Online trajectory planning and control of a MAV payload system in dynamic environments

Abstract

Micro Aerial Vehicles (MAVs) can be used for aerial transportation in remote and urban spaces where portability can be exploited to reach previously inaccessible and inhospitable spaces. Current approaches for path planning of MAV swung payload system either compute conservative minimal-swing trajectories or pre-generate agile collision-free trajectories. However, these approaches have failed to address the prospect of online re-planning in uncertain and dynamic environments, which is a prerequisite for real-world deployability. This paper describes an online method for agile and closed-loop local trajectory planning and control that relies on Non-Linear Model Predictive Control and that addresses the mentioned limitations of contemporary approaches. We integrate the controller in a full system framework, and demonstrate the algorithm’s effectiveness in simulation and in experimental studies. Results show the scalability and adaptability of our method to various dynamic setups with repeatable performance over several complex tasks that include flying through a narrow opening and avoiding moving humans.