Over the last three decades we have witnessed historic missions to Mars where unmanned space vehicles successfully landed on and explored the Martian surface in search of evidence of past life. Recently reusable rockets have captured the public's imagination by delivering payloads to orbit and then landing safely back on Earth. A common requirement for these space vehicles is that they must be operated autonomously during the atmospheric entry, descent, and landing (EDL). Furthermore, the first time they are ever tested as a fully integrated system is during the actual mission. This makes EDL extremely challenging and risky. A key technology that has enabled these recent successful space missions is the onboard software that controls the vehicle's motion during EDL, which must work properly under all expected variations in the mission conditions. Motivated by these effective point-design solutions from aerospace engineering, our research aims to develop a unified algorithmic framework for motion planning and control for a large class of Earth-based autonomous vehicles that operate in challenging environments with increasingly complex performance requirements. Applications include autonomous aerial, ground, and underwater vehicles serving many safety critical tasks in, for example, search and rescue, disaster relief, terrain mapping and monitoring, and toxic spill cleanup applications to name few.
Our main hypothesis is that optimization-based motion planning and control provides an effective and unifying mathematical framework that is able to handle the autonomy problems encountered in space applications and this framework can be generalized to a large variety of autonomous vehicles. Our project aims to build this optimization-based framework by leveraging invaluable insights and experiences from NASA's flagship missions to Mars. These missions had to succeed during their first attempt and any failure would have led to catastrophic results, i.e., there was no margin for error. Hence Mars landing can be considered a prototypical benchmark problem, as it encompasses complexities that one would also face with other (Earth-based) autonomous vehicles: switching between a variety of operational modes; limited fuel, power, and mission time; state and control constraints; and uncertainties in the situational awareness, sensing, actuation, vehicle dynamics, and environment. Our project aims to provide algorithmic foundations for optimization-based motion planning and control. It has both a theoretical component to produce fundamental results that can be used to build trustworthy algorithms and a comprehensive experimental component to produce the empirical evidence necessary to evaluate these algorithms on real-world examples, i.e., autonomous quad-rotors and underwater vehicles. Our research team is assembled to build on these lessons learned in space applications and to develop optimization-based planning and control methods that can seamlessly be transitioned to practice.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
