Using wireless sensors and networks program for chemical particle propagation mapping and chemical source localization

Chemical source localization is a challenge for most of researchers. It has extensive applications, such as anti-terrorist military, Gas and oil industry, and environment engineering. This dissertation used wireless sensor and sensor networks to get chemical particle propagation mapping and chemical source localization. First, the chemical particle propagation mapping is built using interpolation and extrapolation methods. The interpolation method get the chemical particle path through the sensors, and the extrapolation method get the chemical particle beyond the sensors. Both of them compose of the mapping in the whole considered area. Second, the algorithm of sensor fusion is proposed. It smooths the chemical particle paths through integration of more sensors\u27 value and updating the parameters. The updated parameters are associated with including sensor fusion among chemical sensors and wind sensors at same positions and sensor fusion among sensors at different positions. This algorithm improves the accuracy and efficiency of chemical particle mapping. Last, the reasoning system is implemented aiming to detect the chemical source in the considered region where the chemical particle propagation mapping has been finished. This control scheme dynamically analyzes the data from the sensors and guide us to find the goal. In this dissertation, the novel algorithm of modelling chemical propagation is programmed using Matlab. Comparing the results from computational fluid dynamics (CFD) software COMSOL, this algorithm have the same level of accuracy. However, it saves more computational times and memories. The simulation and experiment of detecting chemical source in an indoor environment and outdoor environment are finished in this dissertation --Abstract, page iii

Smelling Nano Aerial Vehicle for Gas Source Localization and Mapping

This paper describes the development and validation of the currently smallest aerial platform with olfaction capabilities. The developed Smelling Nano Aerial Vehicle (SNAV) is based on a lightweight commercial nano-quadcopter (27 g) equipped with a custom gas sensing board that can host up to two in situ metal oxide semiconductor (MOX) gas sensors. Due to its small form-factor, the SNAV is not a hazard for humans, enabling its use in public areas or inside buildings. It can autonomously carry out gas sensing missions of hazardous environments inaccessible to terrestrial robots and bigger drones, for example searching for victims and hazardous gas leaks inside pockets that form within the wreckage of collapsed buildings in the aftermath of an earthquake or explosion. The first contribution of this work is assessing the impact of the nano-propellers on the MOX sensor signals at different distances to a gas source. A second contribution is adapting the 'bout' detection algorithm, proposed by Schmuker et al. (2016) to extract specific features from the derivative of the MOX sensor response, for real-time operation. The third and main contribution is the experimental validation of the SNAV for gas source localization (GSL) and mapping in a large indoor environment (160 m²) with a gas source placed in challenging positions for the drone, for example hidden in the ceiling of the room or inside a power outlet box. Two GSL strategies are compared, one based on the instantaneous gas sensor response and the other one based on the bout frequency. From the measurements collected (in motion) along a predefined sweeping path we built (in less than 3 min) a 3D map of the gas distribution and identified the most likely source location. Using the bout frequency yielded on average a higher localization accuracy than using the instantaneous gas sensor response (1.38 m versus 2.05 m error), however accurate tuning of an additional parameter (the noise threshold) is required in the former case. The main conclusion of this paper is that a nano-drone has the potential to perform gas sensing tasks in complex environments

Hazardous Chemical Source Localisation in Indoor Environments Using Plume-tracing Methods

Bio-inspired chemical plume-tracing methods have been applied to mobile robots to detect chemical emissions in the form of plumes and localise the plume sources in various indoor environments. Nevertheless, it has been found from the literature that most of the research has focused on plume tracing in free-stream plumes, such as indoor plumes where the chemical sources are located away from walls. Moreover, most of the experimental and numerical studies regarding the assessment of indoor plume-tracing algorithms have been undertaken in laboratory-scale environments. Since fluid fields and chemical concentration distributions of plumes near walls can be different from those of free-stream plumes, understanding of the performance of existing plume-tracing algorithms in near-wall regions is needed. In addition, the performance of different plume-tracing algorithms in detecting and tracing wall plumes in large-scale indoor environments is still unclear. In this research, a simulation framework combining ANSYS/FLUENT, which is used for simulating fluid fields and chemical concentration distributions of the environment, and MATLAB, with which plume-tracing algorithms are coded, is applied. In general, a plume-tracing algorithm can be divided into three stages: plume sensing, plume tracking and source localisation for analysis and discussion. In the first part of this research, an assessment of the performance of sixteen widely-used plume-tracing algorithms equipped with a concentration-distance obstacle avoidance method, was undertaken in two different scenarios. In one scenario, a single chemical source is located away from the walls in a wind-tunnel-like channel and in the other scenario, the chemical source is located near a wall. It is found that normal casting, surge anemotaxis and constant stepsize together performed the best, when compared with all the other algorithms. Also, the performance of the concentration-distance obstacle avoidance method is unsatisfactory. By applying an along-wall obstacle avoidance method, an algorithm called vallumtaxis, has been proposed and proved to contribute to higher efficiencies for plume tracing especially when searching in wall plumes. The results and discussion of the first part are presented in Chapter 4 of this thesis. In the second part, ten plume-tracing algorithms were tested and compared in four scenarios in a large-scale indoor environment: an underground warehouse. In these four scenarios, the sources are all on walls while their locations are different. The preliminary testing results of five algorithms show that for most failure cases, the robot failed at source localisation stage. Consequently, with different searching strategies at source localisation stage, this research investigated five further algorithms. The results demonstrated that the algorithm with a specially-designed pseudo casting source localisation method is the best approach to localising hazardous plume sources in the underground warehouse given in this research or other similar environments, among all the tested algorithms. The second part of the study is reported in Chapter 5 of this thesis.Thesis (MPhil) -- University of Adelaide, School of Mechanical Engineering, 202

Uncertainty Minimization in Robotic 3D Mapping Systems Operating in Dynamic Large-Scale Environments

This dissertation research is motivated by the potential and promise of 3D sensing technologies in safety and security applications. With specific focus on unmanned robotic mapping to aid clean-up of hazardous environments, under-vehicle inspection, automatic runway/pavement inspection and modeling of urban environments, we develop modular, multi-sensor, multi-modality robotic 3D imaging prototypes using localization/navigation hardware, laser range scanners and video cameras. While deploying our multi-modality complementary approach to pose and structure recovery in dynamic real-world operating conditions, we observe several data fusion issues that state-of-the-art methodologies are not able to handle. Different bounds on the noise model of heterogeneous sensors, the dynamism of the operating conditions and the interaction of the sensing mechanisms with the environment introduce situations where sensors can intermittently degenerate to accuracy levels lower than their design specification. This observation necessitates the derivation of methods to integrate multi-sensor data considering sensor conflict, performance degradation and potential failure during operation. Our work in this dissertation contributes the derivation of a fault-diagnosis framework inspired by information complexity theory to the data fusion literature. We implement the framework as opportunistic sensing intelligence that is able to evolve a belief policy on the sensors within the multi-agent 3D mapping systems to survive and counter concerns of failure in challenging operating conditions. The implementation of the information-theoretic framework, in addition to eliminating failed/non-functional sensors and avoiding catastrophic fusion, is able to minimize uncertainty during autonomous operation by adaptively deciding to fuse or choose believable sensors. We demonstrate our framework through experiments in multi-sensor robot state localization in large scale dynamic environments and vision-based 3D inference. Our modular hardware and software design of robotic imaging prototypes along with the opportunistic sensing intelligence provides significant improvements towards autonomous accurate photo-realistic 3D mapping and remote visualization of scenes for the motivating applications

Exploring the relationship between spatial cognitive ability and movement ecology

Spatial cognitive ability is hypothesised to be a key determinant of animal movement patterns. However, empirical demonstrations linking intra-individual variations in spatial cognitive ability with movement ecology are rare. I reared ~200 simultaneously hatched pheasant chicks per year over three years in standardised conditions without parents, controlling for the confounding effects of experience, maternal influences and age. I tested the chicks on spatial cognitive tasks from three weeks old to obtain measures of inherent, early-life spatial cognitive ability. Each year, I released birds when 10 weeks old into an open-topped enclosure in woodland. Birds dispersed from this enclosure after about one-month. Importantly, all birds were released into the same, novel area simultaneously, thus their experiences and opportunities were standardised. I remotely tracked pheasant movement through either RFID antenna placed under 43 supplementary feeders situated throughout our field site (2016) or by using a novel reverse-GPS tracking system (2017-2018). Spatial cognitive ability, determined through binary spatial discrimination (2016) or a Barnes maze (2017), was related to the diversity of foraging sites an individual used (Chapter 2: 2016). Those with better spatial cognitive ability used a more diverse range of artificial feeders than poor performing counterparts, perhaps to retain a buffer of alternative foraging sites where resource profitability was known. I found no relationship between the timing of daily foraging onset between birds of differing cognitive ability (Chapter 3; 2016), which I had hypothesised to be a consequence of birds developing efficient routes between refuges and feeders. After establishing a reverse GPS system on our field site (Chapter 4: 2017), I collected more detailed information about pheasant movement and found that birds with higher accuracy scores on the cognition tasks initially moved between foraging and resting sites more slowly than inaccurate birds in novel environments, perhaps to gather more detailed information. Accurate birds increased their speed over one month to match the same speed as inaccurate birds. All birds increased the straightness of their routes at a similar rate. Lastly, I found intraspecific differences in the orientation strategy that birds used to solve a dual strategy maze task (Chapter 5: 2018). These differences predicted habitat use after release: birds that utilised landmarks (allocentric strategies) showed less aversion to urban habitats (farm buildings/yards) than egocentric/mixed strategy birds, which is potentially due to the presence of large, stable landmarks within these habitats. In this thesis, I provide several empirical links between spatial cognitive ability and movement ecology across a range of ecological contexts. I suggest that very specific cognitive processes may govern particular movement behaviours and that there is not one overarching general spatial ability.European Commissio

Circles within spirals, wheels within wheels; Body rotation facilitates critical insights into animal behavioural ecology

How animals behave is fundamental to enhancing their lifetime fitness, so defining how animals move in space and time relates to many ecological questions, including resource selection, activity budgets and animal movement networks. Historically, animal behaviour and movement has been defined by direct observation, however recent advancements in biotelemetry have revolutionised how we now assess behaviour, particularly allowing animals to be monitored when they cannot be seen. Studies now pair ‘convectional’ radio telemetries with motion sensors to facilitate more detailed investigations of animal space-use. Motion sensitive tags (containing e.g., accelerometers and magnetometers) provide precise data on body movements which characterise behaviour, and this has been exemplified in extensive studies using accelerometery data, which has been linked to space-use defined by GPS. Conversely, consideration of body rotation (particularly change in yaw) is virtually absent within the biologging literature, even though various scales of yaw rotation can reveal important patterns in behaviour and movement, with animal heading being a fundamental component characterising space-use. This thesis explores animal body angles, particularly about the yaw axis, for elucidating animal movement ecology. I used five model species (a reptile, a mammal and three birds) to demonstrate the value of assessing body rotation for investigating fine-scale movement-specific behaviours. As part of this, I advanced the ‘dead-reckoning’ method, where fine-scale animal movement between temporally poorly resolved GPS fixes can be deduced using heading vectors and speed. I addressed many issues with this protocol, highlighting errors and potential solutions but was able to show how this approach leads to insights into many difficult-to-study animal behaviours. These ranged from elucidating how and where lions cross supposedly impermeable man-made barriers to examining how penguins react to tidal currents and then navigate their way to their nests far from the sea in colonies enclosed within thick vegetation

Design revolutions: IASDR 2019 Conference Proceedings. Volume 2: Living, Making, Value

In September 2019 Manchester School of Art at Manchester Metropolitan University was honoured to host the bi-annual conference of the International Association of Societies of Design Research (IASDR) under the unifying theme of DESIGN REVOLUTIONS. This was the first time the conference had been held in the UK. Through key research themes across nine conference tracks – Change, Learning, Living, Making, People, Technology, Thinking, Value and Voices – the conference opened up compelling, meaningful and radical dialogue of the role of design in addressing societal and organisational challenges. This Volume 2 includes papers from Living, Making and Value tracks of the conference