2,440 research outputs found
A Robust Model Predictive Control Approach for Autonomous Underwater Vehicles Operating in a Constrained workspace
This paper presents a novel Nonlinear Model Predictive Control (NMPC) scheme
for underwater robotic vehicles operating in a constrained workspace including
static obstacles. The purpose of the controller is to guide the vehicle towards
specific way points. Various limitations such as: obstacles, workspace
boundary, thruster saturation and predefined desired upper bound of the vehicle
velocity are captured as state and input constraints and are guaranteed during
the control design. The proposed scheme incorporates the full dynamics of the
vehicle in which the ocean currents are also involved. Hence, the control
inputs calculated by the proposed scheme are formulated in a way that the
vehicle will exploit the ocean currents, when these are in favor of the
way-point tracking mission which results in reduced energy consumption by the
thrusters. The performance of the proposed control strategy is experimentally
verified using a Degrees of Freedom (DoF) underwater robotic vehicle inside
a constrained test tank with obstacles.Comment: IEEE International Conference on Robotics and Automation (ICRA-2018),
Accepte
An Innovative Mission Management System for Fixed-Wing UAVs
This paper presents two innovative units linked together to build the main frame of a UAV Mis- sion Management System. The first unit is a Path Planner for small UAVs able to generate optimal paths in a tridimensional environment, generat- ing flyable and safe paths with the lowest com- putational effort. The second unit is the Flight Management System based on Nonlinear Model Predictive Control, that tracks the reference path and exploits a spherical camera model to avoid unpredicted obstacles along the path. The control system solves on-line (i.e. at each sampling time) a finite horizon (state horizon) open loop optimal control problem with a Genetic Algorithm. This algorithm finds the command sequence that min- imizes the tracking error with respect to the ref- erence path, driving the aircraft far from sensed obstacles and towards the desired trajectory
Nonlinear model predictive control-based guidance law for path following of unmanned surface vehicles
This work proposes a nonlinear model predictive control-based guidance
strategy for unmanned surface vehicles, focused on path following. The
application of this strategy, in addition to overcome drawbacks of previous
line-of-sight-based guidance laws, intends to enable the application of
predictive strategies also to the low-level control, responsible for tracking
the references provided by the guidance strategy. The stability and robustness
of the proposed strategy are theoretically discussed. Furthermore, given the
non-negligible computational cost of such nonlinear predictive guidance
strategy, a practical nonlinear model predictive control strategy is also
applied in order to reduce the computational cost to a great extent. The
effectiveness and advantages of both proposed strategies over other nonlinear
guidance laws are illustrated through a complete set of simulations.Comment: 21 pages, 15 figures. Postprint of the final published wor
Distributed Model Predictive Control for Cooperative Multirotor Landing on Uncrewed Surface Vessel in Waves
Heterogeneous autonomous robot teams consisting of multirotor and uncrewed
surface vessels (USVs) have the potential to enable various maritime
applications, including advanced search-and-rescue operations. A critical
requirement of these applications is the ability to land a multirotor on a USV
for tasks such as recharging. This paper addresses the challenge of safely
landing a multirotor on a cooperative USV in harsh open waters. To tackle this
problem, we propose a novel sequential distributed model predictive control
(MPC) scheme for cooperative multirotor-USV landing. Our approach combines
standard tracking MPCs for the multirotor and USV with additional artificial
intermediate goal locations. These artificial goals enable the robots to
coordinate their cooperation without prior guidance. Each vehicle solves an
individual optimization problem for both the artificial goal and an input that
tracks it but only communicates the former to the other vehicle. The artificial
goals are penalized by a suitable coupling cost. Furthermore, our proposed
distributed MPC scheme utilizes a spatial-temporal wave model to coordinate in
real-time a safer landing location and time the multirotor's landing to limit
severe tilt of the USV
Non-linear control algorithms for an unmanned surface vehicle
Although intrinsically marine craft are known to exhibit non-linear dynamic characteristics, modern marine autopilot system designs continue to be developed based on both linear and non-linear control approaches. This article evaluates two novel non-linear autopilot designs based on non-linear local control network and non-linear model predictive control approaches to establish their effectiveness in terms of control activity expenditure, power consumption and mission duration length under similar operating conditions. From practical point of view, autopilot with less energy consumption would in reality provide the battery-powered vehicle with longer mission duration. The autopilot systems are used to control the non-linear yaw dynamics of an unmanned surface vehicle named Springer. The yaw dynamics of the vehicle being modelled using a multi-layer perceptron-type neural network. Simulation results showed that the autopilot based on local control network method performed better for Springer. Furthermore, on the whole, the local control network methodology can be regarded as a plausible paradigm for marine control system design. © 2014 IMechE
A review of path following control strategies for autonomous robotic vehicles: theory, simulations, and experiments
This article presents an in-depth review of the topic of path following for
autonomous robotic vehicles, with a specific focus on vehicle motion in two
dimensional space (2D). From a control system standpoint, path following can be
formulated as the problem of stabilizing a path following error system that
describes the dynamics of position and possibly orientation errors of a vehicle
with respect to a path, with the errors defined in an appropriate reference
frame. In spite of the large variety of path following methods described in the
literature we show that, in principle, most of them can be categorized in two
groups: stabilization of the path following error system expressed either in
the vehicle's body frame or in a frame attached to a "reference point" moving
along the path, such as a Frenet-Serret (F-S) frame or a Parallel Transport
(P-T) frame. With this observation, we provide a unified formulation that is
simple but general enough to cover many methods available in the literature. We
then discuss the advantages and disadvantages of each method, comparing them
from the design and implementation standpoint. We further show experimental
results of the path following methods obtained from field trials testing with
under-actuated and fully-actuated autonomous marine vehicles. In addition, we
introduce open-source Matlab and Gazebo/ROS simulation toolboxes that are
helpful in testing path following methods prior to their integration in the
combined guidance, navigation, and control systems of autonomous vehicles
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