Roaming with Robots: Utilizing Artificial Curiosity in Global Path Planning for Autonomous Mobile Robots

Autonomous Mobile Robots are used with increasing frequency in inspection and maintenance tasks completing fixed goal sequences. The downtime robots experience between goals offers an opportunity to gather additional environment information instead of resting. Uncertainty in the amount of downtime available rules out the definition of a pre-determined schedule set by an external operator. Instead, the robot itself should decide dynamically, what information it should gather before its next task begins. This results in a multi-objective optimization problem trying to maximize information gain while utilizing as much of the available time as possible. We propose a genetic algorithm to solve the presented optimization problem and introduce two different models for artificial curiosity used inside the fitness function for gathering as much information as possible. For planning the genetic algorithm utilizes a multi-map approach using information and obstacle maps. We evaluated our models in a pre-defined and pre-mapped Gazebo environment with a given information map and evaluated their performance against an information-agnostic coverage algorithm. In this work, we show that utilizing artificial curiosity in path planning can result in major information gains by effectively using downtime.