Payload-Mass-Aware Trajectory Planning on Multi-User Autonomous Unmanned Aerial Vehicles

Payload-Mass-Aware Trajectory Planning on Multi-User Autonomous Unmanned Aerial Vehicles

Future unmanned aerial vehicles (drones) will be shared by multiple users andwill have to operate in conditions where their fully-autonomous function isrequired. Calculation of a drones trajectory will be important but optimaltrajectories cannot be calculated unless mass and flight speed are take…

arXiv.orgVasileios Tsoutsouras

Abstract

Future unmanned aerial vehicles (drones) will be shared by multiple users and will have to operate in conditions where their fully-autonomous function is required. Calculation of a drones trajectory will be important but optimal trajectories cannot be calculated unless mass and flight speed are taken into account. This article presents the case for on-drone trajectory planning in a multi-user dynamic payload mass scenario, allowing a drone to calculate its trajectory with no need for ground control communication. We formulate and investigate on-drone trajectory planning under variable payload mass and flight speed awareness, in cases where it is shared by multiple users or applications. We present efficient solutions using a combination of heuristic and optimization algorithms. To support this investigation, we present a new model for the power dissipation of drone propulsion as a function of speed and payload mass. We evaluate our proposed algorithmic solution on contemporary embedded processors and demonstrate its capability to generate near-optimal trajectories with limited computational overhead (less than 300 milliseconds on an ARM Cortex-A9 SoC).

Cite as:

Tsoutsouras, Vasileios et al. “Payload-Mass-Aware Trajectory Planning on Multi-User Autonomous Unmanned Aerial Vehicles.” ArXiv abs/2001.02531 (2020).

BibTeX:

@misc{tsoutsouras2020payloadmassaware,
    title={Payload-Mass-Aware Trajectory Planning on Multi-User Autonomous Unmanned Aerial Vehicles},
    author={Vasileios Tsoutsouras and Joseph Story and Phillip Stanley-Marbell},
    year={2020},
    eprint={2001.02531},
    archivePrefix={arXiv},
    primaryClass={eess.SY}
}