Search Results (27)

In this paper, Flare Bright presents flight test results gathered using a ~2m fixed wingspan drone to demonstrate the capability that has been achieved using an Inertial Navigation System (INS) augmented by Machine Learning tuned software. INSs, using Inertial Measurement Units (IMUs), are invaluable for position estimation in GNSS-compromised environments as no external information is required. However, with no absolute measurement of a vehicle’s position or attitude, INSs suffer from significant drift over time. The results from a robust flight test programme, over multiple vehicles, terrains and flight paths, show how Flare Bright combined a low cost and low SWaP (space, weight and power) IMU, with their patent-pending software-only techniques, to boost INS performance to the degree of besting a ‘tactical grade’ IMU in ~20 min. These results credibly demonstrate the value of Flare Bright’s solution as an effective, low-cost and low-weight INS for extended flight operations of small uncrewed aerial systems in GNSS-compromised environments, with performance comparable to heavier, more expensive high-end IMUs. Full article

The accurate acquisition and real-time calculation of the attitude angle of precision firefighting extinguishing projectiles are essential for ensuring stable flight and precise extinguishing agent release. However, measuring the roll attitude angle in such projectiles is challenging due to their highly dynamic nature and environmental disturbances such as fire smoke, high temperature, and electromagnetic interference. Traditional methods for measuring attitude angles rely on multi-sensor fusion schemes, which suffer from complex structure and high cost. This paper proposes a single-gyro attitude calculation method based on micro-electromechanical inertial measurement units (MIMUs). This method integrates Fourier transform time-frequency analysis with a second-order Infinite Impulse Response (IIR) bandpass filtering algorithm optimized by dynamic coefficients. Unlike conventional fixed-coefficient filters, the proposed algorithm adaptively updates filter parameters according to instantaneous roll angular velocity, thereby maintaining tracking capability under time-varying conditions. This theoretical contribution provides a general framework for adaptive frequency-tracking filtering, beyond the specific engineering case of firefighting projectiles. Through joint time-frequency domain processing, it achieves high-precision dynamic decoupling of the roll angle, eliminating the dependency on external sensors (e.g., radar/GPS) inherent in conventional systems. This approach drastically reduces system complexity and provides key technical support for low-cost and high-reliability firefighting projectile attitude control. The research contributes to enhancing the effectiveness of urban firefighting, forest fire suppression, and public safety emergency response. Full article

The increasing demand for on-orbit servicing (OOS), such as satellite life extension and space debris removal, has highlighted the need for research into precise relative navigation between space objects. Model-based tracking (MBT) was applied using the imaging data for relative navigation, incorporating SPNv2 (Spacecraft Pose Network v2) for an initial pose estimation. Furthermore, the performance of General Loss was evaluated by applying it during the model tracking processes and comparing it with seven other robust M-estimators, including Tukey, Welsch, and Huber. The simulations were conducted in a ROS–Gazebo environment that emulated a rendezvous with the International Space Station (ISS). Six approach profiles were generated by pairing three mutually different conic-section apertures with two attitude modes—boresight locked on the ISS versus boresight fixed on the inertial origin—producing six distinct spiral trajectories that bring the chaser from 500 m to 100 m along the depth axis of the camera. General Loss achieved superior estimation accuracy in most profiles. Thus, the proposed algorithm, which integrates General Loss into the MBT-based relative navigation framework, provides robust and stable performance in the presence of diverse residual distributions and outliers. In the few instances where it did not yield the very best results, the initial error arose from matching virtual edges—generated according to the sample weight distribution—to the actual edges in the image frame; notably, by the end of the simulation, when the camera reached a depth of approximately 100 m, these errors were substantially reduced. Thus, the proposed algorithm, which integrates General Loss into the MBT-based relative navigation framework, provides robust and stable performance in the presence of diverse residual distributions and outliers. Full article

This paper presents a strategy for collision detection and recovery control of drones using an onboard Inertial Measurement Unit (IMU). The collision detection algorithm compares the expected response of the drone with the measurements from the IMU to identify and characterize collisions. The recovery controller implements a gain scheduling approach, adjusting its parameters based on the characteristics of the collision and the drone’s attitude. Simulations were conducted to compare the proposed collision detection strategy with a popular detection method with fixed thresholds, and the simulation results showed that the proposed approach outperformed the existing method in terms of detection accuracy. Furthermore, the proposed collision detection and recovery control approaches were tested with physical experiments using a custom-built drone. The experimental results confirmed that the proposed collision detection algorithm was able to distinguish between actual collisions and aggressive flight maneuvers, and the recovery controller can recover the drone within 0.8 s. Full article

A positioning method for a roadheader based on fiber-optic strap-down inertial navigation and binocular vision is proposed to address the issue of low measurement accuracy of the mining machine position caused by single-sensor methods in underground coal mines. A vision system for the mining machine position is constructed based on the four-point target fixed on the body of the roadheader, and the position and attitude information of the roadheader are obtained by combining the inertial navigation on the body. To deal with the problem of position detection inaccuracies caused by the accumulation of errors in inertial navigation measurements over time and disturbances from body vibrations to the combined positioning system, an Adaptive Derivative Unscented Kalman Filtering (ADUKF) algorithm is proposed, which can suppress the impact of process variance uncertainties on the filtering. The simulation results demonstrate that, compared to the Unscented Kalman Filtering algorithm, the position errors in the three directions are reduced by 20%, 20.68%, and 28.57%, respectively. Experiments demonstrate that the method can compensate for the limitations of single-measurement methods and meet the positioning accuracy requirements for underground mining standards. Full article

The initial attitude error is challenging to satisfy the requirements of the linear model due to the complex nature of the ocean environment. This presents a challenge in the transfer alignment of the ship. In order to enhance the precision and velocity of ship transfer alignment, as well as to streamline the alignment processes, this paper proposes a transfer alignment methodology based on the Earth-Centered Earth-Fixed (ECEF) frame special Euclidean group (SE(3)) matrix Lie group. After introducing the two navigation states, velocity and attitude, from the ECEF frame into SE(3), the nonlinear inertial navigation system error state model and its corresponding measurement equations are derived based on the mapping relationship between the Lie groups and Lie algebra. The method effectively solves the error problem due to linear approximation in the traditional transfer alignment method, and applies to misalignment angles of arbitrary scale. The simulation results verify the effectiveness and rapidity of the proposed alignment method in the case of arbitrary misalignment angles. Full article

Without a GNSS (global navigation satellite system) signal, the integrated navigation system in vehicles with a fixed trajectory (e.g., railcars) is limited to the use of micro-electromechanical system-inertial navigation system (MEMS-INS) and odometer (ODO). Due to the significant measurement error of the MEMS inertial device and the inability of ODO to output attitude, the positioning error is generally large. To address this problem, this paper presents a new integrated navigation algorithm based on a dynamically constrained Kalman model. By analyzing the dynamics of a railcar, several new observations have been investigated, including errors of up and lateral velocity, centripetal acceleration, centripetal D-value (difference value), and an up-gyro bias. The state transition matrix and observation matrix for the error state model are represented. To improve navigation accuracy, virtual noise technology is applied to correct errors of up and lateral velocity. The vehicle-running experiment conducted within 240 s demonstrates that the positioning error rate of the dead-reckoning method based on MEMS-INS is 83.5%, whereas the proposed method exhibits a rate of 4.9%. Therefore, the accuracy of positioning can be significantly enhanced. Full article

Compared with traditional control methods, moving mass control (MMC) enhances the aerodynamic efficiency and stealth performance of fixed-wing unmanned aerial vehicles (FWUAVs), thereby facilitating their broader application in military and civilian fields. Nevertheless, this approach increases system complexity, nonlinearity, and coupling characteristics. To address these challenges, a novel attitude controller is proposed using adaptive global fast terminal sliding mode (GFTSM) control. Firstly, a dynamic model is established based on aerodynamics, flight dynamics, and moving mass dynamics. Secondly, to improve transient and steady-state responses, prescribed performance control (PPC) is adopted, which enhances the controller’s adaptability for mass-actuated aircraft. Thirdly, a fixed-time extended state observer (FTESO) is utilized to solve the inertial coupling issue caused by mass block movement. Additionally, the performance of the entire control system is rigorously proven through the Lyapunov function. Finally, numerical simulations of the proposed controller are compared with those of PID and linear ADRC in three different conditions: ideal conditions, fixed aerodynamic parameters, and nonlinear aerodynamic parameter changes. The results indicate that the controller effectively compensates for the system’s uncertainty and unknown disturbances, ensuring rapid and accurate tracking of the desired commands. Full article

The Electric Solar Wind Sail (E-sail) is a propellantless propulsion system that converts solar wind dynamic pressure into a deep-space thrust through a grid of long conducting tethers. The first flight test, needed to experience the true potential of the E-sail concept, is likely to be carried out using a single spinning cable deployed from a small satellite, such as a CubeSat. This specific configuration poses severe limitations to both the performance and the maneuverability of the spacecraft used to analyze the actual in situ thruster capabilities. In fact, the direction of the spin axis in a single-tether configuration can be considered fixed in an inertial reference frame, so that the classic sail pitch angle is no longer a control variable during the interplanetary flight. This paper aims to determine the polar form of the propelled trajectory and the characteristics of the osculating orbit of a spacecraft propelled by a low-performance spinning E-sail with an inertially fixed axis of rotation. Assuming that the spacecraft starts the trajectory from a parking orbit that coincides with the Earth’s heliocentric orbit and that its spin axis belongs to the plane of the ecliptic, a procedure is illustrated to solve the problem accurately with a set of simple analytical relations. Full article

People with upper limb disabilities or high quadriplegia have extremely high requirements for the maneuverability and functionality of power wheelchairs. Normal wheelchairs cannot meet travel tasks, while smart customized wheelchairs are expensive and cannot be popularized. Therefore, a novel extension control instrument for power wheelchairs with low cost, strong scalability, and convenient usage is proposed, which can realize the control of the wheelchair by sensing a change of head posture. The device is divided into a head motion sensing unit (HMSU) and a wheelchair assistance control unit (WACU). The mapping relationship between the head attitude and the subject’s motion intention is established. The inertial measurement module in the HMSU collects the head attitude data and uses the Kalman filtering method to obtain the accurate Euler angle. The WACU is fixed on the original controller of the wheelchair. The joystick is inserted into the extended control mechanism and controlled, instead of the hand, through a 2-degree-of-freedom servo system combined with the pinion and rack push rod structure, thus controlling the movement of the wheelchair. In proceeding, the system can also detect the distance of objects in the environment in real time through the three-direction (front, left, right) ultrasonic ranging sensors installed on the WACU, with a certain obstacle avoidance function. The prototype experiments prove that the extension control instrument developed in this paper based on the Kalman filter can quickly and accurately identify head motion and accurately control the movement of the wheelchair. It is easy to operate and has strong universality, which presents a new low-cost solution for the travel of patients with disabilities. Full article

This paper studies the robust stabilization of rigid-body attitudes represented by a special orthogonal matrix. A geometric proportional–integral–derivative (PID) controller is proposed with all the input commands defined in the dual space ${\mathfrak{so}}^{*}\left(3\right)$ of a Lie algebra for left-invariant systems evolving on a Lie group $SO\left(3\right)$. Almost global asymptotic stability (AGAS) of the close system is proved by constructing a gradient-descent Lyapunov function after explicitly performing two stages of variable change. The attitudes are stabilized to the stable equilibrium despite the influence of inertially fixed biases. The convergent behaviors and the robustness to biases are verified by numerical simulations. Full article

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 ${\displaystyle \mathbb{SO}\left(3\right)}$ 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. Full article

To deal with the attitude tracking control problem of a struck or pierced geocentric polar displaced solar sail (GPDSS), an attitude adaptive control strategy is proposed in this paper under the complex conditions of unknown inertial parameters, external disturbance and input saturation. First, on the basis of a flexible solar sail spacecraft attitude dynamics model with damping terms and vibration initial values, an integrated disturbance term, including inertial parameter uncertainties and external disturbance, is constructed. Second, a radial basis function neural network is applied to design a disturbance estimator with an adaptive law to estimate the integrated disturbance in real time. Then, a sliding-mode controller with fixed-time convergence in the reach phase and finite-time stability in the sliding phase is designed, and stability analysis is conducted by using the Lyapunov theory. Finally, comparative simulations with a linear sliding-mode controller and numerical simulations under various workings are performed. The results show that the designed adaptive control strategy can effectively achieve the attitude tracking control of the GPDSS. Full article

Eliminating the magnetic interference of the carrier platform is an important technical link and plays a vital role in aeromagnetic survey. The traditional compensation method is based on the Tolles–Lawson (T-L) model and establishes the linear relationship between the aircraft interference magnetic field and the aircraft attitude. The compensation coefficients are solved by designing the calibration flight. At present, almost all aeromagnetic systems use the fluxgate magnetometer fixed to the aircraft to realize the attitude measurement of the flight platform. However, the fluxgate magnetometer has problems, such as non-orthogonal error, zero drift error, and linearity error limited by the production process, and the fluxgate magnetometer is also very susceptible to external magnetic interference as a magnetic sensor. These lead to the aircraft attitude calculated by the fluxgate magnetometer being inaccurate, thus reducing the compensation effect. In this article, we analyze the influence of the fluxgate magnetometer noise on compensation and propose a new conjunctive compensation method based on inertial navigation systems (INS) and fluxgate magnetometer information to improve the compensation effect. The flight experiment data show that the proposed method can significantly improve the quality of aeromagnetic data. Compared with the traditional compensation method only based on fluxgate magnetometer information, the improved ratio is increased by 30–60%, and it is a real-time compensation method. It shows that the proposed method has a remarkable compensation effect for aeromagnetic interference. Full article

Small uncrewed aerial systems (sUASs) have the potential to serve as ideal platforms for high spatial and temporal resolution wildfire measurements to complement aircraft and satellite observations, but typically have very limited payload capacity. Recognizing the need for improved data from wildfire management and smoke forecasting communities and the potential advantages of sUAS platforms, the Nighttime Fire Observations eXperiment (NightFOX) project was funded by the US National Oceanic and Atmospheric Administration (NOAA) to develop a suite of miniaturized, relatively low-cost scientific instruments for wildfire-related measurements that would satisfy the size, weight and power constraints of a sUAS payload. Here we report on a remote sensing system developed under the NightFOX project that consists of three optical instruments with five individual sensors for wildfire mapping and fire radiative power measurement and a GPS-aided inertial navigation system module for aircraft position and attitude determination. The first instrument consists of two scanning telescopes with infrared (IR) channels using narrow wavelength bands near 1.6 and 4 µm to make fire radiative power measurements with a blackbody equivalent temperature range of 320–1500 °C. The second instrument is a broadband shortwave (0.95–1.7 µm) IR imager for high spatial resolution fire mapping. Both instruments are custom built. The third instrument is a commercial off-the-shelf visible/thermal IR dual camera. The entire system weighs about 1500 g and consumes approximately 15 W of power. The system has been successfully operated for fire observations using a Black Swift Technologies S2 small, fixed-wing UAS for flights over a prescribed grassland burn in Colorado and onboard an NOAA Twin Otter crewed aircraft over several western US wildfires during the 2019 Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) field mission. Full article