Autonomous task planning and acting for micro aerial vehicles

Abstract

Micro aerial vehicles (MAVs) are ideal platforms for various aerial applications such as search and rescue, building inspection and parcel delivery, due to their superior mobility. It is natural and meaningful to investigate on how the MAVs can be employed to autonomously carry out complicated tasks in real applications. In this paper, we present a task planning and acting framework to extend the autonomy level of MAVs further. We use linear temporal logic (LTL) to formally specify the task requirements and solve the reactive synthesis problem through online iterative planning with a determinized system model. Moreover, special attention is given to the interaction between the task and motion levels, where we use behavior tree as an intermediate interface to execute the plan, feedback the execution outcome and facilitate the high-level task re-planning process. The proposed framework has been tested with a high-fidelity simulation using an actual quadrotor dynamics model.