Developing a System for Robust Planning using Linear Temporal Logic

Abstract: Human robot-collaborative search missions have gotten more and more attention in recent years. Especially in scenarios where the robot first scouts the scene before sending in human agents. This saves time and avoids unnecessary risks for the human agents. One possible configuration of such a rescue team is, a human operator instructing a UAV via speech-commands how to traverse through an environment to investigate areas of interest. A first step to address this problem is presented in this master thesis by developing a framework for mapping temporal logic instructions to physical motion of a UAV. The fact that natural language has a strong resemblance to the logic formalism of Linear-Temporal Logic (LTL) is exploited. Constraints expressed as an LTL-formula are imposed on a provided labeled map of the environment. An LTL-to-cost-map converter including a standard input-skeleton is developed. Respective cost maps are obtained and a satisfaction-measure of fulfilling these constraints is presented. The input-skeleton and the map-converter are then combined with a cost-map-based path planning algorithm in order to obtain solution sets. A clarification request is created such that the operator can choose which solution set should be executed. The proposed framework is successively validated, first by MATLAB-experiments to ensure the validity of the cost-map-creation followed by simulation experiments in ROS incorporating the entire framework. Finally, a real-world experiment is performed at the SML to validate the proposed framework.

Examiner: Dimos Dimarogonas