Recovery Methods for Client-Service Based Action Entropy Active Sensing

Action entropy active sensing represents huge leaps forward in the field of active sensing.Previous papers have found that while action entropy increases the accuracy of thesimulation, it also increases the time required to calculate sensing actions. The time costcan be reduced by implementing parallel processing that splits the computations overmultiple cores. This can be done on a local machine and via implementation of the cloud.This paper focuses on implementing a detection and recovery mechanism for a potentialcloud crash. The communication is facilitated through Docker and OpenVPN, using theROS client-service architecture. This paper adjusts the methodology of the existingmodel to make it more robust to failures. The simulation results indicate that the time torecovery is feasible, and that this new structure increases the reliability of the existingmodel