Abstract:The dynamic model parameter identification is important for unmanned aerial vehicle modeling and control. The unmanned aerial vehicle model parameters are usually identified through wind tunnel experiments, which are complex. In this paper, a model parameter identification method is proposed using the flight data for quadrotors. The parameters of the thrust, drag force, torque, rolling moment and pitching moment are estimated through Kalman filter. Global positioning system and inertial sensors are used as mea… Show more
Express by micro aerial vehicle (MAV) becomes more and more popular because it can avoid the influence of terrain and save more space for taking-off and landing of aircraft. At present, quadrotor is often used in the express industry due to its flexibility and easy operation, and the flight trajectory plays an important role in the efficiency and safety level of express service. In this paper, the trajectory planning problem is studied for quadrotor delivering goods in urban environment with the purpose of avoiding the heavy ground traffic, and a cuckoo search (CS)-based trajectory planning method is proposed to solve the problem. First, a conceptual model containing all the key elements of the delivery task is developed, which presents a general idea of solving the problem. Some characteristics of the urban environment and the delivery task, such as the wind field, dense buildings and inclination of shipped goods, are taken into account in the trajectory planning model. The goal of the delivery task is to make the goods reach the destination accurately. When designing the CS-based trajectory planning algorithm, the basics of CS algorithm are explained, and then it is integrated into the trajectory planning problem. Comparative experiments are carried out to investigate the superiority of the proposed method, and the influences of parameters in CS algorithm are also discussed to conclude its performance in trajectory planning problem.
Express by micro aerial vehicle (MAV) becomes more and more popular because it can avoid the influence of terrain and save more space for taking-off and landing of aircraft. At present, quadrotor is often used in the express industry due to its flexibility and easy operation, and the flight trajectory plays an important role in the efficiency and safety level of express service. In this paper, the trajectory planning problem is studied for quadrotor delivering goods in urban environment with the purpose of avoiding the heavy ground traffic, and a cuckoo search (CS)-based trajectory planning method is proposed to solve the problem. First, a conceptual model containing all the key elements of the delivery task is developed, which presents a general idea of solving the problem. Some characteristics of the urban environment and the delivery task, such as the wind field, dense buildings and inclination of shipped goods, are taken into account in the trajectory planning model. The goal of the delivery task is to make the goods reach the destination accurately. When designing the CS-based trajectory planning algorithm, the basics of CS algorithm are explained, and then it is integrated into the trajectory planning problem. Comparative experiments are carried out to investigate the superiority of the proposed method, and the influences of parameters in CS algorithm are also discussed to conclude its performance in trajectory planning problem.
“…With such methods, the precision of the inertia parameters are impacted by the precision of the other ones previously measured. • Estimation of the dynamic parameters from the analysis of flight data [12]- [14] by means of a Kalman filter assuming the knowledge of, at least, the propeller coefficients of thrust and torque. Those algorithms all require a knowledge of the propeller coefficients.…”
“…For instance, in [20], the aerodynamic parameters of a fixed-wing aircraft and a rotary-wing (helicopter) UAV were estimated. More recently, in [21], a linear Kalman filter-based method was proposed for identifying the dynamic model parameters of quadrotors using flight data. The above method is similar to our work in the sense that both approaches aim to estimate the dynamic model parameters using data available from the onboard sensors.…”
Section: Introductionmentioning
confidence: 99%
“…The above method is similar to our work in the sense that both approaches aim to estimate the dynamic model parameters using data available from the onboard sensors. However, in [21], the parameters to be estimated were grouped into coefficients. In our work, the model parameters are explicitly estimated (i.e., inertia parameters).…”
Section: Introductionmentioning
confidence: 99%
“…Furthermore, in the previous work, a linear analysis approach was used, while in the present work, a non-linear approach is proposed. Moreover, in [21], only a subset of coefficients was recursively estimated (the least squares method was also used), while in this proposed approach, all the parameters are recursively estimated by means of EKF. Additionally, the proposed approach is developed for three class of multi-rotor vehicles instead of one.…”
This work presents a method for estimating the model parameters of multi-rotor unmanned aerial vehicles by means of an extended Kalman filter. Different from test-bed based identification methods, the proposed approach estimates all the model parameters of a multi-rotor aerial vehicle, using a single online estimation process that integrates measurements that can be obtained directly from onboard sensors commonly available in this kind of UAV. In order to develop the proposed method, the observability property of the system is investigated by means of a nonlinear observability analysis. First, the dynamic models of three classes of multi-rotor aerial vehicles are presented. Then, in order to carry out the observability analysis, the state vector is augmented by considering the parameters to be identified as state variables with zero dynamics. From the analysis, the sets of measurements from which the model parameters can be estimated are derived. Furthermore, the necessary conditions that must be satisfied in order to obtain the observability results are given. An extensive set of computer simulations is carried out in order to validate the proposed method. According to the simulation results, it is feasible to estimate all the model parameters of a multi-rotor aerial vehicle in a single estimation process by means of an extended Kalman filter that is updated with measurements obtained directly from the onboard sensors. Furthermore, in order to better validate the proposed method, the model parameters of a custom-built quadrotor were estimated from actual flight log data. The experimental results show that the proposed method is suitable to be practically applied.
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