Eric R. Damm — accepted AI-conference papers

2025

Homotopy-Aware Efficiently Adaptive State Lattices for Mobile Robot Motion Planning in Cluttered Environments

Ashwin Menon, Eric R. Damm, Thomas M. Howard

RA-L 2025

Mobile robot navigation architectures that employ a planning algorithm to provide a single optimal path to follow are flawed in the presence of unstructured, rapidly changing environments. As the environment updates, optimal plans often oscillate around discrete obstacles, which is problematic for p

Cited by 4 Source ↗Scholar ↗

2023

Terrain-Aware Kinodynamic Planning with Efficiently Adaptive State Lattices for Mobile Robot Navigation in Off-Road Environments

Eric R. Damm, Jason M. Gregory, Eli S. Lancaster, Felix A. Sanchez, Daniel M. Sahu, Thomas M. Howard

IROS 2023

To safely traverse non-flat terrain, robots must account for the influence of terrain shape in their planned motions. Terrain-aware motion planners use an estimate of the vehicle roll and pitch as a function of pose, vehicle suspension, and ground elevation map to weigh the cost of edges in the sear

Cited by 11 Source ↗Scholar ↗