Real-time estimation of gas concentration released from a moving source using an unmanned aerial vehicle

This work presents an approach which provides the real-time estimation of the gas concentration in a plume using an unmanned aerial vehicle (UAV) equipped with concentration sensors. The plume is assumed to be generated by a moving aerial or ground source with unknown strength and location, and is modeled by the unsteady advection-diffusion equation with ambient winds and eddy diffusivities. The UAV dynamics is described using the point-mass model of a fixed-wing aircraft resulting in a sixth-order nonlinear dynamical system. The state (gas concentration) estimator takes the form of a Luenberger observer based on the advection-diffusion equation. The UAV in the approach is guided towards the region with the larger state-estimation error via an appropriate choice of a Lyapunov function thus coupling the UAV guidance with the performance of the gas concentration estimator. This coupled controls-CFD guidance scheme provides the desired Cartesian velocities for the UAV and based on these velocities a lower-level controller processes the control signals that are transmitted to the UAV. The finite-volume discretization of the estimator incorporates a second-order total variation diminishing (TVD) scheme for the advection term. For computational efficiency needed in real-time applications, a dynamic grid adaptation for the estimator with local grid-refinement centered at the UAV location is proposed. The approach is tested numerically for several source trajectories using existing specifications for the UAV considered. The estimated plumes are compared with simulated concentration data. The estimator performance is analyzed by the behavior of the RMS error of the concentration and the distance between the sensor and the source

Estimation of the Concentration from a Moving Gaseous Source in the Atmosphere Using a Guided Sensing Aerial Vehicle

The estimation of the gas concentration (process-state) associated with a stationary or moving source using a sensing aerial vehicle (SAV) is considered. The dispersion from such a gaseous source into the ambient atmosphere is representative of an accidental or deliberate release of chemicals, or a release of gases from biological systems. Estimation of the concentration field provides a superior ability for source localization, assessment of possible adverse impacts, and eventual containment. The abstract and finite-dimensional approximation framework presented couples theoretical estimation and control with computational fluid dynamics methods. The gas dispersion (process) model is based on the advection-diffusion equation with variable eddy diffusivities and ambient winds. Cases are considered for a 2D and 3D domain. The state estimator is a modified Luenberger observer with a €�collocated€� filter gain that is parameterized by the position of the SAV. The process-state (concentration) estimator is based on a 2D and 3D adaptive, multigrid, multi-step finite-volume method. The grid is adapted with local refinement and coarsening during the process-state estimation in order to improve accuracy and efficiency. The motion dynamics of the SAV are incorporated into the spatial process and the SAV€™s guidance is directly linked to the performance of the state estimator. The computational model and the state estimator are coupled in the sense that grid-refinement is affected by the SAV repositioning, and the guidance laws of the SAV are affected by grid-refinement. Extensive numerical experiments serve to demonstrate the effectiveness of the coupled approach

Design of a Plume Generation and Detection Systems

The project presents the conceptual design of plume generation and detection systems for ground experiments with sensing robots. The plume generation system provides controlled carbon dioxide concentration profiles and consists of a pressurized tank, a pressure regulator, a flow meter, and a nozzle placed on a stand. The carbon dioxide plume is modeled with the 3d advection diffusion equation and numerical simulations provide the required release rates at the nozzle exit. Nozzle dimensions are estimated using 1d isentropic nozzle theory. The plume detection system consists of three carbon dioxide sensors placed on a horizontal arm that can be repositioned vertically on a stand. Structural analysis is performed for the plume generation and detection stands in order to minimum deflections

Design and Integration of an Indoor Plume Experimental Setup

This project involves the design and integration of a carbon dioxide plume generation system, a plume detection system, and a data acquisitions system for indoor plume experiments. The design of these systems is aided by 3D simulations of carbon dioxide plumes from a stationary source and a known release rate. The plume generation system is designed using SolidWorks and consists of a stand with a pressurized tank, a regulator, a solenoid valve, a mass flow controller and a nozzle. The plume detection system is designed with SolidWorks and consists of carbon dioxide sensors and wind speed sensors placed on a horizontal arm that can be re-positioned vertically on a stand. The systems are controlled using LabVIEW implemented on a data acquisition computer stand

3-D Velocity Regulation for Nonholonomic Source Seeking Without Position Measurement

We consider a three-dimensional problem of steering a nonholonomic vehicle to seek an unknown source of a spatially distributed signal field without any position measurement. In the literature, there exists an extremum seeking-based strategy under a constant forward velocity and tunable pitch and yaw velocities. Obviously, the vehicle with a constant forward velocity may exhibit certain overshoots in the seeking process and can not slow down even it approaches the source. To resolve this undesired behavior, this paper proposes a regulation strategy for the forward velocity along with the pitch and yaw velocities. Under such a strategy, the vehicle slows down near the source and stays within a small area as if it comes to a full stop, and controllers for angular velocities become succinct. We prove the local exponential convergence via the averaging technique. Finally, the theoretical results are illustrated with simulations.Comment: submitted to IEEE TCST;12 pages, 10 figure

Plume Analysis and Detection

This work involves the design and implementation of a gas-sensing mobile robot as an experimental tool to reconstruct a carbon dioxide plume in real-time based on concentration gradient and local wind speed measurements. The autonomous robot, an iRobot Create 2, achieves navigation through an embedded micro-controller using on-board sensors and various sensor fusion methods. A mass flow controller and diffuser are used to dependably generate a plume that simulates a point source. A base station reconstructs the plume via a state estimator through data from the robot and transmits commands to guide it into spatial regions of interest. This method has applicability for unmanned vehicles tracking emissions of contaminants and their effects in the environment

Gas Source Localization with a Mobile Sensing Ground Vehicle

The project focuses on the development of an experiment for an olfactory terrain vehicle localizing a moving gas source inside an enclosed environment using gas, airflow, and proximity sensors. The experiment simulates the movement of an unmanned air vehicle (UAV) tracing the source of a leaking gas from another moving aircraft. A literature review was conducted to aid in the understanding of technologies and processes that have been used in similar experiments. The main accomplishments of the project include the selection of major design components such as the gas, robot, and appropriate gas sensors. Other accomplishments include the design and manufacturing of a sensor mount as well as the development of a robot motion control algorithm using Matlab and Simulink code and simulations

Volume 1 – Symposium

We are pleased to present the conference proceedings for the 12th edition of the International Fluid Power Conference (IFK). The IFK is one of the world’s most significant scientific conferences on fluid power control technology and systems. It offers a common platform for the presentation and discussion of trends and innovations to manufacturers, users and scientists. The Chair of Fluid-Mechatronic Systems at the TU Dresden is organizing and hosting the IFK for the sixth time. Supporting hosts are the Fluid Power Association of the German Engineering Federation (VDMA), Dresdner Verein zur Förderung der Fluidtechnik e. V. (DVF) and GWT-TUD GmbH. The organization and the conference location alternates every two years between the Chair of Fluid-Mechatronic Systems in Dresden and the Institute for Fluid Power Drives and Systems in Aachen. The symposium on the first day is dedicated to presentations focused on methodology and fundamental research. The two following conference days offer a wide variety of application and technology orientated papers about the latest state of the art in fluid power. It is this combination that makes the IFK a unique and excellent forum for the exchange of academic research and industrial application experience. A simultaneously ongoing exhibition offers the possibility to get product information and to have individual talks with manufacturers. The theme of the 12th IFK is “Fluid Power – Future Technology”, covering topics that enable the development of 5G-ready, cost-efficient and demand-driven structures, as well as individual decentralized drives. Another topic is the real-time data exchange that allows the application of numerous predictive maintenance strategies, which will significantly increase the availability of fluid power systems and their elements and ensure their improved lifetime performance. We create an atmosphere for casual exchange by offering a vast frame and cultural program. This includes a get-together, a conference banquet, laboratory festivities and some physical activities such as jogging in Dresden’s old town.:Group A: Materials Group B: System design & integration Group C: Novel system solutions Group D: Additive manufacturing Group E: Components Group F: Intelligent control Group G: Fluids Group H | K: Pumps Group I | L: Mobile applications Group J: Fundamental

Interactive Planning and Sensing for Aircraft in Uncertain Environments with Spatiotemporally Evolving Threats

Autonomous aerial, terrestrial, and marine vehicles provide a platform for several applications including cargo transport, information gathering, surveillance, reconnaissance, and search-and-rescue. To enable such applications, two main technical problems are commonly addressed.On the one hand, the motion-planning problem addresses optimal motion to a destination: an application example is the delivery of a package in the shortest time with least fuel. Solutions to this problem often assume that all relevant information about the environment is available, possibly with some uncertainty. On the other hand, the information gathering problem addresses the maximization of some metric of information about the environment: application examples include such as surveillance and environmental monitoring. Solutions to the motion-planning problem in vehicular autonomy assume that information about the environment is available from three sources: (1) the vehicle’s own onboard sensors, (2) stationary sensor installations (e.g. ground radar stations), and (3) other information gathering vehicles, i.e., mobile sensors, especially with the recent emphasis on collaborative teams of autonomous vehicles with heterogeneous capabilities. Each source typically processes the raw sensor data via estimation algorithms. These estimates are then available to a decision making system such as a motion- planning algorithm. The motion-planner may use some or all of the estimates provided. There is an underlying assumption of “separation� between the motion-planning algorithm and the information about environment. This separation is common in linear feedback control systems, where estimation algorithms are designed independent of control laws, and control laws are designed with the assumption that the estimated state is the true state. In the case of motion-planning, there is no reason to believe that such a separation between the motion-planning algorithm and the sources of estimated environment information will lead to optimal motion plans, even if the motion planner and the estimators are themselves optimal. The goal of this dissertation is to investigate whether the removal of this separation, via interactive motion-planning and sensing, can significantly improve the optimality of motion- planning. The major contribution of this work is interactive planning and sensing. We consider the problem of planning the path of a vehicle, which we refer to as the actor, to traverse a threat field with minimum threat exposure. The threat field is an unknown, time- variant, and strictly positive scalar field defined on a compact 2D spatial domain – the actor’s workspace. The threat field is estimated by a network of mobile sensors that can measure the threat field pointwise. All measurements are noisy. The objective is to determine a path for the actor to reach a desired goal with minimum risk, which is a measure sensitive not only to the threat exposure itself, but also to the uncertainty therein. A novelty of this problem setup is that the actor can communicate with the sensor network and request that the sensors position themselves in a procedure we call sensor reconfiguration such that the actor’s risk is minimized. This work continues with a foundation in motion planning in time-varying fields where waiting is a control input. Waiting is examined in the context of finding an optimal path with considerations for the cost of exposure to a threat field, the cost of movement, and the cost of waiting. For example, an application where waiting may be beneficial in motion-planning is the delivery of a package where adverse weather may pose a risk to the safety of a UAV and its cargo. In such scenarios, an optimal plan may include “waiting until the storm passes.� Results on computational efficiency and optimality of considering waiting in path- planning algorithms are presented. In addition, the relationship of waiting in a time- varying field represented with varying levels of resolution, or multiresolution is studied. Interactive planning and sensing is further developed for the case of time-varying environments. This proposed extension allows for the evaluation of different mission windows, finite sensor network reconfiguration durations, finite planning durations, and varying number of available sensors. Finally, the proposed method considers the effect of waiting in the path planner under the interactive planning and sensing for time-varying fields framework. Future work considers various extensions of the proposed interactive planning and sensing framework including: generalizing the environment using Gaussian processes, sensor reconfiguration costs, multiresolution implementations, nonlinear parameters, decentralized sensor networks and an application to aerial payload delivery by parafoil