.png){kind=icon}
.png){kind=icon}
Return
Collision avoidance maneuver optimization during low-thrust propelled trajectories
.png){kind=icon}
OA
AI
DOI：10.1007/s42064-024-0227-3.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Conjunctions between spacecraft are increasingly common across orbital regimes, demanding reliable and efficient collision avoidance (COLA) strategies. The typical solution to the COLA problem is to compute a maneuver that reduces the collision risk while minimizing fuel expenditure. If the spacecraft is in a continuously propelled phase, this approach must be modified since the thrust profile is determined a priori, aiming to reach a final orbit. This work proposes using convex optimization to solve the short-term encounter COLA problem in such conditions. The optimization problem is two-fold: (i) the collision risk must be reduced below a certain threshold; (ii) after the conjunction, the spacecraft must be rerouted into the nominal trajectory. By casting the problem as a sequential convex program, the original nonlinear optimal control problem is solved iteratively, recovering an optimal solution. Within the second-order cone program framework, three strategies are proposed to address the problem: (i) determining the optimal switch-off time to avoid the collision while minimizing deviation from the nominal trajectory; computing a new thrust profile, deviating as little as possible from the original one in terms of (ii) vector or (iii) angular difference. The three strategies are tested on practical operational scenarios, using the nominal thrust profile from a low-thrust geostationary transfer orbit and conjunction details from a conjunction data message.
Keywords:
collision avoidance (COLA)
low thrust
convex optimization
trajectory optimization
space traffic management
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6.5
Papers:
108
Citations:
807