Developing a System for Robust Planning using Linear Temporal Logic

Sammanfattning: 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. Thissaves time and avoids unnecessary risks for the human agents. One possible configuration of such arescue team is, a human operator instructing a unmanned aerial vehicle (UAV) via speech-commandshow to traverse through an environment to investigate areas of interest. A first step to address thisproblem is presented in this master thesis by developing a framework for mapping temporal logicinstructions to physical motion of a UAV.The fact that natural language has a strong resemblance to the logic formalism of Linear-TemporalLogic (LTL) is exploited. Constraints expressed as an LTL-formula are imposed on a provided labeledmap 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 ispresented. The input-skeleton and the map-converter are then combined with a cost-map-based pathplanning algorithm in order to obtain solution sets. A clarification request is created such that theoperator can choose which solution set should be executed. The proposed framework is successivelyvalidated, first by MATLAB-experiments to ensure the validity of the cost-map-creation followed bysimulation experiments in ROS incorporating the entire framework. Finally, a real-world experimentis performed at the SML to validate the proposed framework.