The objective of this Ph.D. thesis is the development and validation of a VTOLbased (Vertical Take Off and Landing) micro-drone for the measurement of gas concentrations, to locate gas emission sources, and to build gas distribution maps. Gas distribution mapping and localization of a static gas source are complex tasks due to the turbulent nature of gas transport under natural conditions [1] and becomes even more challenging when airborne. This is especially so, when using a VTOL-based micro-drone that induces disturbances through its rotors, which heavily affects gas distribution. Besides the adaptation of a micro-drone for gas concentration measurements, a novel method for the determination of the wind vector in real-time is presented. The on-board sensors for the flight control of the micro-drone provide a basis for the wind vector calculation. Furthermore, robot operating software for controlling the micro-drone autonomously is developed and used to validate the algorithms developed within this Ph.D. thesis in simulations and real-world experiments.
Three biologically inspired algorithms for locating gas sources are adapted and developed for use with the micro-drone: the surge-cast algorithm (a variant of the silkworm moth algorithm) [2], the zigzag / dung beetle algorithm [3], and a newly developed algorithm called “pseudo gradient algorithm”. The latter extracts from two spatially separated measuring positions the information necessary (concentration gradient and mean wind direction) to follow a gas plume to its emission source. The performance of the algorithms is evaluated in simulations and real-world experiments. The distance overhead and the gas source localization success rate are used as main performance criteria for comparing the algorithms.
Next, a new method for gas source localization (GSL) based on a particle filter (PF) is presented. Each particle represents a weighted hypothesis of the gas source position. As a first step, the PF-based GSL algorithm uses gas and wind measure-ments to reason about the trajectory of a gas patch since it was released by the gas source until it reaches the measurement position of the micro-drone. Because of the chaotic nature of wind, an uncertainty about the wind direction has to be considered in the reconstruction process, which extends this trajectory to a patch path envelope (PPE). In general, the PPE describes the envelope of an area which the gas patch has passed with high probability. Then, the weights of the particles are updated based on the PPE. Given a uniform wind field over the search space and a single gas source, the reconstruction of multiple trajectories at different measurement locations using sufficient gas and wind measurements can lead to an accurate estimate of the gas source location, whose distance to the true source location is used as the main performance criterion. Simulations and real-world experiments are used to validate the proposed method.
The aspect of environmental monitoring with a micro-drone is also discussed. Two different sampling approaches are suggested in order to address this problem. One method is the use of a predefined sweeping trajectory to explore the target area with the micro-drone in real-world gas distribution mapping experiments. As an alternative sampling approach an adaptive strategy is presented, which suggests next sampling points based on an artificial potential field to direct the micro-drone towards areas of high predictive mean and high predictive variance, while maximizing the coverage area. The purpose of the sensor planning component is to reduce the time that is necessary to converge to the final gas distribution model or to reliably identify important parameters of the distribution such as areas of high concentration. It is demonstrated that gas distribution models can provide an accurate estimate of the location of stationary gas sources. These strategies have been successfully tested in a variety of real-world experiments in different scenarios of gas release using different gas sensors to verify the reproducibility of the experiments. The adaptive strategy was also successfully validated in simulations using predefined sweeping trajectories as reference criteria.
The results of this Ph.D. thesis reflect the applicability of gas-sensitive microdrones in a variety of scenarios of gas release. Effective counteractive measures can be set in motion after accidents involving gas emissions with the aid of spatially resolved gas concentration and wind data collected with micro-drones. Monitoring of geochemically active regions, landfills, CO2 storage facilities, and the localization of gas leaks are further areas of application.

Abstract V

Acknowledgment IX

Table of Contents XI

1 Introduction 1

1.1 Contributions 5

1.2 Structure of the Thesis 7

2 State of the Art and Related Work 9

2.1 Gas Sensor Technology 9

      2.1.1 Catalytic (Cat) 10

      2.1.2 Acoustic Wave (AW) 11

      2.1.3 Electrochemical (EC) 11

      2.1.4 Metal Oxide (MOX) 12

      2.1.5 Conductive Polymer (CP) 13

      2.1.6 Infrared (IR) 14

      2.1.7 Gas Sensor Selection 14

2.2 Gas Source Localization (GSL) with Mobile Robots 16

      2.2.1 Bio-inspired Algorithms 19

      2.2.2 Probabilistic Algorithms 21

      2.2.2.1 Information Theory 21

      2.2.2.2 Bayesian Inference 21

      2.2.3 Algorithms based on Gas Distribution Maps 23

      2.2.4 Other Approaches 23

2.3 Gas Distribution Mapping (GDM) 24

      2.3.1 Spatial Monitoring with Mobile Sensor Networks 27

      2.3.2 Informative Path Planning 28

      2.3.3 Artificial Potential Fields in Mobile Robotics 28

2.4 Environmental Monitoring using Gas-sensitive Unmanned Aerial Vehicles (UAVs) 29

2.5 Wind Vector Estimation using Micro UAVs 31

3 Design of the Gas-Sensitive Micro-Drone 33

3.1 Airrobot AR100-B – Quadrocopter 35

      3.1.1 Validation of the GPS-based Positioning System 37

      3.1.1.1 Experiment Setup 37

      3.1.1.2 Experiment Results 37

3.2 Integration of Gas Sensors 38

      3.2.1 Dräger X-am 5600 39

      3.2.2 Electronic Nose (e-nose) 42

      3.2.3 Validation Experiments – Dräger X-am 5600 44

      3.2.3.1 Experiment Setup 44

      3.2.3.2 Experiment Results 44

      3.2.4 Calibration Experiments – E-Nose 44

      3.2.4.1 Experiment Setup 45

      3.2.4.2 Experiment Results 47

3.3 Gas Transport to the Sensors 47

      3.3.1 Design Approaches of Gas Transportation47

      3.3.2 Validation Experiments 49

      3.3.2.1 Experiment Setup 49

      3.3.2.2 Experiment Results 50

3.4 Estimation of the Wind Vector 51

      3.4.1 Theory 52

      3.4.2 Experiment Study 56

      3.4.2.1 Experiment Setup 56

      3.4.2.2 Experiment Results 58

      3.4.3 Validation Experiments 61

      3.4.3.1 Experiment Setup 61

      3.4.3.2 Experiment Results 62

3.5 Development of the Robot Operating Software 68

      3.5.1 Waypoint Calculation 69

      3.5.2 Measurement Campaign Software 69

      3.5.3 Autonomous Control Software 69

3.6 Field Test: Gas Measurements in a Volcanic Crater 71

3.7 Summary and Conclusions 72

4 Setup of the Simulation Environment 75

4.1 Filament-Based Gas Dispersion Model 76

      4.1.1 OpenFOAM Flow Model 76

      4.1.2 Filament-based Gas Dispersion 77

4.2 GPSModel 77

4.3 Gas Sensor Model 77

      4.3.1 Sensor Response Experiment 78

      4.3.2 Sensor Model 78

4.4 Simple Disturbance Model 81

4.5 Wind Direction Sensor Model 82

4.6 Summary and Conclusions 82

5 Plume Tracking Implemented on a Micro-Drone 83

5.1 Gas Source Localization 84

      5.1.1 Plume Acquisition 84

      5.1.2 Plume Tracking 85

      5.1.2.1 Surge-Cast Algorithm 85

      5.1.2.2 Zigzag/Dung Beetle Algorithm 88

      5.1.2.3 Pseudo Gradient Algorithm 90

      5.1.3 Source Declaration 92

5.2 Simulation Experiments 93

      5.2.1 Experiment Environment and Setup 93

      5.2.2 Experiment Results 94

5.3 Real-world Experiments 101

      5.3.1 Experiment Environment and Setup 102

      5.3.2 Experiment Results 102

5.4 Summary and Conclusions 105

6 Gas Source Localization using a Particle Filter (PF) 109

6.1 Particle Filter-based Gas Source Localization Algorithm 111

      6.1.1 Measurement Model 114

      6.1.1.1 Gas Concentration Measurements 114

      6.1.1.2 Wind Measurements 115

      6.1.1.3 Non-Uniformity of the Wind Field 115

      6.1.1.4 Construction of the Patch Path Envelope (PPE) in Uniform Wind Fields 118

      6.1.1.5 Construction of the Patch Path Envelope (PPE) in Non-Uniform Wind Fields 121

      6.1.2 Update Step 122

      6.1.3 Resampling Step 124

      6.1.4 Estimation of the Gas Source Location 125

6.2 Simulation Experiments 127

      6.2.1 Experiment Environment and Setup 127

      6.2.2 Experiment Results 128

      6.2.2.1 Parameter Optimization 128

      6.2.2.2 Results of the Validation Experiments 131

6.3 Real-world Experiments 133

      6.3.1 Experiment Environment and Setup 134

      6.3.2 Experiment Results 135

6.4 Related Work 138

6.5 Summary and Conclusions 139

7 Gas Distribution Mapping using a Micro-Drone 141

7.1 Kernel DM+V/W Algorithm 142

7.2 Data Acquisition Strategy 146

7.3 Experiment Environments and Setup 146

      7.3.1 Tuscany Region 147

      7.3.1.1 Ambra River Trials 148

      7.3.1.2 Inferno Trials 149

      7.3.2 BAM TTS Trials 150

      7.3.3 Botanical Garden Trials 151

7.4 Results of the Real-world Experiments 152

      7.4.1 Tuscany Region 153

      7.4.1.1 Ambra River Trials 153

      7.4.1.2 Inferno Trials 156

      7.4.2 BAM TTS Trials 161

      7.4.3 Botanical Garden Trials 165

7.5 Summary and Conclusions 169

8 Sensor and Path Planning Strategy for a Micro-Drone 173

8.1 Declaration of Collaboration 174

8.2 Adaptive Sensor Planning 175

      8.2.1 Locality Constraints for Adaptive Sensor Planning 177

8.3 Sensor and Path Planning Algorithm for the Micro-Drone 178

8.4 Simulation Experiments 179

      8.4.1 Theoretical Performance of SPPAM 180

      8.4.1.1 Experiment Environment and Setup 180

      8.4.1.2 Experiment Results 181

      8.4.2 Robotic Simulation 187

      8.4.2.1 Experiment Environment and Setup 187

      8.4.2.2 Experiment Results 187

8.5 Real-world Experiments 193

      8.5.1 Experiment Environment and Setup 193

      8.5.2 Experiment Results 195

      8.5.2.1 First Experiment 195

      8.5.2.2 Second Experiment 197

8.6 Summary and Conclusions 200

9 Conclusions and Future Work 203

9.1 Conclusions 203

9.2 Future Work 206

Appendix A Directional Statistics 209

Appendix B Coordinate Transformation 211

Appendix C Distance Overhead 213

Appendix D PF-based GSL Algorithm – Simulation Results 215

Appendix E Kullback-Leibler Divergence 219

Appendix F Particle Filter – Linear Time Resampling 221

Appendix G SPPAM – Real-World Experiment Results 223

Bibliography 225

List of Publications 241

List of Figures 245

List of Tables 251

List of Algorithms 253

Nomenclature 255

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