Target Selection for a Space-Energy Driven Laser-Ablation Debris Removal System Based on Ant Colony Optimization

Abstract

The space-energy driven laser-ablation debris removal technology can remove or detach multiple centimeter-level space debris in a single mission. However, the space-energy driven platform can only rely on its own equipment capabilities to detect and identify space debris. It is necessary to select multiple potentially removable debris targets to improve the removal efficiency. In this paper, target selection for a space-energy driven laser-ablation debris removal system is analyzed based on ant colony optimization. The intersection and interaction periods were given by the optimal driving sequence calculation for multiple debris. Parameters such as the detection range, pulsed energy, repetition frequency of the laser and trajectory of debris have been considered as inputs of the simulation. Target selection and optimal action time have been calculated when a single debris entered the detection range of the laser system. This optimization can significantly improve the overall efficiency and laser energy utilization of the space-based laser platform for the same randomly generated debris group, compared to the mode driven sequentially according to the order of entering the laser action range. The results showed that after being filtered by the ant colony algorithm, the number of removable debris doubled, and the de-orbit altitude increased by 15.9%. The energy utilization rate of the laser removal system has been improved by 74.6%. This optimization algorithm can significantly improve the overall work efficiency and laser energy utilization rate of the space-energy driven system. It can remove more debris or have a larger effective orbit reduction distance value for all debris.

Path Planning for the Rapid Reconfiguration of a Multi-Robot Formation Using an Integrated Algorithm

“…Metaheuristic algorithms, such as the genetic algorithm [18][19][20], annealing algorithm [21], tabu search algorithm [22], particle swarm optimization algorithm [23], and ant colony algorithm [24][25][26], have gained popularity in recent years for solving complex problems that cannot be solved by traditional methods [27][28][29]. Consequently, many researchers have utilized these metaheuristic global optimization algorithms to address multi-objective path planning.…”