Perception-Driven Optimal Motion Planning Under Resource Constraints

Thomas Sayre-McCord, Ph.D., 2019
Sertac Karaman, Advisor

Over the past few years there has been a new wave of interest in fully autonomous robots operating in the real world. These robots are expected to operate at high speeds in unknown, unstructured environments using only onboard sensing and computation, presenting significant challenges for high performance autonomous navigation.

To enable research in these challenging scenarios, the first part of this thesis focuses on the development of a custom high-performance research UAV capable of high speed autonomous flight using only vision and inertial sensors. While this platform is capable of high performance state estimation and control, its capabilities in unknown environments are severely limited by the computational costs of vision-based mapping and motion planning algorithms.

Motivated by these challenges, the second part of this thesis presents an algorithmic approach to the problem of motion planning in an unknown environment. We show that the algorithm produces globally optimal motion plans, matching the optimal solution for the case with the full (unprocessed) sensor data, while only processing a subset of the data. The mapping and motion planning algorithm is demonstrated on a number of test systems, with a particular focus on a six-dimensional thrust limited model of a quadrotor.