Guidance and Control of an Autonomous Soaring Vehicle with Flight Test Results

Allen, Michael J.

doi:10.2514/6.2007-867

Cited by 92 publications

(103 citation statements)

References 8 publications

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“…Qðs, aÞ → Qðs, aÞ + ηðr + βQðs′, a′Þ − Qðs, aÞÞ, [5] where r is the received reward and η is the learning rate. The update is made online and does not require any prior model of the flow or the flight.…”

Section: Modelsmentioning

confidence: 99%

“…Once migratory birds have climbed up to the top of a thermal, they glide down to the next thermal and repeat the process, a migration strategy that strongly reduces energy costs (4). Soaring strategies are also important for technological applications, namely, the development of autonomous gliders that can fly large distances with minimal energy consumption (5).…”

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confidence: 99%

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Learning to soar in turbulent environments

Reddy

Celani

Sejnowski

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

146

115

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Birds and gliders exploit warm, rising atmospheric currents (thermals) to reach heights comparable to low-lying clouds with a reduced expenditure of energy. This strategy of flight (thermal soaring) is frequently used by migratory birds. Soaring provides a remarkable instance of complex decision making in biology and requires a longterm strategy to effectively use the ascending thermals. Furthermore, the problem is technologically relevant to extend the flying range of autonomous gliders. Thermal soaring is commonly observed in the atmospheric convective boundary layer on warm, sunny days. The formation of thermals unavoidably generates strong turbulent fluctuations, which constitute an essential element of soaring. Here, we approach soaring flight as a problem of learning to navigate complex, highly fluctuating turbulent environments. We simulate the atmospheric boundary layer by numerical models of turbulent convective flow and combine them with model-free, experience-based, reinforcement learning algorithms to train the gliders. For the learned policies in the regimes of moderate and strong turbulence levels, the glider adopts an increasingly conservative policy as turbulence levels increase, quantifying the degree of risk affordable in turbulent environments. Reinforcement learning uncovers those sensorimotor cues that permit effective control over soaring in turbulent environments.thermal soaring | turbulence | navigation | reinforcement learning M igrating birds and gliders use upward wind currents in the atmosphere to gain height while minimizing the energy cost of propulsion by the flapping of the wings or engines (1, 2). This mode of flight, called soaring, has been observed in a variety of birds. For instance, birds of prey use soaring to maintain an elevated vantage point in their search for food (3); migrating storks exploit soaring to cover large distances in their quest for greener pastures (4). Different forms of soaring have been observed. Of particular interest here is thermal soaring, where a bird gains height by using warm air currents (thermals) formed in the atmospheric boundary layer. For both birds and gliders, a crucial part of the thermal soaring is to identify a thermal and to find and maintain its core, where the lift is typically the largest. Once migratory birds have climbed up to the top of a thermal, they glide down to the next thermal and repeat the process, a migration strategy that strongly reduces energy costs (4). Soaring strategies are also important for technological applications, namely, the development of autonomous gliders that can fly large distances with minimal energy consumption (5).Thermals arise as ascending convective plumes driven by the temperature gradient created due to the heating of the earth's surface by the sun (6). Hydrodynamic instabilities and processes that lead to the formation of a thermal inevitably give rise to a turbulent environment characterized by strong, erratic fluctuations (7,8). Birds or gliders attempting to find and maintain a thermal face...

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Section: Modelsmentioning

confidence: 99%

mentioning

confidence: 99%

Learning to soar in turbulent environments

Reddy

Celani

Sejnowski

et al. 2016

Proc. Natl. Acad. Sci. U.S.A.

146

115

View full text Add to dashboard Cite

show abstract

“…One of these challenges will be the complex wind fields generated by urban structures and terrain. Although much effort has been directed towards developing planning and estimation strategies for wind fields at high altitudes or in large open spaces [1]- [7], these approaches contain an implicit assumption that the wind field evolves over relatively large temporal and spatial scales. Given this simplification, a history of local measurements can be used to estimate the global wind field with sufficient accuracy.…”

Section: Introductionmentioning

confidence: 99%

An analysis of wind field estimation and exploitation for quadrotor flight in the urban canopy layer

Ware

Roy

2016

2016 IEEE International Conference on Robotics and Automation (ICRA)

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Abstract-Although unmanned air vehicles' increasing agility and autonomy may soon allow for flight in urban environments, the impact of complex urban wind fields on vehicle flight performance remains unclear. Unlike synoptic winds at high altitudes, urban wind fields are subject to turbulence generated by the buildings and terrain. The resulting spatial and temporal variation makes inference about the global wind field based on local wind measurements difficult and prevents the use of most simple wind models. Fortunately, the structure of the urban environment provides exploitable predictability given a suitable computational fluid dynamics solver, a representative 3D model of the environment, and an estimate of the expected prevailing wind speed and heading. The prevailing wind speed and direction at altitude and computational fluid dynamics solver can generate the corresponding wind field estimate over the map. By generating wind fields in this way, this work investigates a quadrotor's ability to exploit them for improved flight performance. Along with the wind field estimate, an empirically derived power consumption model is used to find minimum-energy trajectories with a planner both aware of and naive to the wind field. When compared to minimum-energy trajectories that do not incorporate wind conditions, the wind-aware trajectories demonstrate reduced flight times, total energy expenditures, and failures due to excess air speed for trajectories across MIT campus.

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“…1,2) Furthermore, recent advances in the utilization of wind as a useful energy (e.g., soaring flight for low-energy consumption), 3) not just a disturbance, demand more extensive studies of airborne wind estimation and measurement. The airborne methods extract a wind component from data collected by the UAV's onboard sensors (e.g., air-data system (ADS), global positioning system (GPS), and inertial navigation system (INS)) in real time.…”

Section: Introductionmentioning

confidence: 99%

Dynamics and Simulation of the Effects of Wind on UAVs and Airborne Wind Measurement

Choi

Lee

Ryu

et al. 2015

Trans. Japan Soc. Aero. S Sci.

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This paper presents a simulation of wind effects on a flying unmanned aerial vehicle (UAV) and a simulation of wind measurements taken from an airborne UAV. The wind acts as a disturbance on the flying UAV by altering its attitude and velocity. Therefore, the airborne measurement should be taken dynamically to reflect the instantaneous motion of a vehicle that is affected by wind. In order to understand and validate the algorithm that measures wind and the interaction between wind and the UAV, an investigation using simulation is extremely valuable. Considering real applications, this study implements modeling of the interaction between wind and a UAV from the perspective of the UAV's onboard sensors, and a wind measurement formula is clarified based on the established model. The results of the simulation confirm that, if an accurate measurements for angle-of-attack and sideslip are available, the wind can be accurately measured regardless of the control scheme used in flight.

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Guidance and Control of an Autonomous Soaring Vehicle with Flight Test Results

Cited by 92 publications

References 8 publications

Learning to soar in turbulent environments

Learning to soar in turbulent environments

An analysis of wind field estimation and exploitation for quadrotor flight in the urban canopy layer

Dynamics and Simulation of the Effects of Wind on UAVs and Airborne Wind Measurement

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