Optimized Curvilinear Potential Field Based Multi-objective Satellite Collision Avoidance Maneuver

Continuous growth of space objects implies that new accurate techniques for automated obstacle avoidance should be investigated. The problem of collision mitigation system is classified into two main parts: data supply and automation. Data are provided by some organizations as the Space Date Association Conjunction Analysis Operations in USA and SMARTnet in Germany. While, automation phase utilizes these data to build an effective motion planning algorithm able to perform obstacle avoidance maneuver planning and implementation. The key factors before performing any maneuver in space are the fuel budget, time, and the safety of the new trajectory. This paper develops a new technique integrating genetic optimization and artificial potential field (APF), and then implements a space-based obstacle avoidance optimized technique. All APF coefficients are then optimized; consequently, the maneuver trajectory is also optimized. Moreover, the criteria for judging the collision is defined herein considering the curvilinear distance between the satellites, rather than the Euclidean distance. Consequently, the time to collision (TTC) is more accurate, and unnecessary maneuvers are avoided. The curvilinear distance is calculated using real case study is investigated via calculation of orbit dynamics of spacecraft using a high precision orbit propagation module. A real case for the collision between the Chinese “cz_4” and the United States “DMSP 5D-2 F7” satellites is presented. Results clarify the difference between the traditional potential field and the new genetic-based curvilinear artificial potential field. The impulsive velocity supplied to the propulsion control module is then compared to the results with the well-known Hohmann maneuver for changing the altitude.