5 papers
Rapidly-Iterating Grid-Based Near-Optimal Kinodynamic Motion Planning
Michael Moncton, Eric Frew
This paper develops the Rapidly-iterating kinoDynamic Grid (RDG) algorithm, an asymptotically near-optimal kinodynamic motion planning algorithm that produces high quality solution…
Achieving Asymptotic Near-Optimality Without -Similarity
Michael Moncton, Eric Frew
Sampling-based motion planning algorithms are a popular class of trajectory planning algorithm due to their speed in complex, high-dimensional environments and ability to handle ki…
Data-Driven Permissible Safe Control with Barrier Certificates
Rayan Mazouz, John Skovbekk, Frederik Baymler Mathiesen +3
This paper introduces a method of identifying a maximal set of safe strategies from data for stochastic systems with unknown dynamics using barrier certificates. The first step is…
Path Planning in 3D with Motion Primitives for Wind Energy-Harvesting Fixed-Wing Aircraft
Seung-Keol Ryu, Michael Moncton, Han-Lim Choi +1
In this work, a set of motion primitives is defined for use in an energy-aware motion planning problem. The motion primitives are defined as sequences of control inputs to a simpli…
Formal Abstraction of General Stochastic Systems via Noise Partitioning
John Skovbekk, Luca Laurenti, Eric Frew +1
Verifying the performance of safety-critical, stochastic systems with complex noise distributions is difficult. We introduce a general procedure for the finite abstraction of nonli…