The strapdown inertial navigation system (SINS) and Doppler velocity log (DVL) are the major positioning system for underwater vehicles. The initial alignment process is the first working stage of SINS/DVL integration. Currently, the initial alignment method for SINS/DVL integration is imperfect. The major problems can be summarized in two aspects. One is the initial velocity errors and outliers of DVL outputs, which are not suppressed simultaneously. The other is the positioning uncertainty when the attitude alignment is finished. To address these two problems, robust attitude and positioning alignment methods are proposed in this article. First, a vector observation, which is based on the sliding windows improvements, is constructed for eliminating the initial velocity errors. Second, the apparent velocity motion model for the vector observation with the sliding window improvements is constructed. Based on the constructed model, a parameter estimation method, which is established by a robust Kalman filter, is proposed. Using the estimated parameters, the new observed vectors are reconstructed. Thus, the robust attitude alignment process is finished. Third, real-time and postprocessing positioning alignment methods are proposed for addressing positioning uncertainty. At last, the simulation and field tests are designed for verifying the performance of the proposed method. The test results are shown that the existing two problems are overcome.

A novel passing priority allocation method, based on the collision avoidance urgency, used for a formation of UAVs in a serried obstacle laden environment, is presented in this paper. The method employs the collision cone and the prediction of the collision time to evaluate the urgency of imminent collision of each UAV, so as to establish a primary criterion for the priority allocation. And, the priority update policy is also innovative designed to alleviate burdens on the instability of frequent shifty context. This paper exploits the potential field in a cooperative way to plan the path for each UAV, steering the UAVs to a given three-dimensional position, which combines the adaptive priorities to guide the UAVs passing through the barriers in turn. Numerical simulations and semi-physical experiments in designed outdoor condition are carried out to compare the performance of the proposed adaptive priorities with the regular-used default priorities. The total passing time and the energy cost of the semi-physical tests provide the promising validation in terms of the efficiency and superiority of the proposed method.

In this paper, an improved in-motion alignment method for SINS/GPS integration is proposed. The alignment errors, which are caused by the maneuvering of the vehicle and the accumulated errors of inertial sensor errors, are suppressed. Firstly, the traditional in-motion alignment method for SINS/GPS integration is reviewed, and the existing problems, which are accumulated errors in the observed vectors, are investigated. To address these problems, a sliding windows operation for vector observation optimization is proposed. The derivation for the calculation of the new vector observations is given in detail. Using the new vector observations, the accumulated errors, which are existing in the observed vectors, are weakened. Thus, the alignment results are more stable whenever any maneuvering of the vehicle. Lastly, the simulation and field tests are designed for verification of the proposed method. Compared with the currently popular methods, the alignment errors are converged to a small value quickly.

The rapid developments of artificial intelligence in the last decade are influencing aerospace engineering to a great extent and research in this context is proliferating. In this paper, the trajectory optimization of a three-stage launch vehicle in the powering phase subject to the sun-synchronous orbit is considered. To solve the optimal control problem, the Gauss pseudo-spectral method (GPM) is used to transform the optimization model to a nonlinear programming (NLP) problem and sequential quadratic programming is applied to find the optimal solution. However, the sensitivity of the initial guess may cost the solver significant time to do the Newton iteration or even lead to the local minimum. Aiming at this issue, a good initial guess generated by combination of harmony search and GPM is introduced to help the optimizer to faster and better solve the sun-synchronous orbit (SSO) trajectory optimization. Comparative simulation tests are conducted with the proposed algorithm and popular approaches, the results indicate that with the optimized initial guess, the proposed method could achieve better performance in terms of convergence ability and convergence rate.

With the rapid development of unmanned aerial vehicle technology, unmanned aerial vehicles (UAVs) have been widely used in the field of agricultural plant protection. Compared with fuel-driven UAVs, electrically driven rotorcrafts have many advantages such as lower cost, simpler operation, good maneuverability and cleaner power, which them popular in the plant protection. However, electrical rotorcrafts still face battery problems in actual operation, which limits its working time and application. Aiming at this issue, this paper studied the influence of rotorcraft flight parameters on energy consumption through series of carefully designed flight experiments. First of all, the linear motion experiments have been designed that the rotorcraft was made to perform speed tests and acceleration test with the speed varied from 2∼9 m/s. Secondly, the turning maneuver experiments are carried out under the different circular routes, a rotorcraft was made to conduct successive steering maneuvers at a certain speed of 2 m/s. With the collected tests data, the relation of the energy consumption and the flight dynamic parameter are analyzed through correlation analysis, and the test results of different pairs of experiments have been compared. The research results of this paper would encourage the agricultural rotorcraft to make less maneuvers during operation, which can also provide practical experience and data support for subsequent optimization of flight parameters and reduction of energy consumption.

In this paper, the in-motion polar alignment method for SINS/GPS integration is proposed. Firstly, to address the defects of the fast convergence of the longitude with the navigational mechanization, the unified navigation mechanization is used to construct the vector observations for the in-motion alignment method. Secondly, the initial velocity errors, which are generated by the GPS outputs, are considered. To suppress the initial velocity errors, the improved vector observation with the vector subtraction improvements is investigated. With the vector subtraction improvements, the initial velocity errors are eliminated efficiently. Thirdly, to enhance the correction of the observation vectors for the attitude determination the continuous attitude determination method with the expansion samples of the GPS outputs is designed. Lastly, to verify the performance of the proposed method, the virtual polar region technology in the transverse mechanization is devised. The simulation and field tests are designed to verification, the test results are shown that the proposed method can finish the in-motion polar alignment process, and the initial velocity errors are suppressed. Moreover, the subtraction operation for observation vectors do not degrade the performance of the alignment process.