436 research outputs found
PAC: A Novel Self-Adaptive Neuro-Fuzzy Controller for Micro Aerial Vehicles
There exists an increasing demand for a flexible and computationally
efficient controller for micro aerial vehicles (MAVs) due to a high degree of
environmental perturbations. In this work, an evolving neuro-fuzzy controller,
namely Parsimonious Controller (PAC) is proposed. It features fewer network
parameters than conventional approaches due to the absence of rule premise
parameters. PAC is built upon a recently developed evolving neuro-fuzzy system
known as parsimonious learning machine (PALM) and adopts new rule growing and
pruning modules derived from the approximation of bias and variance. These rule
adaptation methods have no reliance on user-defined thresholds, thereby
increasing the PAC's autonomy for real-time deployment. PAC adapts the
consequent parameters with the sliding mode control (SMC) theory in the
single-pass fashion. The boundedness and convergence of the closed-loop control
system's tracking error and the controller's consequent parameters are
confirmed by utilizing the LaSalle-Yoshizawa theorem. Lastly, the controller's
efficacy is evaluated by observing various trajectory tracking performance from
a bio-inspired flapping-wing micro aerial vehicle (BI-FWMAV) and a rotary wing
micro aerial vehicle called hexacopter. Furthermore, it is compared to three
distinctive controllers. Our PAC outperforms the linear PID controller and
feed-forward neural network (FFNN) based nonlinear adaptive controller.
Compared to its predecessor, G-controller, the tracking accuracy is comparable,
but the PAC incurs significantly fewer parameters to attain similar or better
performance than the G-controller.Comment: This paper has been accepted for publication in Information Science
Journal 201
Aerial Vehicles
This book contains 35 chapters written by experts in developing techniques for making aerial vehicles more intelligent, more reliable, more flexible in use, and safer in operation.It will also serve as an inspiration for further improvement of the design and application of aeral vehicles. The advanced techniques and research described here may also be applicable to other high-tech areas such as robotics, avionics, vetronics, and space
BioDrone: A Bionic Drone-based Single Object Tracking Benchmark for Robust Vision
Single object tracking (SOT) is a fundamental problem in computer vision,
with a wide range of applications, including autonomous driving, augmented
reality, and robot navigation. The robustness of SOT faces two main challenges:
tiny target and fast motion. These challenges are especially manifested in
videos captured by unmanned aerial vehicles (UAV), where the target is usually
far away from the camera and often with significant motion relative to the
camera. To evaluate the robustness of SOT methods, we propose BioDrone -- the
first bionic drone-based visual benchmark for SOT. Unlike existing UAV
datasets, BioDrone features videos captured from a flapping-wing UAV system
with a major camera shake due to its aerodynamics. BioDrone hence highlights
the tracking of tiny targets with drastic changes between consecutive frames,
providing a new robust vision benchmark for SOT. To date, BioDrone offers the
largest UAV-based SOT benchmark with high-quality fine-grained manual
annotations and automatically generates frame-level labels, designed for robust
vision analyses. Leveraging our proposed BioDrone, we conduct a systematic
evaluation of existing SOT methods, comparing the performance of 20
representative models and studying novel means of optimizing a SOTA method
(KeepTrack KeepTrack) for robust SOT. Our evaluation leads to new baselines and
insights for robust SOT. Moving forward, we hope that BioDrone will not only
serve as a high-quality benchmark for robust SOT, but also invite future
research into robust computer vision. The database, toolkits, evaluation
server, and baseline results are available at http://biodrone.aitestunion.com.Comment: This paper is published in IJCV (refer to DOI). Please cite the
published IJC
Reinforcement Learning to Control Lift Coefficient Using Distributed Sensors on a Wind Tunnel Model
Arrays of sensors distributed on the wing of fixed-wing vehicles can provide information not directly available to conventional sensor suites. These arrays of sensors have the potential to improve flight control and overall flight performance of small fixed-wing uninhabited aerial vehicles (UAVs). This work investigated the feasibility of estimating and controlling aerodynamic coefficients using the experimental readings of distributed pressure and strain sensors across a wing. The study was performed on a one degree-of-freedom model about pitch of a fixed-wing platform instrumented with the distributed sensing system. A series of reinforcement learning (RL) agents were trained in simulation for lift coefficient control, then validated in wind tunnel experiments. The performance of RL-based controllers with different sets of inputs in the observation space were compared with each other and with that of a manually tuned PID controller. Results showed that hybrid RL agents that used both distributed sensing data and conventional sensors performed best across the different tests.</p
Perching with fixed wings
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.Includes bibliographical references (leaves 43-46).Human pilots have the extraordinary ability to remotely maneuver small Unmanned Aerial Vehicles (UAVs) far outside the flight envelope of conventional autopilots. Given the tremendous thrust-to-weight ratio available on these small machines [1, 2], linear control approaches have recently produced impressive demonstrations that come close to matching this agility for a certain class of aerobatic maneuvers where the rotor or propeller forces dominate the dynamics of the aircraft [3, 4, 5]. However, as our flying machines scale down to smaller sizes (e.g. Micro Aerial Vehicles) operating at low Reynold's numbers, viscous forces dominate propeller thrust [6, 7, 8], causing classical control (and design) techniques to fail. These new technologies will require a different approach to control, where the control system will need to reason about the long term and time dependent effects of the unsteady fluid dynamics on the response of the vehicle. Perching is representative of a large class of control problems for aerobatics that requires and agile and robust control system with the capability of planning well into the future. Our experimental paradigm along with the simplicity of the problem structure has allowed us to study the problem at the most fundamental level. This thesis presents methods and results for identifying an aerodynamic model of a small glider at very high angles-of-attack using tools from supervised machine learning and system identification. Our model then serves as a benchmark platform for studying control of perching using an optimal control framework, namely reinforcement learning. Our results indicate that a compact parameterization of the control is sufficient to successfully execute the task in simulation.by Rick E. Cory.S.M
Model Predictive Control for Micro Aerial Vehicles: A Survey
This paper presents a review of the design and application of model
predictive control strategies for Micro Aerial Vehicles and specifically
multirotor configurations such as quadrotors. The diverse set of works in the
domain is organized based on the control law being optimized over linear or
nonlinear dynamics, the integration of state and input constraints, possible
fault-tolerant design, if reinforcement learning methods have been utilized and
if the controller refers to free-flight or other tasks such as physical
interaction or load transportation. A selected set of comparison results are
also presented and serve to provide insight for the selection between linear
and nonlinear schemes, the tuning of the prediction horizon, the importance of
disturbance observer-based offset-free tracking and the intrinsic robustness of
such methods to parameter uncertainty. Furthermore, an overview of recent
research trends on the combined application of modern deep reinforcement
learning techniques and model predictive control for multirotor vehicles is
presented. Finally, this review concludes with explicit discussion regarding
selected open-source software packages that deliver off-the-shelf model
predictive control functionality applicable to a wide variety of Micro Aerial
Vehicle configurations
Proceedings of the International Micro Air Vehicles Conference and Flight Competition 2017 (IMAV 2017)
The IMAV 2017 conference has been held at ISAE-SUPAERO, Toulouse, France from Sept. 18 to Sept. 21, 2017. More than 250 participants coming from 30 different countries worldwide have presented their latest research activities in the field of drones. 38 papers have been presented during the conference including various topics such as Aerodynamics, Aeroacoustics, Propulsion, Autopilots, Sensors, Communication systems, Mission planning techniques, Artificial Intelligence, Human-machine cooperation as applied to drones
NeuroBEM: Hybrid Aerodynamic Quadrotor Model
Quadrotors are extremely agile, so much in fact, that classic
first-principle-models come to their limits. Aerodynamic effects, while
insignificant at low speeds, become the dominant model defect during high
speeds or agile maneuvers. Accurate modeling is needed to design robust
high-performance control systems and enable flying close to the platform's
physical limits. We propose a hybrid approach fusing first principles and
learning to model quadrotors and their aerodynamic effects with unprecedented
accuracy. First principles fail to capture such aerodynamic effects, rendering
traditional approaches inaccurate when used for simulation or controller
tuning. Data-driven approaches try to capture aerodynamic effects with blackbox
modeling, such as neural networks; however, they struggle to robustly
generalize to arbitrary flight conditions. Our hybrid approach unifies and
outperforms both first-principles blade-element theory and learned residual
dynamics. It is evaluated in one of the world's largest motion-capture systems,
using autonomous-quadrotor-flight data at speeds up to 65km/h. The resulting
model captures the aerodynamic thrust, torques, and parasitic effects with
astonishing accuracy, outperforming existing models with 50% reduced prediction
errors, and shows strong generalization capabilities beyond the training set.Comment: 9 pages + 1 pages reference
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