A Fast Method for Explanations of Failures in Optimization-Based Robot Motion Planning

The transparent interaction between an operator and a robot system is essential for successful task completion. This requires a mutual understanding of decisions and processes in order to provide accurate diagnoses and troubleshooting alternatives in the event of a failure. Due to inaccuracies in the environmental perception or planner configuration, errors can occur in robot motion planning that are hard to understand by the operator. In this work we present a method that is able to provide explanations for motion planning failures quickly. In the context of optimization-based planners, failures origin from planning constraints can be identified using an adaption of the Fast Diag algorithm. It is able to provide one preferred minimal diagnosis in logarithmic time, also for large constraint sets. To evaluate the applicability of the proposed method, experiments are conducted that compare the computational performance to an existing method while considering different parameters such as number of constraints and requested diagnoses.