Low-Cost Sensors State Estimation Algorithm for a Small Hand-Launched Solar-Powered UAV

Abstract

In order to reduce the cost of the flight controller and improve the control accuracy of solar-powered unmanned aerial vehicle (UAV), three state estimation algorithms based on the extended Kalman filter (EKF) with different structures are proposed: Three-stage series, full-state direct and indirect state estimation algorithms. A small hand-launched solar-powered UAV without ailerons is used as the object with which to compare the algorithm structure, estimation accuracy, and platform requirements and application. The three-stage estimation algorithm has a position accuracy of 6 m and is suitable for low-cost small, low control precision UAVs. The precision of full-state direct algorithm is 3.4 m, which is suitable for platforms with low-cost and high-trajectory tracking accuracy. The precision of the full-state indirect method is similar to the direct, but it is more stable for state switching, overall parameters estimation, and can be applied to large platforms. A full-scaled electric hand-launched UAV loaded with the three-stage series algorithm was used for the field test. Results verified the feasibility of the estimation algorithm and it obtained a position estimation accuracy of 23 m.

Vector field path following of a full-wing solar-powered Unmanned Aerial Vehicle (UAV) landing based on Dubins path: a lesson from multiple landing failures

“…The initial position of the UAV is (0, 0), and the velocity is in the positive direction along the x-axis. The solid line represents the real state, and the dotted line represents the estimated state from the sensor measurement to the Extended Kalman Filter (EKF) based data fusion model (9) . Implementing sensor measurement and state estimation is to increase the credibility and fully analyse the source of error.…”

“…At present, the UAV has completed several flights in different areas and altitudes, with a maximum flight endurance of 27h and a maximum flight altitude of 5,000m, with total flights exceeding 100 and total endurance exceeding 1,000h. With the long endurance verification completed, there are still some deficiencies that need to be further researched and improved, such as a more accurate state estimation algorithm (9) , an optimal energy utilisation path planning (10,11) and a stable control in landing. Unlike a normal control scheme, which directly performs roll control by aileron or rudder (12) , the differential throttle generates the yaw moment first and then the roll moment.…”

Sensors and Sensor’s Fusion in Autonomous Vehicles

“…In order to reduce the cost of the flight controller and improve the control accuracy of the solar-powered unmanned aerial vehicle (UAV), Guo A. et al in the article [ 8 ] proposed three state estimation algorithms based on the extended Kalman filter (EKF) with different structures: three-stage series and full-state direct and indirect state estimation algorithms. A small hand-launched solar-powered UAV without ailerons was used as the object with which to compare the algorithm structure, estimation accuracy, and platform requirements and application.…”

Automatic Control and Model Verification for a Small Aileron-Less Hand-Launched Solar-Powered Unmanned Aerial Vehicle

“…The key of this algorithm is to reduce the airspeed and altitude estimation accuracy, improve the heading angle accuracy, and make full use of the limited computing ability to improve the position tracking precision to meet the mission requirement. For example, a four-sided route with an area of 1 km 2 has an acceptable tracking error of approximately 30 m (30 m/km 2 ) and a heading error of nearly 13 degrees, and the simulation conditions are the same as before with an altitude of 600 m, an airspeed of 12.5 m/s, and a rolling angle hovering of 20 degrees in windless conditions [10].…”

“…At present, low-cost flight control platforms have been widely used in miniature air vehicles [9]. Smaller scale, lower-cost solar-powered hand-launched UAVs have the advantages of easier civilian application and marketization, yet there still might be a large amount of sensor errors, vulnerability to lower atmosphere influence, and flight instability [10]. Besides, the aileron-less design places a higher demand on the flight control system, and it is necessary to overcome the problems of only the rudder for lateral control and the lower measurement precision of a low-cost flight controller.…”