Autonomous Parallelization of Resource-Aware Robotic Task Nodes

Abstract

Robot task programming often leads to inefficient plans, as opportunities for parallelization and precomputation are usually not exploited by the programmer. This inefficiency is often especially obvious in mobile manipulation, where path planning and pose estimation algorithms are time-consuming operations. In this paper, we introduce the concept of Resource-Aware Task Nodes (RATNs), a powerful descriptive action model for robots. Next, we propose an algorithm that executes so-called Concurrent Dataflow Task Networks (CDTNs), robot plans consisting of RATNs. It optimizes programmed plans based on two sources of information: 1)~The control flow represented in the original task plan, whose constraints are relaxed to generate opportunities for parallelization and precomputation. 2)~Dependencies between actions pertaining to resources, data flows and world model changes, the latter being equivalent to preconditions and effects. CDTNs have been integrated in our open-source task programming framework RAFCON, and we show that it leads to 11-29\% improvement in terms of execution time in two simulated mobile manipulation scenarios.