Constructing Behavior Trees from Temporal Plans for Robotic Applications

TL;DR

This paper presents a systematic method to convert temporal plans into behavior trees for robotic execution, enabling flexible, concurrent, and real-time plan execution in uncertain environments.

Contribution

It introduces algorithms to transform plans into simple temporal networks and then into behavior trees, facilitating robust robot plan execution.

Findings

Validated approach on real robots within the PlanSys2 framework.

Ensures correct execution of concurrent and time-triggered plans.

Supports flexible, state-triggered plan execution in real-world scenarios.

Abstract

Executing temporal plans in the real and open world requires adapting to uncertainty both in the environment and in the plan actions. A plan executor must therefore be flexible to dispatch actions based on the actual execution conditions. In general, this involves considering both event and time-based constraints between the actions in the plan. A simple temporal network (STN) is a convenient framework for specifying the constraints between actions in the plan. Likewise, a behavior tree (BT) is a convenient framework for controlling the execution flow of the actions in the plan. The principle contributions of this paper are i) an algorithm for transforming a plan into an STN, and ii) an algorithm for transforming an STN into a BT. When combined, these algorithms define a systematic approach for executing total-order (time-triggered) plans in robots operating in the real world. Our approach is based on creating a graph describing a deordered (state-triggered) plan and then creating a BT representing a partial-order (determined at runtime) plan. This approach ensures the correct execution of plans, including those with required concurrency. We demonstrate the validity of our approach within the PlanSys2 framework on real robots.