321 research outputs found
Contact-Aided Invariant Extended Kalman Filtering for Legged Robot State Estimation
This paper derives a contact-aided inertial navigation observer for a 3D
bipedal robot using the theory of invariant observer design. Aided inertial
navigation is fundamentally a nonlinear observer design problem; thus, current
solutions are based on approximations of the system dynamics, such as an
Extended Kalman Filter (EKF), which uses a system's Jacobian linearization
along the current best estimate of its trajectory. On the basis of the theory
of invariant observer design by Barrau and Bonnabel, and in particular, the
Invariant EKF (InEKF), we show that the error dynamics of the point
contact-inertial system follows a log-linear autonomous differential equation;
hence, the observable state variables can be rendered convergent with a domain
of attraction that is independent of the system's trajectory. Due to the
log-linear form of the error dynamics, it is not necessary to perform a
nonlinear observability analysis to show that when using an Inertial
Measurement Unit (IMU) and contact sensors, the absolute position of the robot
and a rotation about the gravity vector (yaw) are unobservable. We further
augment the state of the developed InEKF with IMU biases, as the online
estimation of these parameters has a crucial impact on system performance. We
evaluate the convergence of the proposed system with the commonly used
quaternion-based EKF observer using a Monte-Carlo simulation. In addition, our
experimental evaluation using a Cassie-series bipedal robot shows that the
contact-aided InEKF provides better performance in comparison with the
quaternion-based EKF as a result of exploiting symmetries present in the system
dynamics.Comment: Published in the proceedings of Robotics: Science and Systems 201
Suspended Load Path Tracking Control Using a Tilt-rotor UAV Based on Zonotopic State Estimation
This work addresses the problem of path tracking control of a suspended load
using a tilt-rotor UAV. The main challenge in controlling this kind of system
arises from the dynamic behavior imposed by the load, which is usually coupled
to the UAV by means of a rope, adding unactuated degrees of freedom to the
whole system. Furthermore, to perform the load transportation it is often
needed the knowledge of the load position to accomplish the task. Since
available sensors are commonly embedded in the mobile platform, information on
the load position may not be directly available. To solve this problem in this
work, initially, the kinematics of the multi-body mechanical system are
formulated from the load's perspective, from which a detailed dynamic model is
derived using the Euler-Lagrange approach, yielding a highly coupled, nonlinear
state-space representation of the system, affine in the inputs, with the load's
position and orientation directly represented by state variables. A zonotopic
state estimator is proposed to solve the problem of estimating the load
position and orientation, which is formulated based on sensors located at the
aircraft, with different sampling times, and unknown-but-bounded measurement
noise. To solve the path tracking problem, a discrete-time mixed
controller with pole-placement constraints
is designed with guaranteed time-response properties and robust to unmodeled
dynamics, parametric uncertainties, and external disturbances. Results from
numerical experiments, performed in a platform based on the Gazebo simulator
and on a Computer Aided Design (CAD) model of the system, are presented to
corroborate the performance of the zonotopic state estimator along with the
designed controller
Invariant EKF Design for Scan Matching-aided Localization
Localization in indoor environments is a technique which estimates the
robot's pose by fusing data from onboard motion sensors with readings of the
environment, in our case obtained by scan matching point clouds captured by a
low-cost Kinect depth camera. We develop both an Invariant Extended Kalman
Filter (IEKF)-based and a Multiplicative Extended Kalman Filter (MEKF)-based
solution to this problem. The two designs are successfully validated in
experiments and demonstrate the advantage of the IEKF design
Accelerometers on Quadrotors : What do they Really Measure?
International audienceA revisited quadrotor model is proposed, including the so-called rotor drag. It differs from the model usually considered, even at first order, and much better explains the role of accelerometer feedback in control algorithms. The theoretical derivation is supported by experimental data
Observer-based Controller for VTOL-UAVs Tracking using Direct Vision-Aided Inertial Navigation Measurements
This paper proposes a novel observer-based controller for Vertical Take-Off
and Landing (VTOL) Unmanned Aerial Vehicle (UAV) designed to directly receive
measurements from a Vision-Aided Inertial Navigation System (VA-INS) and
produce the required thrust and rotational torque inputs. The VA-INS is
composed of a vision unit (monocular or stereo camera) and a typical low-cost
6-axis Inertial Measurement Unit (IMU) equipped with an accelerometer and a
gyroscope. A major benefit of this approach is its applicability for
environments where the Global Positioning System (GPS) is inaccessible. The
proposed VTOL-UAV observer utilizes IMU and feature measurements to accurately
estimate attitude (orientation), gyroscope bias, position, and linear velocity.
Ability to use VA-INS measurements directly makes the proposed observer design
more computationally efficient as it obviates the need for attitude and
position reconstruction. Once the motion components are estimated, the
observer-based controller is used to control the VTOL-UAV attitude, angular
velocity, position, and linear velocity guiding the vehicle along the desired
trajectory in six degrees of freedom (6 DoF). The closed-loop estimation and
the control errors of the observer-based controller are proven to be
exponentially stable starting from almost any initial condition. To achieve
global and unique VTOL-UAV representation in 6 DoF, the proposed approach is
posed on the Lie Group and the design in unit-quaternion is presented. Although
the proposed approach is described in a continuous form, the discrete version
is provided and tested. Keywords: Vision-aided inertial navigation system,
unmanned aerial vehicle, vertical take-off and landing, stochastic, noise,
Robotics, control systems, air mobility, observer-based controller algorithm,
landmark measurement, exponential stability
The True Role of Accelerometer Feedback in Quadrotor Control
A revisited quadrotor model is proposed, including in particular the so-called rotor drag. It differs from the model usually considered, even at first order, and much better explains the role of accelerometer feedback in control algorithms. The theoretical derivation is supported by experimental data
Automatic Flight Control Systems
The history of flight control is inseparably linked to the history of aviation itself. Since the early days, the concept of automatic flight control systems has evolved from mechanical control systems to highly advanced automatic fly-by-wire flight control systems which can be found nowadays in military jets and civil airliners. Even today, many research efforts are made for the further development of these flight control systems in various aspects. Recent new developments in this field focus on a wealth of different aspects. This book focuses on a selection of key research areas, such as inertial navigation, control of unmanned aircraft and helicopters, trajectory control of an unmanned space re-entry vehicle, aeroservoelastic control, adaptive flight control, and fault tolerant flight control. This book consists of two major sections. The first section focuses on a literature review and some recent theoretical developments in flight control systems. The second section discusses some concepts of adaptive and fault-tolerant flight control systems. Each technique discussed in this book is illustrated by a relevant example
Cascaded Model Predictive Control of a Tandem-Rotor Helicopter
This letter considers cascaded model predictive control (MPC) as a
computationally lightweight method for controlling a tandem-rotor helicopter. A
traditional single MPC structure is split into separate outer and inner-loops.
The outer-loop MPC uses an error to linearize the translational
dynamics about a reference trajectory. The inner-loop MPC uses the optimal
angular velocity sequence of the outer-loop MPC to linearize the rotational
dynamics. The outer-loop MPC is run at a slower rate than the inner-loop
allowing for longer prediction time and improved performance. Monte-Carlo
simulations demonstrate robustness to model uncertainty and environmental
disturbances. The proposed control structure is benchmarked against a single
MPC algorithm where it shows significant improvements in position and velocity
tracking while using significantly less computational resources.Comment: 6 pages, 3 figure
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