6 papers · 1 filter
SCRAMPPI: Efficient Contingency Planning for Mobile Robot Navigation via Hamilton-Jacobi Reachability
Raj Harshit Srirangam, Leonard Jung, Rohith Poola +1
Autonomous robots commonly aim to complete a nominal behavior while minimizing a cost; this leaves them vulnerable to failure or unplanned scenarios, where a backup or contingency…
Practical and Performant Enhancements for Maximization of Algebraic Connectivity
Leonard Jung, Alan Papalia, Kevin Doherty +1
Long-term state estimation over graphs remains challenging as current graph estimation methods scale poorly on large, long-term graphs. To address this, our work advances a current…
Learning Smooth State-Dependent Traversability from Dense Point Clouds
Zihao Dong, Alan Papalia, Leonard Jung +4
A key open challenge in off-road autonomy is that the traversability of terrain often depends on the vehicle's state. In particular, some obstacles are only traversable from some o…
Verification of Visual Controllers via Compositional Geometric Transformations
Alexander Estornell, Leonard Jung, Michael Everett
Perception-based neural network controllers are increasingly used in autonomous systems that rely on visual inputs to operate in the real world. Ensuring the safety of such systems…
Contingency Constrained Planning with MPPI within MPPI
Leonard Jung, Alexander Estornell, Michael Everett
For safety, autonomous systems must be able to consider sudden changes and enact contingency plans appropriately. State-of-the-art methods currently find trajectories that balance…
LiDAR Inertial Odometry And Mapping Using Learned Registration-Relevant Features
Zihao Dong, Jeff Pflueger, Leonard Jung +5
SLAM is an important capability for many autonomous systems, and modern LiDAR-based methods offer promising performance. However, for long duration missions, existing works that ei…