7,341 research outputs found
Non-Linear Model Predictive Control with Adaptive Time-Mesh Refinement
In this paper, we present a novel solution for real-time, Non-Linear Model
Predictive Control (NMPC) exploiting a time-mesh refinement strategy. The
proposed controller formulates the Optimal Control Problem (OCP) in terms of
flat outputs over an adaptive lattice. In common approximated OCP solutions,
the number of discretization points composing the lattice represents a critical
upper bound for real-time applications. The proposed NMPC-based technique
refines the initially uniform time horizon by adding time steps with a sampling
criterion that aims to reduce the discretization error. This enables a higher
accuracy in the initial part of the receding horizon, which is more relevant to
NMPC, while keeping bounded the number of discretization points. By combining
this feature with an efficient Least Square formulation, our solver is also
extremely time-efficient, generating trajectories of multiple seconds within
only a few milliseconds. The performance of the proposed approach has been
validated in a high fidelity simulation environment, by using an UAV platform.
We also released our implementation as open source C++ code.Comment: In: 2018 IEEE International Conference on Simulation, Modeling, and
Programming for Autonomous Robots (SIMPAR 2018
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
Full-Body Torque-Level Non-linear Model Predictive Control for Aerial Manipulation
Non-linear model predictive control (nMPC) is a powerful approach to control
complex robots (such as humanoids, quadrupeds, or unmanned aerial manipulators
(UAMs)) as it brings important advantages over other existing techniques. The
full-body dynamics, along with the prediction capability of the optimal control
problem (OCP) solved at the core of the controller, allows to actuate the robot
in line with its dynamics. This fact enhances the robot capabilities and
allows, e.g., to perform intricate maneuvers at high dynamics while optimizing
the amount of energy used. Despite the many similarities between humanoids or
quadrupeds and UAMs, full-body torque-level nMPC has rarely been applied to
UAMs.
This paper provides a thorough description of how to use such techniques in
the field of aerial manipulation. We give a detailed explanation of the
different parts involved in the OCP, from the UAM dynamical model to the
residuals in the cost function. We develop and compare three different nMPC
controllers: Weighted MPC, Rail MPC, and Carrot MPC, which differ on the
structure of their OCPs and on how these are updated at every time step. To
validate the proposed framework, we present a wide variety of simulated case
studies. First, we evaluate the trajectory generation problem, i.e., optimal
control problems solved offline, involving different kinds of motions (e.g.,
aggressive maneuvers or contact locomotion) for different types of UAMs. Then,
we assess the performance of the three nMPC controllers, i.e., closed-loop
controllers solved online, through a variety of realistic simulations. For the
benefit of the community, we have made available the source code related to
this work.Comment: Submitted to Transactions on Robotics. 17 pages, 16 figure
A review of aerial manipulation of small-scale rotorcraft unmanned robotic systems
Small-scale rotorcraft unmanned robotic systems (SRURSs) are a kind of unmanned rotorcraft with manipulating devices. This review aims to provide an overview on aerial manipulation of SRURSs nowadays and promote relative research in the future. In the past decade, aerial manipulation of SRURSs has attracted the interest of researchers globally. This paper provides a literature review of the last 10 years (2008â2017) on SRURSs, and details achievements and challenges. Firstly, the definition, current state, development, classification, and challenges of SRURSs are introduced. Then, related papers are organized into two topical categories: mechanical structure design, and modeling and control. Following this, research groups involved in SRURS research and their major achievements are summarized and classified in the form of tables. The research groups are introduced in detail from seven parts. Finally, trends and challenges are compiled and presented to serve as a resource for researchers interested in aerial manipulation of SRURSs. The problem, trends, and challenges are described from three aspects. Conclusions of the paper are presented, and the future of SRURSs is discussed to enable further research interests
Adaptive management of Ramsar wetlands
Abstract
The Macquarie Marshes are one of Australiaâs iconic wetlands, recognised for their international importance, providing habitat for some of the continentâs more important waterbird breeding sites as well as complex and extensive flood-dependent vegetation communities. Part of the area is recognised as a wetland of international importance, under the Ramsar Convention. River regulation has affected their resilience, which may increase with climate change. Counteracting these impacts, the increased amount of environmental flow provided to the wetland through the buy-back and increased wildlife allocation have redressed some of the impacts of river regulation.
This project assists in the development of an adaptive management framework for this Ramsar-listed wetland. It brings together current management and available science to provide an informed hierarchy of objectives that incorporates climate change adaptation and assists transparent management. The project adopts a generic approach allowing the framework to be transferred to other wetlands, including Ramsar-listed wetlands, supplied by rivers ranging from highly regulated to free flowing.
The integration of management with science allows key indicators to be monitored that will inform management and promote increasingly informed decisions. The project involved a multi-disciplinary team of scientists and managers working on one of the more difficult challenges for Australia, exacerbated by increasing impacts of climate change on flows and inundation patterns
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