A Semantic Framework for Modeling Adaptive Autonomy in Task Allocation in Robotic Fleets

摘要

Fundamental problem in human-robot teams is to find a set of heterogeneous robots that have to cooperate to execute a complex mission. This paper describes the Shared Knowledge Interaction Modelling (SKIM) framework for task allocation and how it is used to: 1) evaluate the performance of finding a set of robots to execute a certain task, and 2) model shared knowledge as a basis for adaptive autonomy in mixed human-robot teams. The shared knowledge is described by means of two ontologies: SKIM Resource Ontology (SKIM-RO) and SKIM Coordination Ontology (SKIM-CO). SKIM-RO describes resources, including robot capabilities and task requirements, and SKIM-CO describes coordination constraints for robot-robot and robot-human interactions. SKIM-CO is a basis for reasoning which enables the task allocation, and captures the concept of adaptive autonomy. This paper illustrates how framework is used to model and evaluate performance of task allocation in three use cases with the different level of task complexity. The results indicate how adaptive autonomy and shared knowledge improve performance in task allocation in complex missions.