Abstract: Decision making with imperfect knowledge is an essential capability for unmanned vehicles operating in populated, dynamic domains. For example, a UAV flying autonomously indoors will not be able to rely on GPS for position estimation, but instead use on-board sensors to track its position and map the obstacles in its environment. The planned trajectories for such a vehicle must therefore incorporate sensor limitations to avoid collisions and to ensure accurate state estimation for stable flight — that is, the planner must be be able to predict and avoid uncertainty in the state, in the dynamics and in the model of the world. Incorporating uncertainty requires planning in information space, which leads to substantial computational cost but allows our unmanned vehicles to plan deliberate sensing actions that can not only improve the state estimate, but even improve the vehicle’s model of the world and how people interact with the vehicle.
I will discuss recent results from my group in planning in information space; our algorithms allow robots to generate plans that are robust to state and model uncertainty, while planning to learn more about the world. I will describe the navigation system for a quadrotor helicopter flying autonomously without GPS using laser range-finding, and will show how these results extend to autonomous mapping and human-robot interaction.