A new spacecraft attitude estimation approach using particle filtering is derived. Based on sequential Monte Carlo simulation, the particle filter approximately represents the probability distribution of the state vector with random samples. The filter formulation is based on the star camera measurements using a gyro-based or attitude dynamics-based model for attitude propagation. Modified Rodrigues parameters are used for attitude parametrization when the sample mean and covariance of the attitude are computed. The ambiguity problem associated with the modified Rodrigues parameters in the mean and covariance computation is addressed as well. By using the uniform attitude probability distribution as the initial attitude distribution and using a gradually decreasing measurement variance in the computation of the importance weights, the particle filter based attitude estimator possesses global convergence properties. Simulation results indicate that the particular particle filter, known as bootstrap filter, with as many as 2000 particles is able to converge from arbitrary initial attitude error and initial gyro bias errors as large as 4500 degrees per hour per axis.
This work provides a theoretical analysis for optimally solving the pose estimation problem using total-least-squares for vector observations from landmark features, which is central to applications involving simultaneous localization and mapping. First, the optimization process is formulated with observation vectors extracted from point-cloud features. Then, error-covariance expressions are derived. The attitude and position estimates obtained via the derived optimization process are proven to reach the bounds defined by the Cramér–Rao lower bound under the small-angle approximation of attitude errors. A fully populated observation noise-covariance matrix is assumed as the weight in the cost function to cover the most general case of the sensor uncertainty. This includes more generic correlations in the errors than previous cases involving an isotropic noise assumption. The proposed solution is verified using Monte Carlo simulations, a Gazebo simulation in a robotics operating system, and an experiment with an actual light detection and ranging sensor to validate the error-covariance analysis.
Most conventional therapies have limitations in the repair of complex wounds caused by chronic inflammation in patients with diabetic foot ulcers (DFUs). In response to the demand for more biotechnology strategies, bioprinting has been explored in the regeneration field in recent years. However, challenges remain regarding the structure of complex models and the selection of proper biomaterials. The purpose of this review is to introduce the current applications of bioprinting technology in chronic diabetic foot wound healing. First, the most common application of bioprinting in producing skin equivalents to promote wound healing is introduced; second, functional improvements in the treatment of chronic and difficult-to-heal DFU wounds facilitated by bioprinting applications are discussed; and last but not least, bioprinting applications in addressing unique diabetic foot disease characteristics are summarized. Furthermore, the present work summarizes material selection and correlations between three-dimensional (3D) bioprinting and a variety of biomimetic strategies for accelerating wound healing. Novel, biotechnological tools such as organoids for developing new biomaterials for bioprinting in the future are also discussed.
Aiming at the improvement of autonomy, adaptability and robustness of the reentry guidance technology, a new predictor-corrector reentry guidance law based on online model identification is proposed under the background of the new generation of reusable aircraft. On the basis of equilibrium glide condition, a feasible control envelope is established to satisfy the reentry constraints. The corresponding relationship between the feasible control profile and the feasible range capability is analyzed and proved, thus the initial trajectory generator (ITG) based on the Gauss-Newton method is designed. The generator does not rely on ground equipment, and has certain autonomy for the circumstance of task adjustment and emergency situation in flight process. Aiming at nonlinearity and uncertainty of the reentry model, an adaptive trajectory corrector based on sliding mode guidance law and RBF neural network which is used to approximate the nonlinear function is proposed. The control profile is adaptively modified by the guidance to achieve real-time correction of longitudinal deviation. Besides, the closed-loop control law is enforced by real-time feedback of the aircraft state, and the guidance command is restricted so as not to depend on the equilibrium gliding condition. The lateral guidance adopts the bank angle reversing strategy to decrease the cross-range progressively, so that the number of reversal is controllable. The simulation results show that the guidance method has high accuracy and good robustness in the presence of multiple disturbances and deviations.
This paper presents a study of the reset step in the multiplicative extended Kalman filter (MEKF). This filter is widely used for spacecraft attitude estimation, which typically involves estimating the attitude and gyro drift in real time using external sensors such as star trackers. The basic idea of the MEKF is to use the quaternion or direction-cosine matrix as the “global” attitude parameterization and a three-component state vector for the “local” parameterization of attitude errors. The true attitude is expressed as the product of the error attitude and the estimate rather than as the sum of the error and the estimate. The reset operation moves the local error to the global variable. This reset does not add new information, but it changes the reference frame for the attitude error covariance. This results in an error-covariance reset that is very different from the measurement update of the error covariance in the MEKF. The effects of using an error-covariance reset in the MEKF are analyzed in this work. The results from this work can be applied to any application involving attitude estimation as part of its process, such as inertial navigation.
The traditional predictive correction algorithm requires a large number of iterative calculations for the predicted trajectory, which greatly occupies a large amount of computing resources, so that the real-time solution of the guidance command can not be guaranteed, and the guidance accuracy will have a large impact. And the prediction correction guidance requires the algorithm to have the ability of selfadaptation and intelligent learning. Therefore, this paper proposes a cross-cycle iterative hypersonic UAV predictive correction guidance method based on reinforcement learning. The parametric control variable (CVP) method is used to construct the parametric model of the guidance command. The actor-critic-based reinforcement learning method is used to solve the guidance command in real time, and the guidance information is effectively transmitted in the adjacent guidance solution cycle. The guidance error converges to within the allowable accuracy range during the cross-cycle iteration. Monte Carlo simulation shows that the proposed method has good adaptability to initial conditions and flight parameter uncertainty, and can guarantee the real-time performance of the guidance command while achieving high-precision guidance.
Along with the growing demands of hypersonic vehicle reentry guidance in autonomy, robustness, and situation with insufficient performance of current methods, one compound reentry guidance method is proposed based on altitude-velocity reference profile on-board regeneration and tracking. Aiming at the vehicle reentry problem, overall guidance scheme and related key technologies are studied in this work, and vehicle feasible trajectory on-board planning subject to multiple constraints and adaptive trajectory tracking problem are specially focused on. On basic of reentry problem research and constraints analysis, a novel kind of compound altitude-velocity (short for HV) corridor is designed on-line, in consideration of current state, path constraints, vehicle flying capability and terminal condition constraints. New compound HV corridor provides feasible flight envelope with satisfying constraints. The tracking reference profile is obtained by weighting the upper and lower bounds of HV corridor and strict function monotone property between weighted coefficient and flyable range is also proved. Gauss-Newton method is introduced to solved the transformed single parameter and single constraint problem. For sake of avoiding integral in range prediction, the designed altitude-velocity profile is fitted with Lagrange polynomials and the Legendre primary functions help algorithm improve running speed significantly. Pole place and PID control methods are introduced to finish the tracker design of reference profile. The above researches constitute an autonomous, robust and reliable entry guidance scheme for hypersonic vehicles. Feasible trajectory validity test and Monte Carlo simulations illustate that the proposed compound guidance method performs well in reentry flight under conditions of initial launch deviation, parameter uncertainty and strong interference. New method has be proved with remarkable performance of autonomy, adaptability and robustness.
A numerical multi-constrained predictor-corrector guidance algorithm is proposed in this study, focusing on real-time longitudinal trajectory generation, constraint management, and lateral guidance improvement. First, a new compound bank corridor is designed to help convert the complicated trajectory planning problem into a root-finding problem, which is identified by a recursive least squares estimation algorithm and solved by a newly proposed period-crossing steepest descent method within a fraction of a second in Matlab. Second, three constraint enforcement operators are designed based on state prediction and feedback theory, and the long-standing constraint violation problem of predictor-corrector algorithms is addressed by on-board bank angle compensation. Additionally, a new predictive lateral guidance algorithm is proposed based on a cross-range proportional decrement strategy, and the resulting new bank reversal logic with only one user-defined parameter has better performance on reversal controllability and guidance robustness than the traditional algorithms. Finally, extensive numerical simulations are carried out for different mission scenarios with significant dispersions, and the new methodology is proved to be capable of autonomous and robust guidance flight for reentry vehicles.
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)
