Autonomous Soaring Research Articles

Evaluation of an Autonomous Soaring Algorithm for Survey Missions in Simulation Environment

Evaluation of an Autonomous Soaring Algorithm for Survey Missions in Simulation Environment

Energy-Harvesting Strategy Investigation for Glider Autonomous Soaring Using Reinforcement Learning

Birds and experienced glider pilots frequently use atmospheric updrafts for long-distance flight and energy conservation, with harvested energy from updrafts serving as the foundation. Inspired by their common characteristics in autonomous soaring, a reinforcement learning algorithm, the Twin Delayed Deep Deterministic policy gradient, is used to investigate the optimal strategy for an unpowered glider to harvest energy from thermal updrafts. A round updraft model is utilized to characterize updrafts with varied strengths. A high-fidelity six-degree-of-glider model is used in the dynamic modeling of a glider. The results for various flight initial positions and updraft strengths demonstrate the effectiveness of the strategy learned via reinforcement learning. To enhance the updraft perception ability and expand the applicability of the trained glider agent, an extra wind velocity differential correction module is introduced to the algorithm, and a strategy symmetry method is applied. Comparison experiments regarding round updraft, the Gedeon thermal model, and Dryden continuous turbulence indicate the crucial role of the further optimized methods in improving the updraft-sensing ability of the reinforcement learning glider agent. With optimized methods, a glider trained in a simplified thermal updraft with a simple training method can have more effective flight strategies.

An Autonomous Soaring for Small Drones Using the Extended Kalman Filter Thermal Updraft Center Prediction Method Based on Ordinary Least Squares

Many birds in the natural world are capable of engaging in sustained soaring within thermal updrafts for extended periods without flapping their wings. Autonomous soaring has the potential to greatly improve both the range and endurance of small drones. In this paper, the extended Kalman filter (EKF) thermal updraft center prediction method based on ordinary least squares (OLS) is proposed to develop the autonomous soaring system for small drones, and an adaptive step size update strategy is incorporated into the EKF. The proposed method is compared with EKF thermal updraft prediction methods through simulated experiments. The results indicate that the proposed prediction method has low computational complexity and fast convergence speed and performs more stably in weak thermal updrafts. The above advantages stem from the OLS providing an approximate distribution of the thermal updraft around the drone for the EKF. This empowers the EKF algorithm with ample information to dynamically update the thermal updraft center in real time. The adaptive step size update strategy further accelerates the convergence speed of this process. In addition, flight experiments were conducted on the Talon fixed-wing drone platform to test the autonomous soaring system. During the flight experiment, the drone successfully engaged in static soaring within thermal updrafts, effectively hovering and gaining energy. Throughout the approximately 40 min flight duration, the drone only utilized its propulsion for about 8 min. This demonstrated the effectiveness of the autonomous soaring system using the EKF thermal updraft center prediction method based on OLS. Finally, by analyzing and discussing the differences between the simulation experiment results and the flight experiment results, some improvement strategies for the current work are proposed.

Finite Horizon Nonlinear Suboptimal Control for an Autonomous Soaring UAV

In this paper, a suboptimal nonlinear discrete control for nonlinear discrete affine systems is implemented in an autonomous soaring unmanned aerial vehicle (UAV) to improve energy consumption and performance. General expressions for this controller are obtained from the continuous nonlinear guidance model of a fixed-wing UAV and applied in a multiloop control structure. Simulation results in the task of trajectory tracking inside a thermal updraft and a comparative study with a proportional derivative (PD) controller validated the effectiveness of this method.

Neuroevolutionary Control for Autonomous Soaring

The energy efficiency and flight endurance of small unmanned aerial vehicles (SUAVs) can be improved through the implementation of autonomous soaring strategies. Biologically inspired flight techniques such as dynamic and thermal soaring offer significant energy savings through the exploitation of naturally occurring wind phenomena for thrustless flight. Recent interest in the application of artificial intelligence algorithms for autonomous soaring has been motivated by the pursuit of instilling generalized behavior in control systems, centered around the use of neural networks. However, the topology of such networks is usually predetermined, restricting the search space of potential solutions, while often resulting in complex neural networks that can pose implementation challenges for the limited hardware onboard small-scale autonomous vehicles. In exploring a novel method of generating neurocontrollers, this paper presents a neural network-based soaring strategy to extend flight times and advance the potential operational capability of SUAVs. In this study, the Neuroevolution of Augmenting Topologies (NEAT) algorithm is used to train efficient and effective neurocontrollers that can control a simulated aircraft along sustained dynamic and thermal soaring trajectories. The proposed approach evolves interpretable neural networks in a way that preserves simplicity while maximizing performance without requiring extensive training datasets. As a result, the combined trajectory planning and aircraft control strategy is suitable for real-time implementation on SUAV platforms.

Never landing drone: Autonomous soaring of a unmanned aerial vehicle in front of a moving obstacle

Increasing endurance is a major challenge for battery-powered aerial vehicles. A method is presented which makes use of an updraft around obstacles to decrease the power consumption of a fixed-wing unmanned aerial vehicle. A soaring flight controller has been developed that can autonomously soar while the unmanned aerial vehicle keeps its relative position to that of a moving object. Multiple simulations have been performed to analyse the limitations of the soaring controller under different conditions. The effect of a change in wind velocity and updraft has been analysed. The simulations showed that an increase in updraft decreases the energy consumption of the flight controller. An increase in wind velocity results in a higher updraft requirement, while a decrease in the wind velocity requires less updraft. The simulations achieved sustained flight at 0% throttle. The controller has been validated experimentally using the updraft generated by a moving ship. The practical, autonomous tests reduced the average throttle down to 4.5% in front of a ship. The method presented in this study achieved successful hovering flight using an energy control module for longitudinal positioning.

Artificial Lumbered Flight for Autonomous Soaring

Soaring strategies are redefining the flight capabilities of small-class fixed-wing unmanned aerial vehicles. This paper presents an autonomous soaring strategy that exploits updraft energy independent of the classification of an updraft. The strategy employs an artificial lumbered flight algorithm (ALFA) that weighs near-field updraft velocity estimates and mission priorities in real time for navigation through a wind field. This work addresses the question of ALFA’s ability to handle classified and unclassified updrafts. Instead of explicitly considering the classification of the updraft, the ALFA measures updraft data along an aircraft’s flight path, estimates updraft data ahead of the aircraft, generates candidate flight paths ahead of the aircraft for evaluation, and then selects the best candidate flight path based on a reward function. This paper describes the structure of ALFA and the tuning processes used for the updraft estimator and the reward function. Flight results demonstrate the ALFA’s ability to harvest atmospheric energy from classified and unclassified updrafts. The results discuss several produced flight behaviors in more detail, examining the ALFA’s effectiveness when flying among classified updrafts. Finally, this paper concludes that harvesting energy from the atmosphere with real-time local decision making is practically feasible and suggests that autonomous flight design and control strategies for small-class fixed-wing aircraft will likely be driven by harvesting energy from the atmosphere.

Autonomous soaring using a simplified MPC approach

ABSTRACTThe need for efficient propulsion systems allied to increasingly more challenging fixed-wing UAV mission requirements has led to recent research on the autonomous thermal soaring field with promising results. As part of that effort, the feasibility and advantages of model predictive control (MPC)-based guidance and control algorithms capable of extracting energy from natural occurring updrafts have already been demonstrated numerically. However, given the nature of the dominant atmospheric phenomena and the amplitude of the required manoeuvres, a non-linear optimal control problem results. Depending on the adopted prediction horizon length, it may be of large order, leading to implementation and real-time operation difficulties. Knowing that, an alternative MPC-based autonomous thermal soaring controller is presented herein. It is designed to yield a simple and small non-linear programming problem to be solved online. In order to accomplish that, linear prediction schemes are employed to impose the differential constraints, thus no extra variables are added to the problem and only linear bound restrictions result. For capturing the governing non-linear effects during the climb phase, a simplified representation of the aircraft kinematics with quasi-steady corrections is used by the controller internal model. Flight simulation results using a 3 degree-of-freedom model subjected to a randomly generated time varying thermal environment show that the aircraft is able to locate and exploit updrafts, suggesting that the proposed algorithm is a feasible MPC strategy to be employed in a practical application.

Stochastic Trajectory Optimization for Autonomous Soaring of UAV

Thermal soaring gliders exploit naturally occurring rising air (called thermals) as sources of energy. These sources generally appear in uncertain and random way as their position and strength are hardly predictable. Several atmospheric factors affect the behavior of thermals (season, time of the day and nature of vegetation on the ground, etc). However, a statistical model of thermals to be encountered can be build. Soaring in a optimal manner while taking advantage of thermals represents thus a stochastic optimization problem that can be solved and implemented in an autonomous glider. The present paper details how to find an optimal trajectory for going through given checkpoints, with the objective of doing as much rounds as possible within a predetermined time. The optimal control of the glider is determined by determining i) whether the glider should climb a thermal with a specific strength when it encounters one, or to cruise, ii) the optimal speed to fly during the cruising phases. The problem is solved using dynamic programming and the proposed optimal path planning strategy is validated by simulation.

Glider soaring via reinforcement learning in the field.

Soaring birds often rely on ascending thermal plumes (thermals) in the atmosphere as they search for prey or migrate across large distances1-4. The landscape of convective currents is rugged and shifts on timescales of a few minutes as thermals constantly form, disintegrate or are transported away by the wind5,6. How soaring birds find and navigate thermals within this complex landscape is unknown. Reinforcement learning7 provides an appropriate framework in which to identify an effective navigational strategy as a sequence of decisions made in response to environmental cues. Here we use reinforcement learning to train a glider in the field to navigate atmospheric thermals autonomously. We equipped a glider of two-metre wingspan with a flight controller that precisely controlled the bank angle and pitch, modulating these at intervals with the aim of gaining as much lift as possible. A navigational strategy was determined solely from the glider's pooled experiences, collected over several days in the field. The strategy relies on on-board methods to accurately estimate the local vertical wind accelerations and the roll-wise torques on the glider, which serve as navigational cues. We establish the validity of our learned flight policy through field experiments, numerical simulations and estimates of the noise in measurements caused by atmospheric turbulence. Our results highlight the role of vertical wind accelerations and roll-wise torques as effective mechanosensory cues for soaring birds and provide a navigational strategy that is directly applicable to the development of autonomous soaring vehicles.

Alternate Strategies for Optimal Unmanned Aerial Vehicle Thermaling

This paper investigates optimal glider trajectories for gaining altitude from thermal updraft columns (“thermaling”). Thermal updraft columns provide a natural energy resource that can be beneficial for enabling long-term autonomous soaring objectives. In this paper, computational optimal control is used to investigate possible alternatives to the common strategy of circularly orbiting the thermal to harvest potential energy. The Legendre–Gauss–Lobatto pseudospectral method is applied to a simulated glider in multiple scenarios over a range of thermal sizes, thermal intensities, and glider start conditions. Results for the Gedeon thermal model, which simulates dropoff in wind intensity with distance from the thermal core and additionally incorporates external downdrafts, are considered. These are then compared in performance to centered circular orbiting as well as two other alternate heuristic strategies suggested by the optimal control results: off-centered circular orbiting and “figure eights”. Results suggest benefits to using alternatives to centered circular orbiting when encountering small-diameter thermal updrafts. Results also support the utility of centered circular orbiting for medium- to large-diameter thermals while providing new insights into the optimal parameters of the circular orbit.

The AutoSOAR autonomous soaring aircraft, part 1: Autonomy algorithms

AbstractAutonomous soaring has the potential to greatly improve both the range and endurance of small robotic aircraft. This paper describes an autonomous soaring system that generates a dynamic map of lift sources (thermals) in the environment and uses this map for online flight planning and decision making. Components of the autonomy algorithm include thermal mapping, explore/exploit decision making, navigation, optimal airspeed computation, thermal centering control, and energy state estimation. A finite state machine manages the aircraft behavior during flight and determines when changing behavior is appropriate. A complete system to enable autonomous soaring is described with special attention paid to practical considerations encountered during flight testing. A companion paper describes the hardware implementation of this system and the results of a flight test campaign conducted at Aberdeen Proving Ground in September 2015.

The AutoSOAR autonomous soaring aircraft part 2: Hardware implementation and flight results

AbstractAutonomous soaring has the potential to greatly improve both the range and endurance of small robotic aircraft. This paper describes the results of a test flight campaign to demonstrate an autonomous soaring system that generates a dynamic map of lift sources (thermals) in the environment and uses this map for on‐line flight planning and decision making. The aircraft is based on a commercially available radio‐controlled glider; it is equipped with an autopilot module for low‐level flight control and on‐board computer that hosts all autonomy algorithms. Components of the autonomy algorithm include thermal mapping, explore/exploit decision making, navigation, optimal airspeed computation, thermal centering control, and energy state estimation. A finite state machine manages flight behaviors and switching between behaviors. Flight tests at Aberdeen Proving Ground resulted in 7.8 h flight time with the autonomous soaring system engaged, with three hours spent climbing in thermals. Postflight computation of energy state and frequent observations of groups of birds thermalling with our aircraft indicate that it was effectively exploiting available energy.

Maximizing Net Power in Circular Turns for Solar and Autonomous Soaring Aircraft

This study investigates maximizing net power input from a solar-photovoltaic array and/or a thermal updraft while performing constant bank angle, circular flight-path turns (that is, an orbit). The process inputs are the aircraft sink polar (or, equivalently, the power required curve), the date, the time, the location, and the flight altitude. A solar insolation model is combined with a best-turn performance calculation to determine the bank angle that maximizes power input from a solar-photovoltaic array at varying sun elevation angles. In general, for low sun elevation angles, the maximum net power gain from solar input and drag output is found at higher bank angles and shows 15% (absolute) gain over the limiting case of wings-level orbits in the same conditions. For high sun elevation angles, the maximum net power is found at low bank angles. The break point between high and low sun elevation angles varies with aircraft parameters and is approximately 25 deg for the example aircraft in this paper. The relative power losses of off-optimum orbits at a given bank angle are quantified. Finally, a thermal-updraft model is included in the net power calculations to determine an optimal bank angle that maximizes the average power input from a combination of solar photovoltaics and thermal updrafts. This paper shows that soaring and solar photovoltaics can be mutually beneficial, and it provides a method to calculate losses at off-optimum conditions.

Emulating avian orographic soaring with a small autonomous glider

This paper explores a method by which an unpowered, fixed-wing micro air vehicle (MAV) may autonomously gain height by utilising orographic updrafts in urban environments. These updrafts are created when wind impinges on both man-made and natural obstacles, and are often highly turbulent and very localised. Thus in contrast to most previous autonomous soaring research, which have focused on large thermals and ridges, we use a technique inspired by kestrels known as ‘wind-hovering’, in order to maintain unpowered flight within small updrafts. A six-degree-of-freedom model of a MAV was developed based on wind-tunnel tests and vortex-lattice calculations, and the model was used to develop and test a simple cascaded control system designed to hold the aircraft on a predefined trajectory within an updraft. The wind fields around two typical updraft locations (a building and a hill) were analysed, and a simplified trajectory calculation method was developed by which trajectories for height gain can be calculated on-board the aircraft based on a priori knowledge of the wind field. The results of simulations are presented, demonstrating the behaviour of the system in both smooth and turbulent flows. Finally, the results from a series of flight tests are presented. Flight tests at the hill were consistently successful, while flights around the building could not be sustained for periods of more than approximately 20 s. The difficulty of operating near a building is attributable to significant levels of low-frequency unsteadiness (gustiness) in the oncoming wind during the flight tests, effectively resulting in a loss of updraft for sustained periods.

Long Endurance Autonomous Flight for Unmanned Aerial Vehicles

This paper presents a summary of research performed at the University of Sydney towards extending the flight duration of fixed-wing unmanned aerial vehicles. A historical context to extended flight is provided and particular attention is paid to research in autonomous soaring and aerial refueling. Autonomous soaring presents a unique set of challenges whereby an aircraft must autonomously identify sources of energy in the wind field and generate trajectories to exploit these conditions to collect energy. The basic mechanisms of soaring flight are examined and methods for generating energy gaining trajectories for exploration, information gathering and patrolling missions with multiple aircraft are detailed. Aerial refueling represents a complementary approach for extending flight duration, and the challenges and current efforts towards autonomous refueling between small aircraft are also detailed.

Thermal Centering Control for Autonomous Soaring; Stability Analysis and Flight Test Results

This paper addresses the challenge of using autonomous soaring gliders to search for and exploit thermal lift to extend the gliders’ endurance. For this purpose, a simple thermal centering controller is proposed. The paper includes theoretical analysis of stability and convergence properties of this controller. Using an exponentialGaussian function to represent the updraft field of a thermal, the Lyapunov type analysis shows the proposed controller to be asymptotically stable and determines its region of attraction. The size of the region of attraction is shown to be a function of the feedback gain that can be adjusted for any given strength and geometry of thermal. The paper additionally presents simulation and flight test results that verify the performance of the proposed controller. The results of the flight trials also confirm the feasibility and effectiveness of using autonomous thermal soaring to extend endurance for unmanned gliders.

Predictive Control for Soaring of Unpowered Autonomous UAVs

We design an autonomous soaring controller for an unpowered UAV in a nonlinear MPC framework. The UAV is controlled with the aim of extracting the maximum amount of potential/kinetic energy from the environment's updrafts. We focus on conceptual feasibility at this stage and make the realistic assumption that the UAV obtains updraft information only along the flight trajectory. The surrounding updraft distribution is then recursively estimated (online) by combining the measurements from the optimal trajectory with a heuristic search, if necessary. A variation of the standard grid search is used such that the grid spacing is altered depending on the updraft information along the UAV's flight path. Results from both standard and adaptive grid search approaches are presented. In abstract terms, this work can be viewed as finding optimal paths in uncertain vector fields.

Autonomous Soaring: The Montague Cross-Country Challenge

A novel method was developed for locating and allowing gliders to stay in thermals (convective updrafts). The method was applied to a 5 kg, glider, called ALOFT (autonomous locator of thermals), that was entered in the 2008 Montague Cross-Country Challenge held on 13-15 June 2008 in Montague, California. In this competition, remote-controlled gliders in the 5 kg class competed on the basis of speed and distance. ALOFT was the first known autonomously soaring aircraft to enter a soaring competition and its entry provided a valuable comparison between the effectiveness of manual soaring and autonomous soaring. ALOFT placed third in the competition in overall points, outperforming manually-flown aircraft in its ability to center and use updrafts, especially at higher altitudes and in the presence of wind, to fly more optimal airspeeds, and to fly directly between turn points. The results confirm that autonomous soaring is a bona fide engineering subdiscipline, which is expected to be of interest to engineers, who might find that this has some utility in the aviation industry.

Adaptive LQ Control With Anti-Windup Augmentation to Optimize UAV Performance in Autonomous Soaring Applications

The performance of a glider can be considerably improved by means of soaring and optimization of its trajectory. Recent advances in the area of static soaring assume known linear glider dynamics and no actuator saturation phenomena. In practice, the dynamics of the glider change with flight and environmental conditions, and the actuators moving the control surfaces have mechanical limits. In this paper, we consider the optimization-based static soaring problem in the presence of actuator saturation nonlinearities and large parametric uncertainties in the dynamics of the vehicle. We use ideas from robust adaptive control and anti-windup design tools to develop an adaptive control scheme based on linear quadratic control with disturbance rejection. The saturation-type nonlinearity is addressed by the proposed adaptive version of a linear matrix inequality based anti-windup design. The resulting adaptive control scheme with adaptive anti-windup augmentation allows optimal soaring despite the presence of significant actuator saturation limits and unknown parameters.