6,521 research outputs found
Adaptive sampling for UAV sensor network in oil spill management
In this paper we propose a method for adaptive sampling using Unmanned Aerial Vehicles (UAVs) in oil spill management. The goal is to measure and estimate oil spill concentrations at the sea surface, while at the same time identify the leak rates of sources at known positions. First we construct a cost which approximates the benefit of sampling locations at specific times. This cost is based on measures of observability and of persistency of excitation for the oil spill model. A receding horizon Mixed-Integer Linear Programming (MILP) problem is solved in order to find UAV trajectories which are optimal with respect to the cost. For UAV trajectory tracking we use a Lyapunov based controller. The oil spill concentration measurements taken by the UAVs by following these tracks are used in an adaptive observer, which provides state (concentration) and parameter (leak rate) estimates. Under the assumption that the sampling strategy described above lead to uniform complete observability and persistency of excitation, we prove Uniform Global Asymptotic Stability (UGAS) of the state estimation, parameter identification and UAV trajectory tracking errors. Finally, we provide a simulation of the proposed strategy, and compare it with two other strategies.acceptedVersio
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
Nonlinear predictive control of autonomous soaring UAVs using 3DOF models
We design a nonlinear model predictive control (NMPC) system for a soaring UAV in order to harvest the energy from the atmospheric updrafts. Our control framework combines an online estimation with a heuristic search method to obtain the UAV optimal trajectory. To allow for real-time computation of the control commands we solve the optimal control problem using a 3 degrees-of-freedom (DOF) model but apply the inputs to a more realistic 6DOF model. Hence, we design a 3DOF-6DOF model interaction strategy. Simulations show how the control system succeeds in energy extraction in a challenging dynamic atmospheric environment while satisfying its real-time contraints
Obstacle-aware Adaptive Informative Path Planning for UAV-based Target Search
Target search with unmanned aerial vehicles (UAVs) is relevant problem to
many scenarios, e.g., search and rescue (SaR). However, a key challenge is
planning paths for maximal search efficiency given flight time constraints. To
address this, we propose the Obstacle-aware Adaptive Informative Path Planning
(OA-IPP) algorithm for target search in cluttered environments using UAVs. Our
approach leverages a layered planning strategy using a Gaussian Process
(GP)-based model of target occupancy to generate informative paths in
continuous 3D space. Within this framework, we introduce an adaptive replanning
scheme which allows us to trade off between information gain, field coverage,
sensor performance, and collision avoidance for efficient target detection.
Extensive simulations show that our OA-IPP method performs better than
state-of-the-art planners, and we demonstrate its application in a realistic
urban SaR scenario.Comment: Paper accepted for International Conference on Robotics and
Automation (ICRA-2019) to be held at Montreal, Canad
Vision-model-based Real-time Localization of Unmanned Aerial Vehicle for Autonomous Structure Inspection under GPS-denied Environment
UAVs have been widely used in visual inspections of buildings, bridges and
other structures. In either outdoor autonomous or semi-autonomous flights
missions strong GPS signal is vital for UAV to locate its own positions.
However, strong GPS signal is not always available, and it can degrade or fully
loss underneath large structures or close to power lines, which can cause
serious control issues or even UAV crashes. Such limitations highly restricted
the applications of UAV as a routine inspection tool in various domains. In
this paper a vision-model-based real-time self-positioning method is proposed
to support autonomous aerial inspection without the need of GPS support.
Compared to other localization methods that requires additional onboard
sensors, the proposed method uses a single camera to continuously estimate the
inflight poses of UAV. Each step of the proposed method is discussed in detail,
and its performance is tested through an indoor test case.Comment: 8 pages, 5 figures, submitted to i3ce 201
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