Rapid trajectory planning of a reusable launch vehicle for airdrop with geographic constraints. (7th January 2019)

• Record Type: Journal Article
• Authors: Wei, Xing; Lan, Xuejing; Liu, Lei; Wang, Yongji
• Is Part Of: International journal of advanced robotic systems. Volume 16:Number 1(2019:Jan./Feb.)
• Publication Date: 2019-01-07
• Subjects: Reusable launch vehicle -- online trajectory planning -- waypoint -- no-fly zone -- airdrop -- quasi-equilibrium glide condition; Robotics -- Periodicals; Robotics; Periodicals; 629.892
• DOI: 10.1177/1729881418817971
• Languages: English
• ISSNs: 1729-8806

Abstract

Online feasible trajectory generation for an airdrop unpowered reusable launch vehicle is addressed in this article. A rapid trajectory planning algorithm is proposed to satisfy not only the multiple path and terminal constraints but also the complex geographic constraints of waypoints and no-fly zones. Firstly, the lower and upper boundaries of the bank angle that implement all the path constraints are obtained based on the quasi-equilibrium glide condition. To determine the bank angle directly, a weighted interpolation of the boundaries is then developed, which provides an effective approach to simplify the planning process as a one-parameter search problem. Subsequently, three types of lateral planning algorithms are designed to determine the sign of the bank angle according to the requirements of waypoints passage, no-fly-zones avoidance, and terminal constraints in the airdrop process, and the convergence of these methods for passing over the waypoints and meeting the terminal conditions has been clarified and formally demonstrated. Considering the constraints in the actual airdrop flight missions, the planning trajectory is divided into several subphases to facilitate the application of corresponding algorithms. Finally, the performance of the proposed algorithm is assessed through three airdrop missions of reusable launch vehicle with different geographic constraints. Besides, the effectiveness of the algorithm is demonstrated by the Monte Carlo simulation results.