Search

Simultaneous Localization and Mapping (SLAM) is the foundation of autonomous navigation for unmanned systems. The existing SLAM solutions are mainly divided into the visual SLAM(vSLAM) equipped with camera and the lidar SLAM equipped with lidar. However, pure visual SLAM have shortcomings such as low positioning accuracy, the paper proposes a visual-inertial information fusion SLAM based on Runge-Kutta improved pre-integration. First, the Inertial Measurement Unit (IMU) information between two adjacent keyframes is pre-integrated at the front-end to provide IMU constraints for visual-inertial information fusion. In particular, to improve the accuracy in pre-integration, the paper uses the Runge-Kutta algorithm instead of Euler integral to calculate the pre-integration value at the next moment. Then, the IMU pre-integration value is used as the initial value of the system state at the current frame time. We combine the visual reprojection error and imu pre-integration error to optimize the state variables such as speed and pose, and restore map points' three-dimensional coordinates. Finally, we set a sliding window to optimize map points' coordinates and state variables. The experimental part is divided into dataset experiment and complex indoor-environment experiment. The results show that compared with pure visual SLAM and the existing visual-inertial fusion SLAM, our method has higher positioning accuracy.

Laboratory experiments were performed to study the impact of the relative particle protrusion P/D (P is the protrusion height and D is the diameter of the target particle) on the mechanism of entra...

Localisation is an important part of many applications. Our motivating scenarios are short-term construction work and emergency rescue. These scenarios also require rapid setup and robustness to environmental conditions additional to localisation accuracy. These requirements preclude the use of many traditional high-performance methods, e.g. vision-based, laser-based, Ultra-wide band (UWB) and Global Positioning System (GPS)-based localisation systems. To overcome these challenges, we introduce iMag+, an accurate and rapidly deployable inertial magneto-inductive (MI) mapping and localisation system, which only requires monitored workers to carry a single MI transmitter and an inertial measurement unit in order to localise themselves with minimal setup effort. However, one major challenge is to use distorted and ambiguous MI location estimates for localisation. To solve this challenge, we propose a novel method to use MI devices for sensing environmental distortions for accurate closing inertial loops. We also suggest a robust and efficient first quadrant estimator to sanitise the ambiguous MI estimates. By applying robust simultaneous localisation and mapping (SLAM), our proposed localisation method achieves excellent tracking accuracy and can improve performance significantly compared with only using a Magneto-inductive device or inertial measurement unit (IMU) for localisation.

Robot force-controlled task executions require an accurate perception of the contact force/torque. When a six-axis force/torque (F/T) sensor is attached to the robot wrist, the compensation precision is affected by many non-contact features, such as the end-effector gravity, inertial, centrifugal, Coriolis forces, and the mechanical errors of the sensor. However, it is complicated to identify all parameters at the same time. Some features will be ignored in practice applications, e.g., surface finishing, human-robot interaction, and haptic applications. In this paper, a novel end-effector gravity compensation method for the F/T sensor is proposed, which identifies the end-effector gravity, its center, and the biases (the rotation between the sensor and robot, the robot installation declination, the bias of sensor). Our key is the rotation calibration while the transformation between the F/T sensor and robot is unknown. Simulation and experimental results show that the proposed method works effectively. In a wrist-mounted F/T sensor system, compared to the previous limited method, the compensation error in common force control channel Fz by our method diminishes 1.0% of the end-effector gravity (i.e., 21.7% relative improvement).

For the considered reaction-diffusion inertial memristive neural networks (IMNNs), this article proposes a novel gain-scheduled generalized pinning control scheme, where three pinning control strategies are involved and 2ⁿ controller gains can be scheduled for different system parameters. Moreover, a time delay is considered in the controller to make it has a memory function. With the designed controller, drive-and-response systems can be synchronized within a finite-time interval. Note that the final finite-time synchronization criterion is obtained in the forms of linear matrix inequalities (LMIs) by introducing a memristor-dependent sign function into the controller and constructing a new Lyapunov-Krasovskii functional (LKF). Furthermore, by utilizing some improved integral inequality methods, the conservatism of the main results can be greatly reduced. Finally, three numerical examples are provided to illustrate the feasibility, superiority, and practicability of this article.

The problem of calibrating extrinsic parameter between camera and inertial measurement unit (IMU) using an industrial robotic manipulator has been studied. This generates a result of hand-eye/robot-world/camera-IMU calibration in a simultaneous fashion. The developed method is free of inertial integration over time and thus is robust to uncertain IMU biases. It is derived that the problem can be solved via a simultaneous optimization of hand-eye/robot-world/camera-IMU transformations. The resulted optimization is highly non-convex on the special Euclidean group and we give globally optimal solutions. Experiments verify that the proposed method is capable of estimating accurate calibration parameters. Comparative studies between representatives show the global optimality of the proposed method. The new simultaneous method is capable of conducting calibration of a robot/camera/IMU combination. The designed method guarantees the global optimality thus the accuracy is ensured. The developed globally optimal solutions will also be computationally efcient on modern industrial computers. Finally, we show that the proposed method can give accurate calibration results for a stereo/IMU sensor combination.

This paper presents a non-smooth multibody dynamic formulation and error analysis of an antenna pointing mechanism including frictional spatial revolute joints (FSRJs) with small clearance in the framework of the special Euclidian group SE(3). The formulation leads to an inertial frame-invariant, a compact and unified description for rigid bodies and spatial revolute joints (SRJs). The geometric constraint of the bearing is covered by four open semi-cylinders, which can be treated as bilateral constraints assuming that the impact effects are negligible. The frictional contact problem is formulated as a horizontal linear complementary problem (HLCP), which is embedded in the Lie-group integration scheme. Error of the antenna pointing mechanism is modeled by means of the adjoint transformation and POE-based formula. The evolution of errors is obtained through the solution of non-smooth dynamics. The obtained numerical results illustrate the influences of FSRJs in dynamics modeling and error analysis of the antenna pointing mechanism.

The inertial navigation system (INS) has been widely used to provide self-contained and continuous motion estimation in intelligent transportation systems. Recently, the emergence of chip-level inertial sensors has expanded the relevant applications from positioning, navigation, and mobile mapping to location-based services, unmanned systems, and transportation big data. Meanwhile, benefit from the emergence of big data and the improvement of algorithms and computing power, machine learning (ML) has become a consensus tool that has been successfully applied in various fields. This article reviews the research on using ML technology to enhance inertial sensing from various aspects, including sensor design and selection, calibration and error modeling, navigation and motion-sensing algorithms, multi-sensor information fusion, system evaluation, and practical application. It summarizes the state of the art, advantages, and challenges on each aspect, and points out future research directions.

Terzaghi's consolidation theory neglects inertial effects on the consolidation of saturated soils. To quantify the inertial effects, in this paper an original one-dimensional small-strain consolidation wave (C-wave) theory is developed, based upon a proposed modified Darcy's law with relaxation time and the equation of motion for soil ensemble. The one-dimensional governing equations were first formulated for self-weight consolidation, followed by a closed-form solution employing the method of separation of variables. The proposed model was then validated against wave velocity measurements and verified against finite-difference analysis. The half-closed self-weight consolidation behaviour was subsequently investigated, compared with Terzaghi's theory, Fillunger–Heinrich's dynamic theory and the u–p form of Biot's wave theory. This research indicates that: (a) superior to conventional models under comparison, the C-wave model enhances the predictability of the C-wave velocity; (b) the dimensionless C-wave coefficient (Cw) dominates the fundamental consolidation behaviour; (c) a wave-diffusion duality underlying the consolidation mechanism contributes qualitatively to the spatial bottom-up pattern and temporal response delay in consolidation observations; and (d) Terzaghi's theory can afford a practically accurate solution provided the Cw and time factor are below and above approximately 0·01, respectively. The C-wave theory may enrich the understanding of consolidation-related phenomena involving an appreciable Cw.

This article explores the exponential stabilization issue of a class of state-based switched inertial complex-valued neural networks with multiple delays via event-triggered control. First, the state-based switched inertial complex-valued neural networks with multiple delays are modeled. Second, by separating the real and imaginary parts of complex values, the state-based switched inertial complex-valued neural networks are transformed into two state-based switched inertial real-valued neural networks. Through the variable substitution method, the model of the second-order inertial neural networks is transformed into a model of the first-order neural networks. Third, an event-triggered controller with the transmission sequence is designed to study the exponential stabilization issue of neural networks constructed above. Then, by constructing the Lyapunov functions and based on some inequalities, we obtain sufficient conditions for exponential stabilization of the proposed neural networks. Furthermore, it is proved that the Zeno phenomenon cannot happen under the designed event-triggered controller. Finally, a simulation example is given to illustrate the correctness of the results.

It is difficult to obtain linear output displacements for stepping piezoelectric platform due to their discontinuous outputs. Inspired by the theory that the maximum static friction force is greater than the sliding friction force, this letter proposed a bipedal alternating inertial actuation mode (BAIAM) to improve the displacement linearity of stepping platform. The output displacements are measured, the linear fitting is carried out by the least square method, the correlation coefficient and linear fitting function are used to evaluate the displacement linearity, the correlation coefficients of the forward and backward motions are calculated as 0.9998 and 0.9999 in BAIAM, respectively, which verifies the effectiveness of the proposed method. Closed-loop experiments in BAIAM are also carried out, the steady state errors for target position of 40 m within 0.2 m in the point-to-point positioning control experiments are achieved. The experiment results confirm that the developed platform can be applied in precision fields requiring large travel range, planar two-DOF motion and linear outputs.

Wearable inertial motion capture, a new type of motion capture technology, mainly estimates the human posture in 3-D space through multi-sensor data fusion. The available method for sensor fusion are usually aided by magnetometers to remove the drift error in yaw angle estimation, which in turn limits their application in the presence of complex magnetic field environment. In this paper, an extended Kalman filter data fusion method is proposed to fuse the 9-axis sensor data. Meanwhile, heuristic drift reduction (HDR) method is used to calibrate the accumulated error of heading angle. In addition, the position in 3-D space is estimated by foot-mounted Zero-Velocity-Update (ZUPT) technique. Combining 3-D attitude and position, a biomechanical model of the human body is established to track the motion of real human body. The extended Kalman filter (EKF) algorithm and position estimation methods are benchmarked against the golden standard, optical motion capture system, for various indoor experiments. In addition, various outdoor experiments are also conducted to verify the reliability of the proposed method. The results show that the proposed algorithm outperforms the available attitude estimation model in motion tracking and is feasible for 3-D human motion capture.

The force-generation mechanism of a dovelike flapping-wing micro air vehicle was studied by numerical simulation and experiment. To obtain the real deformation pattern of the flapping wing, the digital image correlation technology was used to measure the dynamic deformation of the wing. The dynamic deformation data were subsequently interpolated and embedded into the CFD solver to account for the aeroelastic effects. The dynamic deformation data were further used to calculate the inertial forces by regarding the wing as a system of particles to take into account the wing flexibility. The temporal variation of the forces produced by the flapping wing was measured by a miniature load cell. The numerical results provide more flow details of the unsteady aerodynamics of the flapping wing in terms of vortex formation and evolution. The calculated results of the inertial forces are analyzed and compared with the CFD results which represent the aerodynamic forces. In addition, the total forces, i.e., the sum of the CFD result and inertial result, are compared with the experimental results, and an overall good agreement is obtained.

Foot-mounted inertial navigation with zero velocity update (ZUPT) is commonly used in pedestrian positioning.To suppress the accumulating position error owing to the low-cost inertial sensor,classical dual-foot pedestrian navigation method provides the compensation using the two feet's relative step length,whose values can be set an upper bound constraint or measured by ranging sensors.However,it suffers from low improvement and poor wearability.In this study,the two sensors are distributed on the toe and ankle of the same leg.The sensor-to-sensor position is unchangeable with the lower limb segments.This makes the uncoupled two subsystems be related.Through gait analysis,the step is divided into the stance and swing phases.For each phase,positions,velocities,and the related characteristics of the toe and the ankle are analyzed and modeled,respectively.A Kalman filter is constructed to fuse the position and velocity observations according to the geometric constraints and the foot vector.The Mahony filter by fusing the inertial sensor and magnetometer is adopted for the attitude improvement.Several experiments at low and high walking velocities on the plane and upstairs are carried out to verify the proposed method.The results show that compared with the conventional single-IMU ZUPT and dual-IMU two-foot constraint methods,the position and heading errors have been well suppressed.

This paper proposes to use a newly-derived transformed inertial navigation system (INS) mechanization to fuse INS with other complementary navigation systems. Through formulating the attitude, velocity and position as one group state of group of double direct spatial isometries SE2(3), the transformed INS mechanization has proven to be group affine, which means that the corresponding vector error state model will be trajectory-independent. In order to make use of the transformed INS mechanization in inertial based integration, both the right and left vector error state models are derived. The INS/GPS and INS/Odometer integration are investigated as two representatives of inertial based integration. Some application aspects of the derived error state models in the two applications are presented, which include how to select the error state model, initialization of the SE2(3) based error state covariance and feedback correction corresponding to the error state definitions. Extensive Monte Carlo simulations and land vehicle experiments are conducted to evaluate the performance of the derived error state models. It is shown that the most striking superiority of using the derived error state models is their ability to handle the large initial attitude misalignments, which is just the result of log-linearity property of the derived error state models. Therefore, the derived error state models can be used in the so-called attitude alignment for the two applications. Moreover, the derived right error state-space model is also very preferred for long-endurance INS/Odometer integration due to the filtering consistency caused by its less dependence on the global state estimate.

BACKGROUND: Optoelectronic systems and force platforms represent the gold standard for postural sway assessment, but pose disadvantages in terms of equipment, cost and preparation time. OBJECTIVE: Wearable inertial measurement units (IMUs) have been proposed to overcome these issues, but have never been compared to an optoelectronic system. The study aim was therefore to investigate agreement between inertial measurement unit and optoelectronic system in postural sway assessment. METHODS: Thirty healthy volunteers performed four balance tasks. IMU was placed on the sacrum (S2) with a retroreflective marker over the sensor and subjects’ performance was simultaneously recorded by both systems. Total (TOT), anterior-posterior (AP) and medial-lateral (ML) length of trace, range, speed, root mean squared (RMS), and confidence ellipse were computed. RESULTS: ICCs revealed excellent correlations for Length-TOT, Length-AP and Speed-AP, good correlation for Length-ML, Speed-ML, Confidence Ellipse, Range-AP and RMS-AP, and moderate correlation for range-ML and RMS-ML. Bland-Altman plot showed greater estimation for Length-TOT, Length-AP, Speed-AP, confidence ellipse and RMS-AP using optoelectronic system, and for Length-ML, Range-AP, Range-ML, Speed-ML, RMS-ML using IMU. Both systems revealed the same differences among tasks. CONCLUSION: The excellent to good agreement of IMU for length of trace and speed parameters and its user-friendly application suggest its potential implementations in clinical practice.

The H∞ state estimation issue for switched delayed inertial neural networks is addressed in this work. A more universal switching law, persistent dwell-time (DT) switching law, is considered here rather than average DT one of which switching frequency among subsystems is strictly limited, or DT one. Concurrently, time delays are inevitable when transmitting information, then taking the time-varying delays into account makes the constructed systems conform well with the actual situations. The main goal in the work is devoted to designing a state estimator to ensure that the state estimation error system is globally uniformly exponentially stable and satisfies a prescribed H∞ noise attenuation level. A mixed time/mode-dependent Lyapunov-Krasovskii functional matched with the foregoing switching law is introduced. Through utilizing some reasonable inequalities and common matrix operations, some sufficient criteria which guarantee the aforesaid stability and the solvability of the addressed issue are presented. Finally, an illustrative example is provided to present the potentiality and validity of the developed results.

Inertial Navigation System/Celestial Navigation System (INS/CNS) integration, especially for the tightly-coupled mode, provides a promising autonomous tactics for Hypersonic Vehicle (HV) in military demands. However, INS/CNS integration is a challenging research task due to its special characteristics such as strong nonlinearity, non-additive noise and dynamic complexity. This paper presents a novel nonlinear filtering method for INS/CNS integration by adopting the emerging Cubature Kalman Filter (CKF) to handle the strong INS error model nonlinearity caused by HV’s high dynamics. It combines the state-augmentation technique into the nonlinear CKF to decrease the negative effect of non-additive noise in inertial measurements. Subsequently, a technique for the detection of dynamic model uncertainty is developed, and the augmented CKF is modified with fading memory to tackle dynamic model uncertainty by rigorously deriving the fading factor via the theory of Mahalanobis distance without artificial empiricism. Simulation results and comparison analysis prove that the proposed method can effectively curb the adverse impacts of non-additive noise and dynamic model uncertainty for inertial measurements, leading to improved performance for HV navigation with tightly-coupled INS/CNS integration.

In this paper, the finite-time synchronization of drive-response fuzzy inertial neural networks with delays are considered. Without applying the finite-time stability theorems and integral inequality approach, by using the maximum-value approach of functions and designing two kinds of different controllers of time variable t, two criteria ensuring the finite-time synchronization for the drive-response fuzzy inertial neural networks are put forward. In our paper, since the maximum-value approach of functions is used and the controllers of time variable $ t$ are designed in studying the finite-time synchronization, our approach and results on the finite-time synchronization of neural networks are new.

This article tackles the global exponential stability for a class of delayed complex-valued inertial neural networks in a discrete-time form. It is assumed that the activation function can be separated explicitly into the real part and imaginary part. Two methods are employed to deal with the stability issue. One is based on the reduced-order method. Two exponential stability criteria are obtained for the equivalent reduced-order network with the generalized matrix-measure concept. The other is directly based on the original second-order system. The main theoretical results complement each other. Some comparisons with the existing works show that the results in this article are less conservative. Two numerical examples are given to illustrate the validity of the main results.

This article focuses on the solution to the coordinated formation problem of heterogeneous vertical takeoff and landing (VTOL) unmanned aerial vehicles (UAVs) in the presence of parametric uncertainties. In particular, their inertial parameters are distinct and unavailable. For the sake of the accomplishment of the coordinated formation objective of multiple underactuated VTOL UAVs through local information exchange, an adaptive distributed control algorithm is developed under a cascaded structure. Specifically, by introducing an immersion and invariance (I&I) adaption strategy for the exponential mass estimation, a distributed command force is first synthesized in the position loop. Next, an applied torque with adaption is synthesized for the attitude tracking to a command attitude. This command attitude, as well as the applied thrust, is extracted from the synthesized command force without singularity. It is shown in terms of the Lyapunov theory that driven by the proposed adaptive distributed control algorithm, the concerned coordinated formation control of multiple VTOL UAVs is achieved asymptotically. Finally, an illustrative example is simulated to validate the effectiveness of the proposed control algorithm.

This paper deals with the simultaneous estimation of the attitude, position and linear velocity for vision-aided inertial navigation systems. We propose a nonlinear observer on $SO(3)\times \mathbb{R}^{15}$ relying on body-frame acceleration, angular velocity and (stereo or monocular) bearing measurements of some landmarks that are constant and known in the inertial frame. Unlike the existing local Kalman-type observers, our proposed nonlinear observer guarantees almost global asymptotic stability and local exponential stability. A detailed uniform observability analysis has been conducted and sufficient conditions are derived. Moreover, a hybrid version of the proposed observer is provided to handle the intermittent nature of the measurements in practical applications. Simulation and experimental results are provided to illustrate the effectiveness of the proposed state observer., Accepted for publication in IEEE Transaction on Automatic Control. 16 pages, 6 figures

Time-equispaced inertial measurements are practically used as inputs for motion determination. Polynomial interpolation is a common technique of recovering the gyroscope signal but is subject to a fundamentally numerical stability problem due to the Runge effect on equispaced samples. This paper reviews the theoretical results of Runge phenomenon in related areas and proposes a straightforward borrowing-and-cutting (BAC) strategy to depress it. It employs the neighboring samples for higher-order polynomial interpolation but only uses the middle polynomial segment in the actual time interval. The BAC strategy has been incorporated into attitude computation by functional iteration, leading to accuracy benefit of several orders of magnitude under the classical coning motion. It would potentially bring significant benefits to the inertial navigation computation under sustained dynamic motions., 9 pages, 8 figures

Insertion into delicate tissue, such as retina vein occlusion, cell manipulation and vitreoretinal injection, poses challenges to surgeons and biomedical researchers due to the high resolution, large movement range, compact structure and multi-DOF output requirements. In order to meet these requirements, a 2-DOF arched needle insertion device driven by inertial piezoelectric actuator was proposed in this work. This device could perform theoretical unlimited translational movement and full rotation with high step resolution. A prototype was fabricated with compact structure, the length and diameter were 230 mm and 32 mm, respectively. The experimental tests were carried out, the insertion device output 2.47 m and 0.56 mrad step distance with maximum velocity of 382 m/s and 200.9 mrad/s under 120 V driving signals, the maximum insertion force of 32 mN was measured. Additionally, a handheld controller with friendly user interface was also developed for open-loop control utilization, which could automatically calculate and generate the required control signals according to the motion command. This device explores the biomedical application of inertial piezoelectric actuator within a compact size, providing a new choice for numerous innovative treatment and biological research involving precision operation.

A piezoelectric stick slip actuator with the inertial block structure is presented in an attempt to achieve smooth motion. Under asymmetric excitation, the inertial block produces different inertia, which can dynamically change the normal pressure between the stator and rotor to adjust the friction. During the slip stage, the driving arm produces a deflection away from the rotor, the normal pressure between the stator and rotor can be eliminated, and the angular impulse of the moment of friction force on the rotor can be almost reduced to zero. To verify the feasibility of the prototype, finite element method (FEM) simulation is performed. On the basis of the experimental system, the output characteristics of the prototype are systematically studied. The experimental results indicate that the prototype can achieve smooth motion under large pre-tightening displacement. The displacement curve under the load of 2.0 kg still shows good smoothness, and the resolution is 1.56 rad. This work proposes a new design concept of dynamic control of the normal pressure to achieve smooth motion for piezoelectric stick slip actuators, even under heavy-load conditions, using the inertial block structure.

The fast-inertial-relaxation engine (FIRE) has proven to efficiently find local minima of potential energies or related penalty functions although its implementation requires only few, additional lines of code in a molecular-dynamics or steepest-descent program. So far, FIRE has been predominantly applied to particle-based or low-dimensional problems. In this work, we demonstrate that it can also benefit the solution of boundary-value problems. Towards this end, we study the mechanical contact between an elastic body and a rigid indenter of varying complexity by augmenting Green's function molecular dynamics (GFMD) with FIRE. We find a rather remarkable speedup, which can be further enhanced when choosing the masses associated with the eigenmodes of the free elastic solid appropriately. For the investigated adhesive and randomly rough indenter with typical system size, 100 mass-weighted FIRE-GFMD iterations suffice to relax the excess energy to ${10}^{\ensuremath{-}3}$ of its original value. The standard GFMD method needs 25 times more iterations. For the investigated problems, FIRE-GFMD even appears to slightly outperform conjugate-gradient based optimization.

In this article a global method for master-slave synchronization of nonlinear systems is provided. Based on a diffeomorphic transformation of the original dynamics, a reduced observer is designed such that the nonlinear state feedback law is independent of the inertial frame. Generally, the method requires non trivial computations but leads to global convergence results for the invariant tracking error. We illustrate the design procedure in detail for a master-slave synchronization of a bacterial growth model, which may escape to infinity in finite time.

Resumo O Princípio da Relatividade de Galileu (PRG) estabelece que as leis da mecânica são as mesmas em qualquer referencial inercial. Ao determinar que todos os referenciais inerciais são equivalentes, esse princípio implica em uma relatividade do movimento. Por outro lado, mesmo considerando o PRG como basilar para a mecânica, Isaac Newton introduz os conceitos de tempo e espaço absolutos e apresenta argumentos, de natureza dinâmica, em defesa do movimento absoluto, justificado na diferenciação entre referenciais inerciais e não-inerciais. A Teoria da Relatividade Restrita (TRR) critica as noções de tempo e espaço absolutos, mas preserva o papel privilegiado dos referenciais inerciais na descrição das leis da natureza. Apenas com a Teoria da Relatividade Geral (TRG) é estabelecida a equivalência entre todos os referenciais, independentemente do seu estado de movimento e, assim, é derrubada a noção de movimento absoluto. Este trabalho tem por objetivo discutir as principais dúvidas e questões que surgem ao se estudar os conceitos supramencionados, bem como investigar a aparente dubiedade entre o PRG e o movimento absoluto. Abstract Galileo’s Principle of Relativity (GPR) states that the laws of mechanics are the same in any inertial frame of reference. By determining that all inertial reference frames are equivalent, this principle implies a relativity of motion. On the other hand, even considering the GPR as fundamental for mechanics, Isaac Newton introduces the concepts of absolute time and space and presents arguments, of a dynamic nature, in defense of absolute motion, justified by the differentiation between inertial and non-inertial reference frames. The Special Relativity Theory (SRT) criticizes the notions of absolute time and space, but preserves the privileged role of inertial reference frames in the description of the laws of nature. Only with the General Relativity Theory (GRT) is equivalence established between all references, regardless of their state of motion and, thus, the notion of absolute motion is overthrown. This work aims to discuss the main doubts and issues that arise when studying the aforementioned concepts, as well as to investigate the apparent dubiousness between the PRG and the absolutemovement.

The introduction of data-driven inertial navigation provides new opportunities that the pedestrian dead reckoning could not well provide for constraining inertial system error drift on smartphones, and has been considered as another promising approach to meet the requirement of location-based services. However, indoor localization systems based on a single technology still have their limitations, such as the drift of inertial navigation and the received signal strength fluctuation of Bluetooth, making them unable to provide reliable positioning. To exploit the complementary strengths of each technology, this paper proposes a feasible fusion framework by utilizing a particle filter to integrate data-driven inertial navigation with localization based on Bluetooth Low Energy (BLE). For data-driven inertial navigation, under the premise of using the deep neural network with great potential in model-free generalization to regress pedestrian motion characteristics, we effectively combined the method of using gravity to stabilize inertial measurement units data to make the network more robust. Experimental results show that in the test of different smartphone usages, the proposed data-driven inertial navigation and BLE-based localization technology have good results in modeling user’s movement and positioning respectively. And due to this, the proposed fusion algorithm has almost unaffected by the usages of smartphones. Compared with BLE-based localization that achieved a good mean positional error (MPE) of 1.76m, for the four usages of texting, swinging, calling and pocket, the proposed fusion algorithm reduced the MPE by 32.35%, 20.51%, 20.74%, and 45.37%, respectively, and can further improve localization accuracy on the basis of existing fusion method.

Accurate indoor positioning is critical in the field of location-based services and robotics. Visible light positioning (VLP) technology is a promising technique as it can provide high accuracy positioning based on the existing lighting infrastructure. However, it is difficult to meet the requirement of multiple LED anchors in range for successful and accurate positioning. In this paper, we proposed a loosely-coupled VLP-inertial fusion method for VLP, with an inertial measurement unit (IMU) and rolling shutter camera, to improve positioning robustness under LED shortage/outage. The efficacy of the proposed VLP scheme as well as the robustness under LED outage, handover situation and background light interference, are verified by real-world experiments1. The results show that our proposed scheme can provide an average accuracy of 2.1 cm (stationary localization) and the average computational time in low-cost embedded platforms is around 33 ms.

Robust and accurate zero-velocity detection can improve the performance of zero-velocity-aided foot-mounted inertial navigation system. To ensure the accuracy of zero-velocity detection, we propose a novel detector based on contrastive learning. This detector roughly eliminates the inertial data that must not be the zero-velocity event in advance, to reduce the computation cost. Then the detector uses the remaining inertial data to detect the zero-velocity event via a trained contrastive neural network. The contrastive neural network uses the triplet network and is trained by comparing with the anchor data which consists of the known static inertial data from the period of initial alignment. The classifier will finally determine whether the output of the triplet network is the zero-velocity event. Two experiments were conducted to evaluate this novel detector, showing that it can adaptively and accurately detect the zero-velocity event. The horizontal position errors of the two experiments are respectively 1.33m over a 953m outdoor path with walking and 3.74m over a 1143m indoor/outdoor path with combined motion of low dynamic and high dynamic.

Inertial navigation system (INS) is a practical method for indoor pedestrian navigation without pre-installation of infrastructure. Based on the fundamentals of human bipedal motion, zero velocity update (ZUPT) is a pervasive approach to tackle the accumulated error of inertial measurement units (IMU). While zero velocity detection plays a vital role in the algorithm, existing fixed-threshold methods to pick these pseudo-measurements of error-state Kalman Filter (ESKF) have the doubtful capability to fit various individuals in different motions. To address this issue, we propose the Symmetrical-Net leveraging deep Recurrent Convolutional Neural Networks (RCNNs) to detect the zero velocity interval adaptively. Additionally, two RCNNs are constructed in the symmetrical framework considering bidirectional IMU readings, which further improves the performance of the model. A comprehensive evaluation containing 87 different trajectories from 27 individuals has been conducted. The results show that the detection accuracy is up to 99.5% and 96.5% on the training and validation sets, respectively. It is verified that the precise and robust Symmetrical-Net can be a viable approach for the ZUPT-aided INS system.

The last decade has witnessed a growing demand for precise positioning in many applications, including autonomous car navigation. The safety features in autonomous driving and Advanced Driver Assistance Systems (ADAS) require lane-level positioning accuracy. Such accuracy can be obtained from the Global Navigation Satellite Systems (GNSS) through either differential techniques or Precise Point Positioning (PPP). PPP is currently favored over differential GNSS because it provides a global solution without the need for local reference stations. Nevertheless, employing PPP for land vehicles would be challenging due to frequent signal degradation and blockage. Integrating PPP with an Inertial Navigation System (INS) can solve the solution continuity problem; however, the INS solution drifts over time, resulting in losing the desired accuracy. Implementing a reliable PPP/INS system that can preserve the required accuracy is not trivial, especially with financial and computational cost constraints. This article proposes the integration of PPP with the Reduced Inertial Sensor System (RISS) for lane-level car navigation. The high-precision needed in lane-level positioning can be achieved by integrating PPP with high-end INS. Since high-end INS are expensive, this work proposes the use of RISS instead of the traditional INS. RISS uses only one gyroscope and two accelerometers, which can save more than half the high-end INS cost. The proposed PPP/RISS system was tested through three road tests that included highway driving under several overpasses. The system was able to maintain horizontal position errors of less than 50 cm.

This work presents the design and performance analysis of attitude determination algorithms for CubeSats that fuse rate gyros with a single vector measurement using a sensor set consisting of an in...

In recent years, the designers of long girder bridges in seismic areas have frequently opted for a continuous deck. One implication of this choice is that in many instances bridge abutments are called upon to carry large seismic forces, engaging the dynamic response of the soil–abutment system. To deal with this problem, this paper describes the formulation of a novel one-dimensional, inertial macroelement for simulating the dynamic behaviour of bridge abutments. The non-linear force–displacement relationship is characterised by a multi-surface plasticity model using a rigorous thermodynamic approach. The plastic response of the model is bounded by the ultimate capacity of the soil–abutment system that includes dissymmetry of the soil response in active and passive loading directions, while inertial effects transferred by the near-field approach embankment are simulated through appropriate participating masses in the macroelement formulation. The paper describes a straightforward calibration procedure of the proposed macroelement for horizontal, longitudinal loading of the abutment. The macroelement has been incorporated into a simplified, global, finite-element model of a multi-span girder bridge and validated through comparisons with results from a full three-dimensional (3D) dynamic time domain analysis under seismic loading. The inertial macroelement predictions of abutment deformations, axial deck loads and pier reaction forces are in very good agreement with the 3D soil–structure interaction model, and are achieved at much lower computational costs. The proposed inertial macroelement represents a significant improvement over existing simplified models based on linear response of the soil–abutment system.

With the improvement of the accuracy of the inertial system, the influence of the disturbing gravity field on the accuracy of long-range rocket has become increasingly prominent. However, in actual engineering, there are problems of low accuracy and being time-consuming for disturbing gravity field compensation. In view of this, this paper proposes a set of online comprehensive solutions combining disturbing gravity reconstruction and stellar correction. According to the pre-launch binding parameters, the net function assignment method is used in the navigation system to calculate disturbing gravity in the boost phase online. In the guidance system, a closed-loop guidance online compensation method is proposed based on the state-space perturbation method for the disturbing gravity in the coast phase. At the same time, the vertical deflection can also be corrected by stellar guidance. The calculation results are simulated and verified under different circumstances. Simulation results show that the proposed online compensation algorithm has an accuracy improvement compared with the element compensation algorithm on ground. And the stellar guidance algorithm can further correct the impact deviation. The impact deviation after comprehensive compensation does not exceed 50 m, and the compensation percentage is greater than 65%.

In this article, we consider the exponential consensus of coupled inertial (double-integrator) agents, particularly with the general setting of the damping and stiffness control gains. Each agent has one damping gain and one stiffness gain. Here, the damping and stiffness control gains of all agents can be both fully heterogeneous (FH) and fully variable (FV), which are called the FH-FV gains for convenience of reference. Specifically, the FH gains are defined as follows: 1) the damping gains of all agents are heterogeneous; 2) the stiffness gains of all agents are heterogeneous; and 3) the set of the damping gains and the set of the stiffness gains are distinct without dependence. Otherwise, the control gains are said partially heterogeneous (PH). The FV or partially variable (PV) aspect of control gains is defined similarly. The FH-FV gains setting is novel and generalizes the specially PH settings of constant gains in previous papers. We also consider the general FH-PV gains and the PH-PV gains. Then, we provide the series of conditions that ensure exponential convergence to consensus, for the agents with the FH-FV gains, the general FH-PV gains, and the PH-PV gains, respectively. The series of the conditions for each type of control gains has particular meaning for characterizing heterogeneity of the gains, especially, when the digraph of the agents is far-from-balanced.

In this paper, we propose two inertial projection methods to solve mixed variational inequalities without assuming monotonicity on the cost operator. Consequently, we show that the sequence of iterates generated by our proposed methods converge globally to a solution of the mixed variational inequality under the condition that the set of solutions of the dual mixed variational inequality is nonempty. Our convergence results are obtained without assuming any further stringent condition imposed in other related results in the literature. Moreover, our results improve on many important related results in the literature. Some numerical illustrations are given to show the efficiency of our proposed methods.

A three-dimensional low-order modeling technique is described for modeling both wing inertial and aerodynamic forces of animals and robots flapping heavy and morphing wings. The model is applied to...

In the era of internet of things (IoT), sensor plays a more vital role and its quality has a great impact on final measurement. From the viewpoint of final measurement, there are two kinds of effects: bias-only measurement and error-growth measurement. The bias-only measurement can be directly read from a sensing device, and each measurement is only distorted by the inherited bias error of the sensor, e.g., measuring temperature by a thermometer sensor. On the other hand, the error-growth measurement cannot be read directly but be calculated by combining multiple former sampling data, therefore multiple bias errors are accumulated in the measurement. For instance, in inertial navigation, the raw data sampled from an inertial measurement unit (IMU) are converted into a trajectory by multiple integral operations, so the errors of new sampling data are continuously added into the trajectory, and the deviation of the traced trajectory will reach an unacceptable level over time. Clearly, a good IMU trajectory strategy is the one with a less error-accumulation effect. Unfortunately, the analysis of error-growth effect remains not well studied, which motivates this work. This letter first proposes a theoretical error-growth effect analysis framework. Next, we use it to analyze three typical inertial methods, namely single-IMU method, gyro-free-IMU (GF-IMU) method, and -free accelerometer pair (OFAP) method. Finally, the theoretical derivations of the three inertial methods are proved by simulation results.

This paper proposed the high sensitive linear and rotational based differential capacitive inertial sensor. The designed sensor consists of a proof of mass, which resides in an air medium — the acceleration measured along with a change of displacement and the differential capacitance. Sensor modeling and simulation have done with CoventorWare software. SOIMUMPS fabrication process has described because it allows pattern on both sides of an SOI wafer. Solid mechanics and electrostatic analysis have applied for measurement of acceleration. The high sensitivity of 981.28fF/g has observed. The simulated results have tabulated. Finally, performance and future scope of the inertial sensor has discussed.

This paper investigates global dynamics in fractional-order dual inertial neural networks with time lags. Firstly, according to some crucial features of Mittag-Leffler functions and Banach contracting mapping principle, the existence and uniqueness of $ S $-asymptotically $ \omega $-periodic oscillation of the model are gained. Secondly, by using the comparison principle and the stability criteria of delayed Caputo fractional-order differential equations, global asymptotical stability of the model is studied. In the end, the feasibility and effectiveness of the obtained conclusions are supported by two numerical examples. There are few papers focus on $ S $-asymptotically $ \omega $-periodic dynamics in fractional-order dual inertial neural networks with time-varying lags, apparently, the works in this paper fill some of the gaps.

This paper is mainly concerned with the stability of inertial delayed neural networks (IDNNs) with stochastic delayed impulses. In contrast with previous related works, the stochastic impulsive intensity and density, and impulsive delays are considered simultaneously. Firstly, by appropriate variable substitution, the considered IDNNs model is converted to a first order differential equation with stochastic delayed impulsive effect. Secondly, by using the matrix measure approach and some stochastic analysis techniques, the stability criteria of IDNNs with stochastic delayed impulses are presented. Finally, numerical examples are provided to illustrate the obtained theoretical results.

Rat-robots show great potential for rescue and search applications in very tight spaces because of their excellent mobility and environmental adaptability. However, owing to the complexity of the working environment, there is currently no effective localization method. In this study, we present a novel rat-robot localization system based on dead reckoning. A wearable exoskeleton was used to measure the trajectories of the rat-robot’s two hindlimb ankle joints. A relational model was established to estimate the step length from the horizontal distance between the two ankle joints in the sagittal plane, and an inertial sensor obtained the heading of the rat-robot in the navigation coordinate system. The proposed system was tested by both the one-dimensional and two-dimensional locomotion experiment. The step length estimation error of our system was 0.6 ± 3.1%, and the positioning estimation error in the two-dimensional space was 1.65 ± 1.06%. These experimental results demonstrate the efficacy of this system in providing accurate distance and position estimation for the rat-robot.

This article is concentrated on the stabilization problem of inertial memristive neural networks (IMNNs) with time-varying delay. Here, removing the reduced order method, the stabilization of delayed IMNNs is directly studied under the framework of the second-order system. To decrease control cost, the aperiodically intermittent control strategy is adopted. This strategy implies that work time (with control input) alternates with rest time (without control input), and every work or rest width may be different. By constructing a suitable Lyapunov functional, some algebraic criteria are achieved to guarantee the stabilization of the considered system. Note that the obtained control gains are often larger than practical needs due to the conservatism deriving from inevitable inequality scaling. For further saving control resources, an intermittent adaptive control strategy is applied to stabilize the delayed IMNNs. Finally, a numerical example is exhibited to confirm the availability of the developed theoretical outcomes.