Max Robotics — Global Service Robot Directory + US Certification

About

Siavash Tavana is a rising star in the field of autonomous space robotics, whose work is critical to the future of on-orbit servicing and space exploration. His research focuses on solving the complex motion planning and control problems required for autonomous spacecraft to perform intricate tasks in close proximity to other structures. Tavana’s major contributions include developing novel optimization-based and reinforcement learning strategies for full-coverage inspection of large space structures and autonomous on-orbit assembly. His most cited work, a 2022 paper on multi-spacecraft coordination for inspection, has garnered 27 citations, establishing a foundation for efficient, safe, and complete observation of space assets. He has also pioneered a non-conservative collision avoidance technique using convex optimization, addressing a key challenge in non-convex motion planning. More recently, Tavana has advanced the field by applying reinforcement learning to create continuation strategies for solving the highly sensitive optimal control problems inherent in autonomous assembly. His 2024 work on optimal pose design for close-proximity inspection further underscores his focus on enabling the critical technologies needed for long-term space colonization and complex orbital operations.