Vision-based Guidance and Navigation for Swarm of Small Satellites in a Formation Flying Mission.
Vision-based Guidance and Navigation for Swarm of Small Satellites in a Formation Flying Mission. Small satellites, including the nanosatellite platforms called CubeSats, are suitable for formation flying missions because of their modular nature and low cost. A formation flying mission involves a set of spatially-distributed satellites capable of autonomous interaction and cooperation with one another in order to maintain the desired formation. One of the fundamental drawbacks of current guidance, navigation, and control techniques is that they rely on a centralized process, primarily using a global positioning system (GPS). While real-time positioning computed by standard GPS service is adequate for some disperse applications (i.e., constellation missions), inherent position discontinuities are not acceptable for proximate formation flying or docking missions required by high-precision science instruments like phased array antenna measurements. Therefore, a new breed of swarm navigation algorithms along with accurate state estimation methodologies need to be developed for reliable formation flying of small satellites. First, obtaining an accurate relative pose estimation for the individual CubeSats of the swarm is essential. To accomplish this task, a vision-based fiducial system, referred to as augmented reality tags, was used to provide a unique identifier within each CubeSat in the swarm. We used ArUco markers for relative pose estimation and conducted various experiments for feasibility analysis of the method in the laboratory environment for implementation in space in proximate formation flying missions where GPS positioning is unreliable. Second, a new recursive hybrid consensus filter is developed for distributed state estimation using Kalman Filtering. We demonstrated a fully operational collaborative team that can be utilized for applying any method of swarm control. The proposed approach is scalable, robust to network failure, and capable of handling a non-Gaussian transition in observation models and, therefore, is quite suitable for controlling the CubeSat swarm to accomplish a proximate formation flying mission.
Fekrmandi, Hadi & Rutan-Bedard, Skye & Frye, Alexander & Yoon, Michael & Hoover, Randy. 2019 July, 32nd Florida Conference for Recent Advances in Robotics. Lakeland, FL. Link
Page Author: Samuel Irwin, BS student Mechanical Engineering, SDSMT