On the Use of the Observability Gramian for Partially Observed Robotic Path Planning Problems

01/30/2018

∙

by Mohammadhussein Rafieisakhaei, et al.

∙

Optimizing measures of the observability Gramian as a surrogate for the estimation performance may provide irrelevant or misleading trajectories for planning under observation uncertainty.

READ FULL TEXT

On the Use of the Observability Gramian for Partially Observed Robotic Path Planning Problems

Path Planning Games

Solving a Path Planning Problem in a Partially Known Environment using a Swarm Algorithm

Path Planning Under Uncertainty to Localize mmWave Sources

Waterberry Farms: A Novel Benchmark For Informative Path Planning

Sensor-based Planning and Control for Robotic Systems: Introducing Clarity and Perceivability

Incremental Adversarial Learning for Optimal Path Planning

JobComposer: Career Path Optimization via Multicriteria Utility Learning

On the Use of the Observability Gramian for Partially Observed Robotic Path Planning Problems

Related Research

Path Planning Games

Solving a Path Planning Problem in a Partially Known Environment using a Swarm Algorithm

Path Planning Under Uncertainty to Localize mmWave Sources

Waterberry Farms: A Novel Benchmark For Informative Path Planning

Sensor-based Planning and Control for Robotic Systems: Introducing Clarity and Perceivability

Incremental Adversarial Learning for Optimal Path Planning

JobComposer: Career Path Optimization via Multicriteria Utility Learning