Probabilistic Allocation of Specialized Robots on Targets Detected Using Deep Learning Networks

Abstract

Task allocation for specialized unmanned robotic agents is addressed in this paper. Based on the assumptions that each individual robotic agent possesses specialized capabilities and that targets representing the tasks to be performed in the surrounding environment impose specific requirements, the proposed approach computes task-agent fitting probabilities to efficiently match the available robotic agents with the detected targets. The framework is supported by a deep learning method with an object instance segmentation capability, Mask R-CNN, that is adapted to provide target object recognition and localization estimates from vision sensors mounted on the robotic agents. Experimental validation, for indoor search-and-rescue (SAR) scenarios, is conducted and results demonstrate the reliability and efficiency of the proposed approach.