In recent years, Unmanned Aerial Vehicles (UAVs) have become increasingly popular for various applications such as rescue and delivery due to their high flexibility and mobility. However, UAVs lack some essential abilities compared to Unmanned Ground Vehicles (UGVs), like high power efficiency and robustness. To overcome these limitations, researchers are exploring the concept of terrestrial-aerial robots, which combine the advantages of both types of vehicles. In this article, we delve into the intricacies of one such robot, called Fthr, and its role in controlling the quadrotor’s motion during various phases of flight.
I. Introduction
Fthr is a crucial component in the control system of a quadrotor, responsible for adjusting the yaw angle based on the aerial-phase trajectory. It plays a vital role in ensuring the robot navigates smoothly and efficiently, particularly during the transition from the aerial phase to the crawling phase. Fthr is affected by various factors such as air density, battery level, propeller type, and forgetting factor, which we will explore in detail later.
II. Explanation of Key Concepts
Aerial-phase trajectory: The path traced by a quadrotor during flight.
Forgetting factor (λ): A value that determines how quickly the algorithm forgets previous estimates of Fthr, allowing for more accurate calculations.
Fthr: The proportional gain in the control system, which controls the yaw angle of the quadrotor based on the aerial-phase trajectory.
Kthr: The proportional gain in the crawling mode, which controls the speed of the propellers and prevents excessive thrust that can lead to lateral deviations or overly rapid crawling speeds.
λd (lambda d): The maximum value of Fthr during the crawling phase, which determines the maximum acceleration of the quadrotor in the yaw axis.
λp (lambda p): The maximum value of Fthr during the aerial phase, which determines the maximum acceleration of the quadrotor in the yaw axis.
Yaw angle: The angle between the direction of motion and the vertical direction.
III. Calculation of Fthr
To calculate Fthr accurately, we employ a forgetting factor recursive least-squares algorithm expressed as:
(kf)k+1 = (kf)k + Pk+1 (Fthr)k+1 (Fthr)k+1 Pk
where Pk+1 is the covariance and ρ is the forgetting factor. The algorithm iteratively refines Fthr by adjusting its value based on the error between the estimated and actual values of Fthr.
IV. Applications and Implications
By accurately calculating Fthr, we can improve the performance of our quadrotor during various phases of flight, such as transitioning from the aerial phase to the crawling phase or navigating through complex environments. Moreover, Fthr’s role in controlling yaw angle is crucial for safe and efficient operation, especially when the quadrotor faces unexpected obstacles or changes in its environment.
V. Conclusion
In conclusion, Fthr is a critical component in the control system of a quadrotor that plays a vital role in ensuring smooth navigation during various phases of flight. By understanding the factors affecting Fthr and employing appropriate calculation methods, we can enhance the overall performance of our quadrotors and expand their capabilities in various applications.
