Human-interpretable diagnostic information for robotic planning systems

Advances in automation have the potential to reduce the workload required for human planning and execution of missions carried out by robotic systems such as unmanned aerial vehicles (UAVs). However, automation can also result in an increase in system complexity and a corresponding decrease in system transparency, which makes identifying and reasoning about errors in mission plans more difficult. To help explain errors in robotic planning systems, we define a notion of structured probabilistic counterexamples, which provide human-interpretable diagnostic information about requirements violations resulting from complex probabilistic robotic behavior. We propose an approach for generating such counterexamples using mixed integer linear programming and demonstrate the usefulness of our approach via a case study of UAV mission planning demonstrated in the AMASE multi-UAV simulator.