On the use of autonomous unmanned vehicles in response to hazardous atmospheric release incidents

Recent events have induced a surge of interest in the methods of response to releases of hazardous materials or gases into the atmosphere. In the last decade there has been particular interest in mapping and quantifying emissions for regulatory purposes, emergency response, and environmental monitoring. Examples include: responding to events such as gas leaks, nuclear accidents or chemical, biological or radiological (CBR) accidents or attacks, and even exploring sources of methane emissions on the planet Mars. This thesis presents a review of the potential responses to hazardous releases, which includes source localisation, boundary tracking, mapping and source term estimation. [Continues.]</div

A review of source term estimation methods for atmospheric dispersion events using static or mobile sensors

Understanding atmospheric transport and dispersal events has an important role in a range of scenarios. Of particular importance is aiding in emergency response after an intentional or accidental chemical, biological or radiological (CBR) release. In the event of a CBR release, it is desirable to know the current and future spatial extent of the contaminant as well as its location in order to aid decision makers in emergency response. Many dispersion phenomena may be opaque or clear, thus monitoring them using visual methods will be difficult or impossible. In these scenarios, relevant concentration sensors are required to detect the substance where they can form a static network on the ground or be placed upon mobile platforms. This paper presents a review of techniques used to gain information about atmospheric dispersion events using static or mobile sensors. The review is concluded with a discussion on the current limitations of the state of the art and recommendations for future research

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Department of Mechanical EngineeringThe potential danger of invisible hazardous substance leakage accident is increasing, such as hazardous chemical leakage accidents in industrial complexes, potential risks of aging nuclear power plants, and international chemical terrorism threats. In particular, hazardous chemical, biological, or radioactive substances leaked into the atmosphere cause irreversible damage to nature, and there is a risk of human damage if prompt action is not taken. Therefore, estimating the emission source and the amount of invisible hazardous substances is required to minimize human casualties and increase public safety. As the risk of hazardous material leakage and potential terrorism increases in random places, it is difficult using traditional systems such as pre-installed ground sensors in a specific area. This thesis proposes autonomous search method for estimating the source of hazardous materials using a mobile sensor attached to an unmanned aerial vehicle (UAV). Since the mobile sensor can be freely deployed to any arbitrary places, it is possible to monitor a wider area with a relatively low cost. Besides, this approach is an unmanned autonomous system, so it has the advantage of minimizing secondary human casualties that may additionally occur during search. The source term estimation (STE) using mobile sensors is considered to be a challenging problem because the sensor measurements from atmospheric gas dispersion are sparse, intermittent, and time-varying due to the turbulence and the sensor noise. Thus, Bayesian inference-based estimation technique, sequential Monte Carlo method (i.e., particle filter), is used to estimate the source by using the inaccurate measurements which is easily influenced by air turbulence and sensor noise in this thesis. The autonomous search algorithms using information theory are also proposed. In the proposed algorithms, the information entropy (i.e., uncertainty of estimation) is calculated by using information theory and the agent choose the action to move to the next sensing location that can minimize the expected uncertainty. In other words, the proposed information-theoretic search algorithm is reward-based decision making approaches that use information entropy as a reward. The receding horizon and Gaussian mixture model clustering approaches are adopted to improve the search performance in various environment. Since the time required to compute all of the respective rewards increases as the number of action candidates increases, the policy-based autonomous source term search and estimation algorithm is proposed using deep neural network and reinforcement learning approach to determine efficient search path considering continuous action space. Furthermore, this thesis proposes a cooperative search method for multiple unmanned mobile vehicles based on game theory. The inaccuracy of sensor measurement values can be reduced by using multiple mobile sensors with the fusion approach, so the source of hazardous substances can be quickly estimated. The negotiation based on the game theory can improve the group search performance for source term estimation and search. Finally, to verify the performance of the proposed algorithm, numerical simulation and flight test results using an actual gas measurement sensor and multicopter drone are presented.ope

A review of source term estimation methods for atmospheric dispersion events using static or mobile sensors

Understanding atmospheric transport and dispersal events has an important role in a range of scenarios. Of particular importance is aiding in emergency response after an intentional or accidental chemical, biological or radiological (CBR) release. In the event of a CBR release, it is desirable to know the current and future spatial extent of the contaminant as well as its location in order to aid decision makers in emergency response. Many dispersion phenomena may be opaque or clear, thus monitoring them using visual methods will be difficult or impossible. In these scenarios, relevant concentration sensors are required to detect the substance where they can form a static network on the ground or be placed upon mobile platforms. This paper presents a review of techniques used to gain information about atmospheric dispersion events using static or mobile sensors. The review is concluded with a discussion on the current limitations of the state of the art and recommendations for future research.close

Chemical source localization fusing concentration information in the presence of chemical background noise

We present the estimation of a likelihood map for the location of the source of a chemical plume dispersed under atmospheric turbulence under uniform wind conditions. The main contribution of this work is to extend previous proposals based on Bayesian inference with binary detections to the use of concentration information while at the same time being robust against the presence of background chemical noise. For that, the algorithm builds a background model with robust statistics measurements to assess the posterior probability that a given chemical concentration reading comes from the background or from a source emitting at a distance with a specific release rate. In addition, our algorithm allows multiple mobile gas sensors to be used. Ten realistic simulations and ten real data experiments are used for evaluation purposes. For the simulations, we have supposed that sensors are mounted on cars which do not have among its main tasks navigating toward the source. To collect the real dataset, a special arena with induced wind is built, and an autonomous vehicle equipped with several sensors, including a photo ionization detector (PID) for sensing chemical concentration, is used. Simulation results show that our algorithm, provides a better estimation of the source location even for a low background level that benefits the performance of binary version. The improvement is clear for the synthetic data while for real data the estimation is only slightly better, probably because our exploration arena is not able to provide uniform wind conditions. Finally, an estimation of the computational cost of the algorithmic proposal is presente

Robotic Olfactory-Based Navigation with Mobile Robots

Robotic odor source localization (OSL) is a technology that enables mobile robots or autonomous vehicles to find an odor source in unknown environments. It has been viewed as challenging due to the turbulent nature of airflows and the resulting odor plume characteristics. The key to correctly finding an odor source is designing an effective olfactory-based navigation algorithm, which guides the robot to detect emitted odor plumes as cues in finding the source. This dissertation proposes three kinds of olfactory-based navigation methods to improve search efficiency while maintaining a low computational cost, incorporating different machine learning and artificial intelligence methods. A. Adaptive Bio-inspired Navigation via Fuzzy Inference Systems. In nature, animals use olfaction to perform many life-essential activities, such as homing, foraging, mate-seeking, and evading predators. Inspired by the mate-seeking behaviors of male moths, this method presents a behavior-based navigation algorithm for using on a mobile robot to locate an odor source. Unlike traditional bio-inspired methods, which use fixed parameters to formulate robot search trajectories, a fuzzy inference system is designed to perceive the environment and adjust trajectory parameters based on the current search situation. The robot can automatically adapt the scale of search trajectories to fit environmental changes and balance the exploration and exploitation of the search. B. Olfactory-based Navigation via Model-based Reinforcement Learning Methods. This method analogizes the odor source localization as a reinforcement learning problem. During the odor plume tracing process, the belief state in a partially observable Markov decision process model is adapted to generate a source probability map that estimates possible odor source locations. A hidden Markov model is employed to produce a plume distribution map that premises plume propagation areas. Both source and plume estimates are fed to the robot. A decision-making model based on a fuzzy inference system is designed to dynamically fuse information from two maps and balance the exploitation and exploration of the search. After assigning the fused information to reward functions, a value iteration-based path planning algorithm solves the optimal action policy. C. Robotic Odor Source Localization via Deep Learning-based Methods. This method investigates the viability of implementing deep learning algorithms to solve the odor source localization problem. The primary objective is to obtain a deep learning model that guides a mobile robot to find an odor source without explicating search strategies. To achieve this goal, two kinds of deep learning models, including adaptive neuro-fuzzy inference system (ANFIS) and deep neural networks (DNNs), are employed to generate the olfactory-based navigation strategies. Multiple training data sets are acquired by applying two traditional methods in both simulation and on-vehicle tests to train deep learning models. After the supervised training, the deep learning models are verified with unseen search situations in simulation and real-world environments. All proposed algorithms are implemented in simulation and on-vehicle tests to verify their effectiveness. Compared to traditional methods, experiment results show that the proposed algorithms outperform them in terms of the success rate and average search time. Finally, the future research directions are presented at the end of the dissertation

Information Theoretic Models of Social Interaction

This dissertation demonstrates, in a non-semantic information-theoretic framework, how the principles of \maximisation of relevant information" and \information parsimony" can guide the adaptation of an agent towards agent-agent interaction. Central to this thesis is the concept of digested information; I argue that an agent is intrinsically motivated to a.) process the relevant information in its environment and b.) display this information in its own actions. From the perspective of similar agents, who require similar information, this di erentiates other agents from the rest of the environment, by virtue of the information they provide. This provides an informational incentive to observe other agents and integrate their information into one's own decision making process. This process is formalized in the framework of information theory, which allows for a quantitative treatment of the resulting e ects, speci cally how the digested information of an agent is in uenced by several factors, such as the agent's performance and the integrated information of other agents. Two speci c phenomena based on information maximisation arise in this thesis. One is ocking behaviour similar to boids that results when agents are searching for a location in a girdworld and integrated the information in other agent's actions via Bayes' Theorem. The other is an e ect where integrating information from too many agents becomes detrimental to an agent's performance, for which several explanations are provided

Self-Motivated Composition of Strategic Action Policies

In the last 50 years computers have made dramatic progress in their capabilities, but at the same time their failings have demonstrated that we, as designers, do not yet understand the nature of intelligence. Chess playing, for example, was long offered up as an example of the unassailability of the human mind to Artificial Intelligence, but now a chess engine on a smartphone can beat a grandmaster. Yet, at the same time, computers struggle to beat amateur players in simpler games, such as Stratego, where sheer processing power cannot substitute for a lack of deeper understanding. The task of developing that deeper understanding is overwhelming, and has previously been underestimated. There are many threads and all must be investigated. This dissertation explores one of those threads, namely asking the question “How might an artificial agent decide on a sensible course of action, without being told what to do?”. To this end, this research builds upon empowerment, a universal utility which provides an entirely general method for allowing an agent to measure the preferability of one state over another. Empowerment requires no explicit goals, and instead favours states that maximise an agent’s control over its environment. Several extensions to the empowerment framework are proposed, which drastically increase the array of scenarios to which it can be applied, and allow it to evaluate actions in addition to states. These extensions are motivated by concepts such as bounded rationality, sub-goals, and anticipated future utility. In addition, the novel concept of strategic affinity is proposed as a general method for measuring the strategic similarity between two (or more) potential sequences of actions. It does this in a general fashion, by examining how similar the distribution of future possible states would be in the case of enacting either sequence. This allows an agent to group action sequences, even in an unknown task space, into ‘strategies’. Strategic affinity is combined with the empowerment extensions to form soft-horizon empowerment, which is capable of composing action policies in a variety of unknown scenarios. A Pac-Man-inspired prey game and the Gambler’s Problem are used to demonstrate this selfmotivated action selection, and a Sokoban inspired box-pushing scenario is used to highlight the capability to pick strategically diverse actions. The culmination of this is that soft-horizon empowerment demonstrates a variety of ‘intuitive’ behaviours, which are not dissimilar to what we might expect a human to try. This line of thinking demonstrates compelling results, and it is suggested there are a couple of avenues for immediate further research. One of the most promising of these would be applying the self-motivated methodology and strategic affinity method to a wider range of scenarios, with a view to developing improved heuristic approximations that generate similar results. A goal of replicating similar results, whilst reducing the computational overhead, could help drive an improved understanding of how we may get closer to replicating a human-like approach