Least-Energy Path Planning With Building Accurate Power Consumption Model of Rotary Unmanned Aerial Vehicle

Hong, Dooyoung; Lee, Seonhoon; Cho, Young Hoo; Baek, Donkyu; Kim, Jaemin; Chang, Naehyuck

doi:10.1109/tvt.2020.3040537

Cited by 20 publications

(4 citation statements)

References 14 publications

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“…This is achieved by using the results from CFD simulation into other methods, such as for training machine learning algorithms [31,32], by scaling it according to historical real-time weather readings [33], or by creating a surrogate function which can predict the wind field through inference from the acquired wind measurements at ideal positions [20,34,35]. With quicker wind field prediction, warnings can be given ahead of time to prevent UAS from flying into hazardous areas, and flight paths can be planned to achieve the most efficient UAS energy consumption [36][37][38]. While the findings in this study are still preliminary, this paper along with numerous others highlight the importance of assessing the wind field conditions in urban environments as part of implementing UAS traffic management to ensure the safety of all UAS operations in the future.…”

Section: Summary and Future Workmentioning

confidence: 99%

Simulation of Wind Field in a Building Complex for Evaluation of the Wind Effect Along UAS Flight Path

Nathanael

Wang

Low

2023

AIAA AVIATION 2023 Forum

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With the use of Unmanned Aerial Systems (UAS) becoming more common, wind field within urban environments becomes significant, and its impact on UAS flight should be examined to ensure UAS operations can be implemented and managed safely with little to no risk of damages and injuries to the inhabitants and infrastructures. This study presents the wind field within the Biopolis building complex in Singapore from steady-state RANS simulation using OpenFOAM v1912. The wind simulation was done using various inlet wind speeds and directions based on the measurements from a weather station near the building complex. After obtaining the velocity field for the whole area, the wind effect on UAS along a given flight path was inferred from the local wind velocity magnitude and direction along the path. When the general wind heading was aligned with the flight path, the local wind and its impact on the UAS along its path had more gradual and less extreme changes than in other cases where the UAS had to fly by crossing through the wake flow of the buildings. Additionally, for flow cases with the same inlet wind direction but different reference speeds, similarity in their results was observed when the wind velocity magnitude was normalized with respect to the velocity magnitude at the inlet boundary. Contours of the wind Turbulence Kinetic Energy (TKE) from the simulation results show that the areas with higher TKE are located around buildings at the most upstream against the wind, and these areas increase in size and TKE levels with higher wind speed.

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Section: Summary and Future Workmentioning

confidence: 99%

Simulation of Wind Field in a Building Complex for Evaluation of the Wind Effect Along UAS Flight Path

Nathanael

Wang

Low

2023

AIAA AVIATION 2023 Forum

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“…These models typically include various aspects such as flight speed, attitude control, sensors, and communication systems [20][21][22]. The second method involves reducing energy consumption and extending the mission time of the UAV through rational trajectory planning and movement strategies within the defined trajectory [23,24].…”

Section: Introductionmentioning

confidence: 99%

Multiobjective Energy Consumption Optimization of a Flying–Walking Power Transmission Line Inspection Robot during Flight Missions Using Improved NSGA-II

Wang,

Qin,

Jia

et al. 2024

Applied Sciences

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In order to improve the flight efficiency of a flying–walking power transmission line inspection robot (FPTLIR) during flight missions, an accurate energy consumption model is constructed, and a multiobjective optimization approach using the improved NSGA-II is proposed to address the high energy consumption and long execution time. The energy consumption model is derived from the FPTLIR kinematics to the motor dynamics, with the key parameters validated using a test platform. A multiobjective optimization model is proposed that considers many constraints related to the FPTLIR during missions, offering a comprehensive analysis of the energy consumption and execution time. The NSGA-II algorithm is improved by integrating the Cauchy variation operator and the simulated annealing algorithm, which is used to construct the multiobjective optimization approach. Simulation and experimental results demonstrate that the proposed model accurately predicts the energy consumption of the FPTLIR across different paths and flight conditions with an average relative error ranging from 0.76% to 3.24%. After optimization, energy savings of 5.33% and 5.01% are achieved for on-line and off-line missions, respectively, while maintaining the shortest execution time at the given energy level. The energy consumption optimization approach significantly improves the flight efficiency of the system, providing a reference for analyzing and optimizing energy consumption of inspection robots.

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“…Besides, some researchers proposed data-driven models by selecting variables that affect the power consumption (e.g. the vehicle's speed and acceleration, wind speed, and payload weight) as inputs and finding their relationship to power consumption through experimental data [16] [21].…”

Section: Introductionmentioning

confidence: 99%

Model-Free Online Motion Adaptation for Energy-Efficient Flight of Multicopters

Wu¹,

Zeng

Tagliabue

et al. 2022

IEEE Access

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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 sideslip angle of a multicopter flying a given path to achieve either the longest flight distance or time. Since flight speed and sideslip 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, which is inspired by recent work from the machine learning community on stochastic optimization. Our method (a) does not require a power consumption model of the vehicle, (b) is computationally efficient and runs on low-cost embedded computers in real-time, and (c) converges faster than the standard extremum seeking controller with constant step size. We prove the stability of this approach and validate it through outdoor experiments. The method is shown to converge with different payloads and in the presence of wind. Compared to flying at the maximum achievable speed in the experiments with a uniformly selected random sideslip angle, flying at the optimal range speed and sideslip on average increases the flight range by 14.3% without payload and 19.4% with a box payload. In addition, compared to hovering, flying at the optimal endurance speed and sideslip increases the flight time by 7.5% without payload and 14.4% with a box payload. A video can be found at https://youtu.be/aLds8LVfogk INDEX TERMS Unmanned aerial vehicles, Motion planning, Energy consumption, Robotics and automation VOLUME xx, 2022

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Least-Energy Path Planning With Building Accurate Power Consumption Model of Rotary Unmanned Aerial Vehicle

Cited by 20 publications

References 14 publications

Simulation of Wind Field in a Building Complex for Evaluation of the Wind Effect Along UAS Flight Path

Simulation of Wind Field in a Building Complex for Evaluation of the Wind Effect Along UAS Flight Path

Multiobjective Energy Consumption Optimization of a Flying–Walking Power Transmission Line Inspection Robot during Flight Missions Using Improved NSGA-II

Model-Free Online Motion Adaptation for Energy-Efficient Flight of Multicopters

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