Model-free Online Motion Adaptation for Optimal Range and Endurance of Multicopters

Tagliabue, Andrea; Wu, Xiangyu; Mueller, Mark W.

doi:10.1109/icra.2019.8793708

Cited by 23 publications

(24 citation statements)

References 18 publications

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“…This is of particular importance for multicopters, as a primary constraint to their widespread use is limitations in range and payload due to modest energy budgets. Examples exploring this include Ware and Roy (2016) and Tagliabue et al (2019). Trajectories for multiple vehicles operating simultaneously are also challenging, due to increasing size of the state space and mutual collision avoidance; for large collections of vehicles, this remains an open problem.…”

Section: Summary and Future Directionsmentioning

confidence: 99%

Trajectory Generation for Aerial Multicopters

Mueller¹,

D’Andrea²

2019

Encyclopedia of Systems and Control

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The ability to generate feasible and safe trajectories is crucial for autonomous multicopter systems. These trajectories can ideally be generated on low-cost, embedded computational hardware and exploit the system's full dynamic capabilities while satisfying constraints. As operations increasingly focus on operation at high speeds, or in dynamically changing environments, strategies are required that can rapidly plan and replan trajectories. This entry reviews typical approaches for trajectory generation of aerial robots, with a focus on multicopters, and discusses various approximations that may be used to make the problem more tractable. The strategy of planning in higher derivatives of the vehicle position (such as acceleration, jerk, and snap) is discussed in depth. We also discuss the related issue of expressing system limitations and constraints in these derivatives. Finally, possible future directions are discussed.

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Section: Summary and Future Directionsmentioning

confidence: 99%

Trajectory Generation for Aerial Multicopters

Mueller¹,

D’Andrea²

2019

Encyclopedia of Systems and Control

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“…However, in both cases, simple cost functions were minimized, and in [29] a linear quadrotor model was considered. In [30], the authors presented a model-free, adaptive approach based on extremum seeking control, to find the velocity that maximizes the total flight time or flight distance of a quadrotor moving along a given path. This approach was extended in [31], where a new multivariable extremum seeking controller with adaptive step size is considered, and both the speed and the heading angle of the aerial robot are optimized.…”

Section: Introductionmentioning

confidence: 99%

Practical and Accurate Generation of Energy-Optimal Trajectories for a Planar Quadrotor

Morbidi

Pisarski

2021

2021 IEEE International Conference on Robotics and Automation (ICRA)

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Motivated by the limited flight time of batterypowered multi-rotor UAVs, in this paper we address the problem of generating energy-optimal trajectories for a planar quadrotor. More specifically, by considering an accurate electrical model for the brushless DC motors and rest-to-rest maneuvers between two predefined boundary states, we explicitly compute the minimum-energy curves by adopting a free and a fixed end-time optimal control formulation. The numerical solution of these optimal control problems hinges upon a simple yet effective indirect projected gradient method. Simulation experiments illustrate the theory in a variety of realistic flight scenarios.

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“…pesticide or fertilizer at farms) often requires re-identification of parameters in the power consumption model, which is time-consuming. These effects motivated us to propose, in our previous work [22], a modelfree method for finding the flight speed which achieves the longest flight time or flight range given a predefined path. The method was based on extremum seeking control and does not require power consumption models of the multicopter.…”

Section: Introductionmentioning

confidence: 99%

“…A common problem of extremum seeking controllers is slow convergence speed. In [28], which is our follow-up work of [22], we proposed a novel extremum seeking controller with adaptive step size which achieves faster convergence than the standard extremum seeking controller. In addition to flight speed, it could also find the optimal flight sideslip (heading) to achieve the longest flight range.…”

Section: Introductionmentioning

confidence: 99%

“…Nevertheless, in both [22] and [28], the experimental validation was conducted indoors where the setup was far from real-world applications: firstly, a motion capture system was used to provide state estimation of very high accuracy, which is not available outdoors. Secondly, because of the limited flight space, following the flight path (a circular path with a radius of about 2 meters) at a high speed required a significant centripetal force, which contributed a lot to the power consumption and is rare in real-world applications.…”

Section: Introductionmentioning

confidence: 99%

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Model-free online motion adaptation for energy efficient flights of multicopters

Zeng

Tagliabue

et al. 2021

Preprint

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Limited flight distance and time is a common problem for multicopters. We propose a method for finding the optimal speed and heading of multicopters while flying a given path to achieve the longest flight distance or time. Since flight speed and heading are often free variables in multicopter path planning, they can be changed without changing the mission. The proposed method is based on a novel multivariable extremum seeking controller with adaptive step size. It (a) does not require any power consumption model of the vehicle, (b) can be executed online, (c) is computationally efficient and runs on low-cost embedded computers in real-time, and (d) converges faster than the standard extremum seeking controller with constant step size. We prove the stability of this proposed extremum seeking controller, and conduct outdoor experiments to validate the effectiveness of this method with different initial conditions, with and without payload. This method could be especially useful for applications such as package delivery, where the weight, size and shape of the payload vary between deliveries and the power consumption of the vehicle is hard to model. Experiments show that compared to flying at the maximum speed with a bad heading angle, flying at the optimal range speed and heading reduces the energy consumed per distance by 24.9% without payload and 33.5% with a box payload. In addition, compared to hovering, flying at the optimal endurance speed and heading reduces the the power consumption by 7.0% without payload and 12.6% with a box payload.

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Model-free Online Motion Adaptation for Optimal Range and Endurance of Multicopters

Cited by 23 publications

References 18 publications

Trajectory Generation for Aerial Multicopters

Trajectory Generation for Aerial Multicopters

Practical and Accurate Generation of Energy-Optimal Trajectories for a Planar Quadrotor

Model-free online motion adaptation for energy efficient flights of multicopters

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