Towards Blended Reactive Planning and Acting using Behavior Trees
This work addresses the challenge of blending planning and acting for robots in dynamic settings, though it appears incremental as it builds on existing Behavior Tree and planning concepts.
The paper tackles the problem of enabling robots to operate in dynamic environments by automatically generating and updating Behavior Trees using a back-chaining planning algorithm, resulting in efficient reaction to disturbances without replanning, as demonstrated in two robotics scenarios.
In this paper, we show how a planning algorithm can be used to automatically create and update a Behavior Tree (BT), controlling a robot in a dynamic environment. The planning part of the algorithm is based on the idea of back chaining. Starting from a goal condition we iteratively select actions to achieve that goal, and if those actions have unmet preconditions, they are extended with actions to achieve them in the same way. The fact that BTs are inherently modular and reactive makes the proposed solution blend acting and planning in a way that enables the robot to efficiently react to external disturbances. If an external agent undoes an action the robot reexecutes it without re-planning, and if an external agent helps the robot, it skips the corresponding actions, again without replanning. We illustrate our approach in two different robotics scenarios.