MDE are developed. In addition to promising accuracy performance, these two CNNs run at high frame rates (126 fps and 90 fps respectively), on a single modest power Graphical Processing Unit (GPU). As regards the third task, the visual guidance for UAVs is first developed on top of the designed MDE networks. To improve the robustness of UAV guidance, OF maps are integrated into the developed visual guidance method. A cross-attention module is applied to fuse the features learned from the depth maps and OF maps. The fused features are then passed through a deep reinforcement learning (DRL) network to generate the policy for guiding the flight of UAV. Additionally, a simulation framework is developed which integrates AirSim, Unreal Engine and PyTorch. The effectiveness of the developed visual guidance method is validated through extensive experiments in the simulation framework

    Deep Learning for Head Pose Estimation: A Survey

    Get PDF
    Head pose estimation (HPE) is an active and popular area of research. Over the years, many approaches have constantly been developed, leading to a progressive improvement in accuracy; nevertheless, head pose estimation remains an open research topic, especially in unconstrained environments. In this paper, we will review the increasing amount of available datasets and the modern methodologies used to estimate orientation, with a special attention to deep learning techniques. We will discuss the evolution of the feld by proposing a classifcation of head pose estimation methods, explaining their advantages and disadvantages, and highlighting the diferent ways deep learning techniques have been used in the context of HPE. An in-depth performance comparison and discussion is presented at the end of the work. We also highlight the most promising research directions for future investigations on the topic

    Synergy-Based Human Grasp Representations and Semi-Autonomous Control of Prosthetic Hands

    Get PDF
    Das sichere und stabile Greifen mit humanoiden Roboterhänden stellt eine große Herausforderung dar. Diese Dissertation befasst sich daher mit der Ableitung von Greifstrategien für Roboterhände aus der Beobachtung menschlichen Greifens. Dabei liegt der Fokus auf der Betrachtung des gesamten Greifvorgangs. Dieser umfasst zum einen die Hand- und Fingertrajektorien während des Greifprozesses und zum anderen die Kontaktpunkte sowie den Kraftverlauf zwischen Hand und Objekt vom ersten Kontakt bis zum statisch stabilen Griff. Es werden nichtlineare posturale Synergien und Kraftsynergien menschlicher Griffe vorgestellt, die die Generierung menschenähnlicher Griffposen und Griffkräfte erlauben. Weiterhin werden Synergieprimitive als adaptierbare Repräsentation menschlicher Greifbewegungen entwickelt. Die beschriebenen, vom Menschen gelernten Greifstrategien werden für die Steuerung robotischer Prothesenhände angewendet. Im Rahmen einer semi-autonomen Steuerung werden menschenähnliche Greifbewegungen situationsgerecht vorgeschlagen und vom Nutzenden der Prothese überwacht

    Proceedings XXIII Congresso SIAMOC 2023

    Get PDF
    Il congresso annuale della Società Italiana di Analisi del Movimento in Clinica (SIAMOC), giunto quest’anno alla sua ventitreesima edizione, approda nuovamente a Roma. Il congresso SIAMOC, come ogni anno, è l’occasione per tutti i professionisti che operano nell’ambito dell’analisi del movimento di incontrarsi, presentare i risultati delle proprie ricerche e rimanere aggiornati sulle più recenti innovazioni riguardanti le procedure e le tecnologie per l’analisi del movimento nella pratica clinica. Il congresso SIAMOC 2023 di Roma si propone l’obiettivo di fornire ulteriore impulso ad una già eccellente attività di ricerca italiana nel settore dell’analisi del movimento e di conferirle ulteriore respiro ed impatto internazionale. Oltre ai qualificanti temi tradizionali che riguardano la ricerca di base e applicata in ambito clinico e sportivo, il congresso SIAMOC 2023 intende approfondire ulteriori tematiche di particolare interesse scientifico e di impatto sulla società. Tra questi temi anche quello dell’inserimento lavorativo di persone affette da disabilità anche grazie alla diffusione esponenziale in ambito clinico-occupazionale delle tecnologie robotiche collaborative e quello della protesica innovativa a supporto delle persone con amputazione. Verrà infine affrontato il tema dei nuovi algoritmi di intelligenza artificiale per l’ottimizzazione della classificazione in tempo reale dei pattern motori nei vari campi di applicazione
    • …
    corecore