In this study, the conjunction situation when a number of explosion fragments approach four cluster satellites was simulated, and an optimum avoidance maneuver plan that mitigates this risk was established. The orbits of four deputy satellites around a virtual chief satellite were defined using the Hill–Clohessy–Wiltshire equation, and NASA’s breakup model was applied to similar altitude rocket body to simulate explosions situation. The distribution of 230 explosion fragments after an explosion was simulated, and the size and direction of the Delta-V were applied to each fragment. In this situation, the collision avoidance maneuver planning strategy that uses heuristic algorithm was proposed to establish efficient avoidance maneuver plan for approaching fragments after the explosion. Avoidance maneuver plans using four heuristic algorithms were established to minimize the fuel consumption of the four cluster satellites within several constraints including conjunction risk, ground track and formation between cluster satellites. Eight simulations were performed with different combinations of the four heuristic algorithms and different generations and populations. As a result, all avoidance maneuver plan satisfied conjunction risk constraint as well as the other complex constraints. In particular, particle swarm optimization–gravitational search algorithm established the most efficient maneuver plan with the least fuel consumption and fastest convergence speed.
[1]
Hae-Dong Kim,et al.
Collision avoidance maneuvers for multiple threatening objects using heuristic algorithms
,
2015
.
[2]
Maurice Martin,et al.
TechSat 21 and Revolutionizing Space Missions using Microsatellites
,
2001
.
[3]
Ryan E. Sherrill.
Dynamics and Control of Satellite Relative Motion in Elliptic Orbits using Lyapunov-Floquet Theory
,
2013
.
[4]
David E. Goldberg,et al.
Genetic algorithms and Machine Learning
,
1988,
Machine Learning.
[5]
Hae-Dong Kim,et al.
A Study on the Collision Avoidance Maneuver Optimization with Multiple Space Debris
,
2012
.
[6]
Rainer Storn,et al.
Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces
,
1997,
J. Glob. Optim..
[7]
N. Johnson,et al.
NASA's new breakup model of evolve 4.0
,
2001
.