Autonomous Planning Algorithm for Satellite Laser Ranging Tasks Based on Rolling Horizon Optimization Framework

Abstract

Observation task planning is a key issue and the first step in the development of automated Satellite Laser Ranging (SLR) systems. Aiming at the problem of dynamic change of cloud cover during SLR operation, this paper proposes an autonomous mission planning algorithm for SLR based on the Rolling Horizon Optimization (RHO) framework. A hybrid event- and cycle-driven replanning mechanism is adopted, and four functional modules, rolling, planning, information acquisition and decision-making, are established to decompose the SLR observation task planning process into a series of static planning intervals. An improved ant colony algorithm is proposed and utilized to realize the autonomous planning of SLR system observation tasks, and the above autonomous planning algorithm is verified and analyzed based on the SLR system at station 7237. The results show that the above algorithm can effectively increase the number of observation satellites and revenue under cloud disturbance, solve the problems of low efficiency and poor interference resistance of conventional static algorithms, and provide a new research idea for the establishment of an unattended SLR system.