Task Management for Autonomous Flights of Micro Aerial Vehicles: A Behavior Tree Approach

This paper proposes a task scheduling framework for autonomous navigation of micro aerial vehicles (MAVs) in unknown environments. Currently, the dominant approach for task scheduling in MAV systems typically relies on the finite state machine (FSM), which presents limitations in scaling the system functionality due to the coupled relationship between modules. We propose a generic task scheduling framework for MAVs based on behavior trees (BTs) to address this challenge. A blackboard is built as a state data management center, decoupling different modules of the MAV so that the various functional modules can operate independently. In addition, we set up standardized interfaces for the modules, thus the behavior tree can quickly connect the modules. This framework enables MAVs to perform autonomous flight scheduling tasks and supports the rapid expansion of system functions. Finally, we validated the effectiveness of the framework with real-world experiments on a customized MAV.