Extending Behavior Trees with Market-Based Task Allocation in Dynamic Environments

Abstract

Behavior tree (BT) is a novel control architecture in the robotic field. Being modular and reactive, BTs show great advantages in autonomous systems when applied to robot control. However, there are unsolved problems on the use of BTs in multi-robot scenarios include but are not limited to task allocation and robot coordination. In this work, we propose a market-based behavior tree method for task perception, allocation, and execution. This method introduces an auction mechanism to adjust commitments between different robots as the change of environment and extends BTs into multi-robot systems. We validate the proposed method quantitatively and qualitatively by simulation with Gazebo.