The Waitomo Caves attract approximately 500 000 tourists each year. A requirement of tourist cave management is that the partial pressure of carbon dioxide (PCO₂) is kept below levels that are: hazardous to the health of visitors, hazardous to the glowworms and other natural inhabitants, or potentially corrosive to speleothems. For the Glowworm Cave at Waitomo, the maximum permissible PCO₂ level is 2400 ppm. When exceeded, the tourist operators are required to close the cave. Ten years of monitoring data at the Glowworm Cave was analysed. Most of the variation in PCO₂ could be attributed to CO₂ respired by tourists, and the mixing of cave air with lower PCO₂ outside air. Occasionally, there were periods with high PCO₂ levels while the cave was closed to tourists. The main objective of this study was to investigate the potential role of the Waitomo Stream in contributing CO₂ to the Glowworm Cave atmosphere. Analysis of ten years of Glowworm Cave monitoring data showed that the 2400 ppm PCO₂ limit was, on average, exceeded five times each year, with a total of 48 events between 1998 and 2007. Of the PCO₂ limit exceedences, approximately 31% of events were largely driven by high tourist numbers; 27% of PCO₂ limit exceedences were mainly driven by increased discharge, rainfall, and/or a low temperature gradient between the cave and outside air, whilst 29% of the PCO₂ limit exceedences were due to a combination of tourists and increased discharge, rainfall, and/or a low temperature gradient. The remaining 13% of exceedences were unexplained by tourists or the factors investigated. It may be that the unexplained exceedences were due to the night time closure of the cave door, restricting air exchange. The PCO₂ of the Waitomo Stream was measured by equilibrating air with the streamwater within a closed loop. The air was passed continuously through an infrared gas analyser (IRGA). The streamwater PCO₂ typically ranged between 600 - 1200 ppm. Fluctuations in the PCO₂ of the Waitomo Stream coincided with PCO₂ fluctuations in the Glowworm Cave air, and under most conditions, the stream probably acted as a sink for cave air CO₂. However, following rainfall events, the stream PCO₂ increased, exceeding cave air PCO₂, thus acting as a source of CO₂ to the cave air. High stream PCO₂ often occurred at times when air flow through the cave was restricted, e.g. when the temperature gradient between the cave air and outside air was low, or stream levels were high, thus limiting air movement. The combination of high stream PCO₂ and a low temperature gradient increased the likelihood of high cave air PCO₂. Dripwater was measured to determine whether an increase in dripwater PCO₂ occurred in response to rainfall events. When rainfall events resulted in increased discharge, the dripwater PCO₂ sometimes increased (occasionally exceeding 5000 ppm), however the pattern was not consistent. The chemistry of the Waitomo and Okohua (Ruakuri) Streams was monitored with daily samples collected and analysed for major ions: HCO₃ -, Ca²⁺, Na⁺ and Mg²⁺, and δ¹³C stable isotope. The HCO₃ -, Ca²⁺, Na⁺ and Mg²⁺ concentrations in the streamwater decreased with increased discharge, presumably due to dilution. Increased discharge following rainfall events correlated with increasing PCO₂ in the Waitomo Stream, suggesting that soil atmosphere CO₂ dissolved in soil waters, and carried to the stream by saturated flow, was responsible for the streamwater PCO₂ increase. Ca in the stream showed both an increase and a decrease with respect to rainfall. Increased Ca in the stream occurred at times when the discharged waters were coming from the phreatic zone, and thus sufficient time had lapsed for CO₂ in the discharge waters to react with the limestone (carbonate dissolution reaction). Decreased Ca occurred when the infiltration and percolation of rainwater was rapid, and thus the streamwater was characterised by a higher PCO₂ and a lower Ca concentration, as insufficient time had lapsed for the discharge waters to equilibrate with the limestone. Increased negativity in the δ¹³C of the Waitomo and Ruakuri Streams coincided with increased discharge. During summer low flow, the δ¹³C of Waitomo Stream waters was -11.3‰, whereas during high stream discharge events, the δ¹³C dropped to -12 - -14‰. The δ¹³C of limestone is 0‰, the atmosphere is -7‰, and the soil atmosphere is reported to be about -24‰, thus the decrease in δ¹³C during high flow events supports the contention that soil atmosphere CO₂ is a likely source of the increased CO₂ in flood waters.

Multimodal function optimization generally focuses on algorithms to find either a local optimum or the global optimum while avoiding local optima. However, there is another class of optimization problems which have the objective of finding multiple optima with either equal or unequal function values. The knowledge of multiple local and global optima has several advantages such as obtaining an insight into the function landscape and selecting an alternative solution when dynamic nature of constraints in the search space makes a previous optimum solution infeasible to implement. Applications include identification of multiple signal sources like sound, heat, light and leaks in pressurized systems, hazardous plumes/aerosols resulting from nuclear/ chemical spills, fire-origins in forest fires and hazardous chemical discharge in water bodies, oil spills, deep-sea hydrothermal vent plumes, etc. Signals such as sound, light, and other electromagnetic radiations propagate in the form of a wave. Therefore, the nominal source profile that spreads in the environment can be represented as a multimodal function and hence, the problem of localizing their respective origins can be modeled as optimization of multimodal functions. Multimodality in a search and optimization problem gives rise to several attractors and thereby presents a challenge to any optimization algorithm in terms of finding global optimum solutions. However, the problem is compounded when multiple (global and local) optima are sought. This thesis develops a novel glowworm swarm optimization (GSO) algorithm for simultaneous capture of multiple optima of multimodal functions. The algorithm shares some features with the ant-colony optimization (ACO) and particle swarm optimization (PSO) algorithms, but with several significant differences. The agents in the GSO algorithm are thought of as glowworms that carry a luminescence quantity called luciferin along with them. The glowworms encode the function-profile values at their current locations into a luciferin value and broadcast the same to other agents in their neighborhood. The glowworm depends on a variable local decision domain, which is bounded above by a circular sensor range, to identify its neighbors and compute its movements. Each glowworm selects a neighbor that has a luciferin value more than its own, using a probabilistic mechanism, and moves toward it. That is, they are attracted to neighbors that glow brighter. These movements that are based only on local information enable the swarm of glowworms to partition into disjoint subgroups, exhibit simultaneous taxis-behavior towards, and rendezvous at multiple optima (not necessarily equal) of a given multimodal function. Natural glowworms primarily use the bioluminescent light to signal other individuals of the same species for reproduction and to attract prey. The general idea in the GSO algorithm is similar in these aspects in the sense that glowworm agents are assumed to be attracted to move toward other glowworm agents that have brighter luminescence (higher luciferin value). We present the development of the GSO algorithm in terms of its working principle, various algorithmic phases, and evolution of the algorithm from the first version of the algorithm to its present form. Two major phases ¡ splitting of the agent swarm into disjoint subgroups and local convergence of agents in each subgroup to peak locations ¡ are identified at the group level of the algorithm and theoretical performance results related to the latter phase are obtained for a simplified GSO model. Performance of the GSO algorithm against a large class of benchmark multimodal functions is demonstrated through simulation experiments. We categorize the various constants of the algorithm into algorithmic constants and parameters. We show in simulations that fixed values of the algorithmic constants work well for a large class of problems and only two parameters have some influence on algorithmic performance. We also study the performance of the algorithm in the presence of noise. Simulations show that the algorithm exhibits good performance in the presence of fairly high noise levels. We observe graceful degradation only with significant increase in levels of measurement noise. A comparison with the gradient based algorithm reveals the superiority of the GSO algorithm in coping with uncertainty. We conduct embodied robot simulations, by using a multi-robot-simulator called Player/Stage that provides realistic sensor and actuator models, in order to assess the GSO algorithm's suitability for multiple source localization tasks. Next, we extend this work to collective robotics experiments. For this purpose, we use a set of four wheeled robots that are endowed with the capabilities required to implement the various behavioral primitives of the GSO algorithm. We present an experiment where two robots use the GSO algorithm to localize a light source. We discuss an application of GSO to ubiquitous computing based environments. In particular, we propose a hazard-sensing environment using a heterogeneous swarm that consists of stationary agents and mobile agents. The agents deployed in the environment implement a modification of the GSO algorithm. In a graph of mini mum number of mobile agents required for 100% source-capture as a function of the number of stationary agents, we show that deployment of the stationary agents in a grid configuration leads to multiple phase-transitions in the heterogeneous swarm behavior. Finally, we use the GSO algorithm to address the problem of pursuit of multiple mobile signal sources. For the case where the positions of the pursuers and the moving source are collinear, we present a theoretical result that provides an upper bound on the relative speed of the mobile source below which the agents succeed in pursuing the source. We use several simulation scenarios to demonstrate the ecacy of the algorithm in pursuing mobile signal sources. In the case where the positions of the pursuers and the moving source are non-collinear, we use numerical experiments to determine an upper bound on the relative speed of the mobile source below which the pursuers succeed in pursuing the source.

Locating an odor source in a turbulent environment, an instinctive behavior of insects such as moths, is a nontrivial task in robotics. Robots equipped with odor sensors find it difficult to locate the odor source due to the sporadic nature of odor patches in a turbulent environment. In this thesis, we develop a swarm algorithm which acquires information from odor patches and utilizes it to locate the odor source. The algorithm utilizes an intelligent integration of the chemotaxis, anemotaxis and spiralling approaches, where the chemotactic behavior is implemented by the recently proposed Glowworm Swarm Optimization (GSO) algorithm. Agents switch between chemotactic, anemotactic, and spiralling modes in accordance with the information available from the environment for optimal performance. The proposed algorithm takes full advantage of communication and collaboration between the robots. It is shown to be robust, efficient and well suited for implementation in olfactory robots. An important feature of the algorithm is the use of maximum concentration encountered in the recent past for navigation, which is seen to improve algorithmic performance significantly. The algorithm initially assumes agents to be point masses, later this is modified for robots and includes a gyroscopic avoidance strategy. A variant of the algorithm which does not demand wind information, is shown to be capable of locating odor sources even in no wind environment. A deterministic GSO algorithm has been proposed which is shown capable of faster convergence. Another proposed variant, the push pull GSO algorithm is shown to be more efficient in the presence of obstacle avoidance. The proposed algorithm is also seen capable of locating odor source under varying wind conditions. We have also shown the simultaneous capture of multiple odor sources by the proposed algorithm. A mobile odor source is shown to be captured and tracked by the proposed approach. The proposed approaches are later tested on data obtained from a realistic dye mixing experiment. A gas source localization experiment is also carried out in the lab to demonstrate the validity of the proposed approaches under real world conditions.