Rationally Inattentive Path-Planning via RRT*
This addresses path-planning under stochastic disturbances for robotics applications, representing an incremental improvement.
The paper tackles path-planning for mobile robots in uncertain environments by proposing a novel path length metric that combines Euclidean distance and perception cost, integrated into the RRT* algorithm, with numerical studies demonstrating its utility.
We consider a path-planning scenario for a mobile robot traveling in a configuration space with obstacles under the presence of stochastic disturbances. A novel path length metric is proposed on the uncertain configuration space and then integrated with the existing RRT* algorithm. The metric is a weighted sum of two terms which capture both the Euclidean distance traveled by the robot and the perception cost, i.e., the amount of information the robot must perceive about the environment to follow the path safely. The continuity of the path length function with respect to the topology of the total variation metric is shown and the optimality of the Rationally Inattentive RRT* algorithm is discussed. Three numerical studies are presented which display the utility of the new algorithm.