Using metareasoning to improve autonomous robot planning

Abstract

An autonomous robot's onboard computational resources are limited due to size, weight, and power limits. Thus, optimizing the use of those resources is essential so that the robot can complete its mission reliably and efficiently. Metareasoning, a branch of artificial intelligence, enables a robot to monitor and control its perception, mapping, planning, and other reasoning processes in response to changes in the robot and its environment. Metareasoning is implemented in a meta-level that is logically separate from the object level (which performs the reasoning processes). Previous work has developed metareasoning approaches for specific robotic systems, but these are not easy to generalize. This paper describes our implementation of a metareasoning approach in the Army Research Laboratory (ARL) ground autonomy stack, which is deployable on a variety of robotic platforms. This paper describes the general approach and our implementation of a metareasoning node that can switch the global and local path planners when a planning failure occurs. The results of simulated experiments show that adding metareasoning increases the likelihood of mission success in some cases. More research is needed to optimize the metareasoning approach. Ultimately comprehensive metareasoning that can control the most important aspects of object level reasoning will enable an autonomous robot to deploy its limited computational resources more effectively and complete its mission more reliably.