Online Trajectory Planning and Control of a MAV Payload System in Dynamic Environments

Abstract

Micro Aerial Vehicles (MAVs) are increasingly being used for aerial transportation in remote and urban spaces where portability can be exploited to reach previously inaccessible and inhospitable spaces. Current approaches to MAV swung payload system path planning have primarily focused on pre-generating (agile) collision-free, or conservative minimal-swing trajectories in static environments. 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 article describes a novel Non-Linear Model Predictive Controller (NMPC) for online, agile and closed-loop local trajectory planning and control addressing the limitations mentioned of contemporary approaches. We integrate the controller in a full system framework and demonstrate the algorithm’s effectiveness in simulation and experimental studies. Results show the scalability and adaptability of our method to various dynamic setups with repeatable performance over several complex tasks which include flying through a narrow opening and avoiding moving humans.