Kyle Hollins Wray

Log-space harmonic function path planning

We propose a log-space solution for robotic path planning with harmonic functions that solves the long-standing numerical precision problem. We prove that this algorithm: (1) performs the correct computations in log-space, (2) returns the true equivalent path using the log-space mapping, and (3) has a strong error bound given its convergence criterion. We evaluate the algorithm on 7 problem domains. A Graphics Processing Unit (GPU) implementation is also shown to greatly improve performance. We also provide an open source library entitled epic with extensive ROS support and demonstrate this method on a real humanoid robot: the uBot-6. Experiments demonstrate that the log-space solution rapidly produces smooth obstacle-avoiding trajectories, and supports planning in exponentially larger real-world robotic applications.

Online decision-making for scalable autonomous systems

We present a general formal model called MODIA that can tackle a central challenge for autonomous vehicles (AVs), namely the ability to interact with an unspecified, large number of world entities. In MODIA, a collection of possible decision-problems (DPs), known a priori, are instantiated online and executed as decision-components (DCs), unknown a priori. To combine the individual action recommendations of the DCs into a single action, we propose the lexicographic executor action function (LEAF) mechanism. We analyze the complexity of MODIA and establish LEAFs relation to regret minimization. Finally, we implement MODIA and LEAF using collections of partially observable Markov decision process (POMDP) DPs, and use them for complex AV intersection decision-making. We evaluate the approach in six scenarios within a realistic vehicle simulator and present its use on an AV prototype.