Collaborative Research: Negotiated Planning for Stochastic Control of Dynamical Systems

This project focuses on the development of new computational tools and new knowledge that can be used to help ground operators of satellites manage the complexity of next generation space missions. Ground operators of spacecraft typically must balance multiple, conflicting goals, and as spacecraft missions become more complex, so will the ground operator's task of satellite coordination.

However, existing tools make it difficult for operators to obtain a complete understanding of possible trade-offs and rewards when designing paths for the satellites to follow. Further, the use of autonomy to guide satellites along desired paths can introduce further complexity, as well as uncertainty. This project supports research that is motivated by the question: How can path planning for autonomous systems operating in uncertain environments, be responsive to the human, the dynamics, and appropriate levels of risk?

Creation of a mathematical and algorithmic framework to accomplish these objectives could have broader impact on complex missions involving autonomous vehicles in other domains beyond spacecraft.

This grant supports the development of algorithms and theoretical methods to enable the human operator to seamlessly manipulate mission objectives, risks, and rewards in path planning for controlled autonomous vehicles. The research approach is premised on the notion that convex optimization provides a theoretical framework for not only stochastic motion planning and control, but also for sensitivity analysis of the risks, rewards, and constraints, to mission parameters, in large part due to its ability to provide certificates in a run-time compatible manner.

The PIs focus on the development of systematic methods and tools for 1) specification of mission objectives and constraints without the need for expert knowledge; 2) negotiation of reward parameters, risk tolerances, and constraints, between the user and the vehicle's autonomous control system; and 3) integration of these capabilities into a receding horizon framework, to enable responsiveness to unanticipated and dynamic changes to mission priorities and operator preferences. The novelty of this research is in the inclusion of data driven characterization of uncertainty into a stochastic optimal control framework; in the use of duality theory for sensitivity analysis of objectives, risks, and rewards; and in the run-time implementation of stochastic reachability and optimization algorithms within a receding horizon framework, to enable real-time operator support.

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.