What are the current and future trends and technologies for autonomous docking and AI-based docking systems?

1 Challenges and requirements

Docking in space is an intricate and hazardous operation that requires exact navigation, guidance, control, and coordination of multiple spacecraft. Autonomous docking and AI-based docking systems must be robust and reliable enough to respond to uncertainties, disturbances, failures, and emergencies. They must also be adjustable and expandable to accommodate various docking scenarios, mission profiles, relative motion dynamics, docking ports, and environmental conditions. Interoperability and compatibility with other spacecraft, platforms, and standards such as the International Docking System Standard (IDSS) or the Docking System Interface Control Document (DS-ICD) are also necessary. Finally, these systems must be able to guarantee the safety of the crew, the spacecraft, and the target by avoiding collisions, damage, or unauthorized access.

2 Current technologies and systems

Various space agencies and companies, such as NASA, ESA, Roscosmos, SpaceX, Boeing, and others have developed and demonstrated several autonomous docking and AI-based docking systems. These systems incorporate a range of technologies and systems, including sensor fusion, vision-based navigation, control algorithms, and artificial intelligence. Sensor fusion uses multiple sensors such as cameras, lidars, radars, GPS or star trackers to obtain accurate information about the relative position, orientation, velocity and distance of the spacecraft and the target. Vision-based navigation uses computer vision techniques such as feature extraction, matching, tracking or pose estimation to recognize and align the docking ports or markers on the spacecraft and the target. Control algorithms use proportional-integral-derivative (PID), model predictive control (MPC) or fuzzy logic to generate and execute the optimal docking trajectory and maneuvers. Artificial intelligence techniques such as machine learning, deep learning or reinforcement learning are used to enhance the performance, robustness and adaptability of the systems by learning from data, experience or feedback.

3 Future trends and opportunities

Autonomous docking and AI-based docking systems have many potential applications and benefits for the future of space exploration and commercialization. On-orbit servicing and assembly, space tourism and transportation, asteroid mining and exploration, and space security and defense are just a few of the trends and opportunities that these systems can enable. For example, they can facilitate the servicing, repair, refueling, or upgrading of satellites, enable transportation of passengers and cargo between Earth and orbit, enable exploration and exploitation of asteroids, or enhance the security and defense of space assets.

4 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?