83 research outputs found
AutoTrans: A Complete Planning and Control Framework for Autonomous UAV Payload Transportation
The robotics community is increasingly interested in autonomous aerial
transportation. Unmanned aerial vehicles with suspended payloads have
advantages over other systems, including mechanical simplicity and agility, but
pose great challenges in planning and control. To realize fully autonomous
aerial transportation, this paper presents a systematic solution to address
these difficulties. First, we present a real-time planning method that
generates smooth trajectories considering the time-varying shape and non-linear
dynamics of the system, ensuring whole-body safety and dynamic feasibility.
Additionally, an adaptive NMPC with a hierarchical disturbance compensation
strategy is designed to overcome unknown external perturbations and inaccurate
model parameters. Extensive experiments show that our method is capable of
generating high-quality trajectories online, even in highly constrained
environments, and tracking aggressive flight trajectories accurately, even
under significant uncertainty. We plan to release our code to benefit the
community.Comment: Accepted by IEEE Robotics and Automation Letter
Trajectory Generation and Tracking Control for Aggressive Tail-Sitter Flights
We address the theoretical and practical problems related to the trajectory
generation and tracking control of tail-sitter UAVs. Theoretically, we focus on
the differential flatness property with full exploitation of actual UAV
aerodynamic models, which lays a foundation for generating dynamically feasible
trajectory and achieving high-performance tracking control. We have found that
a tail-sitter is differentially flat with accurate aerodynamic models within
the entire flight envelope, by specifying coordinate flight condition and
choosing the vehicle position as the flat output. This fundamental property
allows us to fully exploit the high-fidelity aerodynamic models in the
trajectory planning and tracking control to achieve accurate tail-sitter
flights. Particularly, an optimization-based trajectory planner for
tail-sitters is proposed to design high-quality, smooth trajectories with
consideration of kinodynamic constraints, singularity-free constraints and
actuator saturation. The planned trajectory of flat output is transformed to
state trajectory in real-time with consideration of wind in environments. To
track the state trajectory, a global, singularity-free, and
minimally-parameterized on-manifold MPC is developed, which fully leverages the
accurate aerodynamic model to achieve high-accuracy trajectory tracking within
the whole flight envelope. The effectiveness of the proposed framework is
demonstrated through extensive real-world experiments in both indoor and
outdoor field tests, including agile SE(3) flight through consecutive narrow
windows requiring specific attitude and with speed up to 10m/s, typical
tail-sitter maneuvers (transition, level flight and loiter) with speed up to
20m/s, and extremely aggressive aerobatic maneuvers (Wingover, Loop, Vertical
Eight and Cuban Eight) with acceleration up to 2.5g
Backflipping with Miniature Quadcopters by Gaussian Process Based Control and Planning
The paper proposes two control methods for performing a backflip maneuver
with miniature quadcopters. First, an existing feedforward control approach is
improved by finding the optimal sequence of motion primitives via Bayesian
optimization, using a surrogate Gaussian Process model. To evaluate the cost
function, the flip maneuver is performed repeatedly in a simulation
environment. The second method is based on closed-loop control and it consists
of two main steps: first a novel robust, adaptive controller is designed to
provide reliable reference tracking even in case of model uncertainties. The
controller is constructed by augmenting the nominal model of the drone with a
Gaussian Process that is trained by using measurement data. Second, an
efficient trajectory planning algorithm is proposed, which designs feasible
trajectories for the flip maneuver by using only quadratic programming. The two
approaches are analyzed in simulations and in real experiments using Bitcraze
Crazyflie 2.1 quadcopters.Comment: Submitted to IEEE Transactions on Control Systems Technology (2022
Data-Driven MPC for Quadrotors
Aerodynamic forces render accurate high-speed trajectory tracking with
quadrotors extremely challenging. These complex aerodynamic effects become a
significant disturbance at high speeds, introducing large positional tracking
errors, and are extremely difficult to model. To fly at high speeds, feedback
control must be able to account for these aerodynamic effects in real-time.
This necessitates a modelling procedure that is both accurate and efficient to
evaluate. Therefore, we present an approach to model aerodynamic effects using
Gaussian Processes, which we incorporate into a Model Predictive Controller to
achieve efficient and precise real-time feedback control, leading to up to 70%
reduction in trajectory tracking error at high speeds. We verify our method by
extensive comparison to a state-of-the-art linear drag model in synthetic and
real-world experiments at speeds of up to 14m/s and accelerations beyond 4g.Comment: 8 page
Sampling-based Motion Planning for Active Multirotor System Identification
This paper reports on an algorithm for planning trajectories that allow a
multirotor micro aerial vehicle (MAV) to quickly identify a set of unknown
parameters. In many problems like self calibration or model parameter
identification some states are only observable under a specific motion. These
motions are often hard to find, especially for inexperienced users. Therefore,
we consider system model identification in an active setting, where the vehicle
autonomously decides what actions to take in order to quickly identify the
model. Our algorithm approximates the belief dynamics of the system around a
candidate trajectory using an extended Kalman filter (EKF). It uses
sampling-based motion planning to explore the space of possible beliefs and
find a maximally informative trajectory within a user-defined budget. We
validate our method in simulation and on a real system showing the feasibility
and repeatability of the proposed approach. Our planner creates trajectories
which reduce model parameter convergence time and uncertainty by a factor of
four.Comment: Published at ICRA 2017. Video available at
https://www.youtube.com/watch?v=xtqrWbgep5
Safe and accurate MAV Control, navigation and manipulation
This work focuses on the problem of precise, aggressive and safe Micro Aerial Vehicle (MAV) navigation as well as deployment in applications which require physical interaction with the environment. To address these issues, we propose three different MAV model based control algorithms that rely on the concept of receding horizon control. As a starting point, we present a computationally cheap algorithm which utilizes an approximate linear model of the system around hover and is thus maximally accurate for slow reference maneuvers. Aiming at overcoming the limitations of the linear model parameterisation, we present an extension to the first controller which relies on the true nonlinear dynamics of the system. This approach, even though computationally more intense, ensures that the control model is always valid and allows tracking of full state aggressive trajectories. The last controller addresses the topic of aerial manipulation in which the versatility of
aerial vehicles is combined with the manipulation capabilities of robotic arms. The proposed method relies on the formulation of a hybrid nonlinear MAV-arm
model which also takes into account the effects of contact with the environment. Finally, in order to enable safe operation despite the potential loss of an
actuator, we propose a supervisory algorithm which estimates the health status of each motor. We further showcase how this can be used in conjunction with
the nonlinear controllers described above for fault tolerant MAV flight. While all the developed algorithms are formulated and tested using our specific MAV platforms (consisting of underactuated hexacopters for the free flight experiments, hexacopter-delta arm system for the manipulation experiments),
we further discuss how these can be applied to other underactuated/overactuated MAVs and robotic arm platforms. The same applies to the fault tolerant
control where we discuss different stabilisation techniques depending on the capabilities of the available hardware. Even though the primary focus of this work is on feedback control, we thoroughly describe the custom hardware platforms used for the experimental evaluation, the state estimation algorithms which provide the basis for control
as well as the parameter identification required for the formulation of the various control models.
We showcase all the developed algorithms in experimental scenarios designed to highlight the corresponding strengths and weaknesses as well as show that the proposed methods can run in realtime on commercially available hardware.Open Acces
Optimization-based iterative learning for precise quadrocopter trajectory tracking
Current control systems regulate the behavior of dynamic systems by reacting to noise and unexpected disturbances as they occur. To improve the performance of such control systems, experience from iterative executions can be used to anticipate recurring disturbances and proactively compensate for them. This paper presents an algorithm that exploits data from previous repetitions in order to learn to precisely follow a predefined trajectory. We adapt the feed-forward input signal to the system with the goal of achieving high tracking performance—even under the presence of model errors and other recurring disturbances. The approach is based on a dynamics model that captures the essential features of the system and that explicitly takes system input and state constraints into account. We combine traditional optimal filtering methods with state-of-the-art optimization techniques in order to obtain an effective and computationally efficient learning strategy that updates the feed-forward input signal according to a customizable learning objective. It is possible to define a termination condition that stops an execution early if the deviation from the nominal trajectory exceeds a given bound. This allows for a safe learning that gradually extends the time horizon of the trajectory. We developed a framework for generating arbitrary flight trajectories and for applying the algorithm to highly maneuverable autonomous quadrotor vehicles in the ETH Flying Machine Arena testbed. Experimental results are discussed for selected trajectories and different learning algorithm parameter
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