6 citations · 6 across the 4 of their papers we have counts for
4 papers · 1 filter
Temporally-Sampled Efficiently Adaptive State Lattices for Autonomous Ground Robot Navigation in Partially Observed Environments
Ashwin Satish Menon, Eric R. Damm, Eli S. Lancaster +3
Due to sensor limitations, environments that off-road mobile robots operate in are often only partially observable. As the robots move throughout the environment and towards their…
Kinodynamic Motion Planning for Mobile Robot Navigation across Inconsistent World Models
Eric R. Damm, Thomas M. Howard
Mobile ground robots lacking prior knowledge of an environment must rely on sensor data to develop a model of their surroundings. In these scenarios, consistent identification of o…
Incorporating Stochastic Models of Controller Behavior into Kinodynamic Efficiently Adaptive State Lattices for Mobile Robot Motion Planning in Off-Road Environments
Eric R. Damm, Eli S. Lancaster, Felix A. Sanchez +3
Mobile robot motion planners rely on theoretical models to predict how the robot will move through the world. However, when deployed on a physical robot, these models are subject t…
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 +3
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 r…