As a sub-task of the general gas source localisation problem, gas source declaration is the process of determining the certainty that a source is in the immediate vicinity. Due to the turbulent character of gas transport in a natural indoor environment, it is not sufficient to search for instantaneous concentration maxima, in order to solve this task. Therefore, this paper introduces a method to classify whether an object is a gas source or not from a series of concentration measurements, recorded while the robot performs a rotation manoeuvre in front of a possible source. For three different gas source positions, a total of 288 declaration experiments were carried out at different robot-to-source distances. Based on these readings, two machine learning techniques (ANN, SVM) were evaluated in terms of their classification performance. With learning parameters that were optimised by grid search, a maximal hit rate of approximately 87.5% could be obtained using a support vector machine.

This paper presents a new robotic concept, called SWARM-BOT, based on a swarm of autonomous mobile robots with self-assembling capabilities. This concept has been developed to ensure robust navigation, search and transportation in rough terrain. The SWARM-BOT concept takes advantage from collective and distributed approaches to ensure robustness to failures and to hard environment conditions. SWARM-BOT is provided with self-assembling and self-reconfiguring capabilities: each robot is able to connect and disconnect to another one building large flexible structures. This type of structure is designed to face all-terrain conditions in a very efficient way. This paper introduces the SWARM-BOT concept from a mechatronic prospective.

This paper presents the investigation of three communication schemes which may be used in a distributed robotic system, two based on implicit forms of communication ('mechanical interaction and vision) and one based on an explicit form of communication (infrared signaling). To support the discussion and comparison between the three forms, we have chosen a concrete case study concerned with locating and pulling sticks out of an arena floor, a task successfully achieved only through collaboration between two robots. Communication schemes, among other system features, heavily influence the rate of successful collaborations, the metric adopted in this paper in order to evaluate the performance of the robotic team. Results collected using an embodied simulator show that, as a function of the system constraints ('e.g., number of robots, hardware and behavioral parameters) solutions based on more complex individuals do not necessarily lead to an improved team performance. Although the stick pulling is a simple case study without any practical application, it presents all the main difficulties of designing and controlling scalable, distributed robotic systems, characterized by subtle, nested effects between individual and group behavior or hardware and software parameters. We believe that embodied simulations are a key level of implementation in helping us understand these subtle mechanisms, achieve further abstraction, and optimize the system before any real hardware solution is implemented.

Abstract — This paper presents a new approach for estimating in real-time the parameters of the advection-diffusion equation that describes the propagation of an instantaneously released gas. A mobile robot equipped with an appropriate sensing device collects measurements in order to estimate the parameters of this equation. The selection of the set of locations where chemical concentration measurements should be recorded, is performed in real-time with the objective of maximizing the accuracy of the parameter estimates and reducing the time to convergence of this estimation problem. Simulation results are presented that validate the described approach, which has significantly lower computational requirements compared to alternative motion strategies based on exhaustive global search. I.

This thesis presents a methodology for designing self-organized autonomous robotic systems and demonstrates how this process can be applied to the problem of finding the source of an airborne odor plume. The design methodology is applicable to other task domains and the resulting odor localization system extends the state of the art. The design procedure centers on the ability to define a specific task performance metric, systematically evaluate performance in a realistic environment, and define abstract relationships between system parameters and system performance. Once such relationships have been experimentally validated in a test environment, they can be used to guide the design of a deployable system. Because this process relies heavily on evaluative feedback, this work emphasizes the development of tools that allow the collection of accurate performance data. It presents a reliable multiple robot test-bed and some task-enabling sensory hardware, as well as validation of the sensory and kinematic models used in simulation. Also, a reinforcement learning methodology is described that provides consistent optimization performance while minimizing the amount of required evaluation.

This paper presents a novel chemical plume tracing algorithm executed by a distributed network of mobile sensing robots that measure the ambient fluid velocity and chemical concentration. The algorithm drives the robotic network to the source of the toxic plume, where measures can be taken to remove or extinguish the source emitter.

Abstract — As a sub-task of the general gas source localisation problem, gas source declaration is the process of determining the certainty that a source is in the immediate vicinity. Due to the turbulent character of gas transport in a natural indoor environment, it is not sufficient to search for instantaneous concentration maxima, in order to solve this task. Therefore, this paper introduces a method to classify whether an object is a gas source from a series of concentration measurements, recorded while the robot performs a rotation manoeuvre in front of a possible source. For three different gas source positions, a total of 1056 declaration experiments were carried out at different robot-to-source distances. Based on these readings, support vector machines (SVM) with optimised learning parameters were trained and the cross-validation classification performance was evaluated. The results demonstrate the feasibility of the approach to detect proximity to a gas source using only gas sensors. The paper presents also an analysis of the classification rate depending on the desired declaration accuracy, and a comparison with the classification rate that can be achieved by selecting an optimal threshold value regarding the mean sensor signal. I.

Traditional approaches to designing multi-agent systems are offline, in simula-tion, and assume the presence of a global observer. Artificial Physics (AP) or physicomimetics (Spears and Gordon 1999) can be used to self-organize swarms of mobile robots into formations that move towards a goal. Using an offline ap-proach, we extend the AP framework to moving formations through obstacle fields. We provide important metrics of performance that allow us to (a) compare the utility of different generalized force laws in the artificial physics framework, (b) examine trade-offs between different metrics, and (c) provide a detailed method of comparison for future researchers in this area. In the online, real world, a global observer may be absent, performance feedback may be delayed or perturbed by noise, agents may only interact with their local neighbors, and only a subset of agents may experience any form of performance feed-back. Under these constraints, designing multi-agent systems is difficult. We present a novel approach called“Distributed Agent Evolution with Dynamic Adaptation to Local Unexpected Scenarios ” or DAEDALUS to address these issues, by mimicking

Abstract — This paper addresses the problem of estimating the parameters of the advection-diffusion equation, which describes the propagation of an instantaneously released gas. A team of mobile robots, equipped with appropriate sensing devices, are used for collecting gas concentration measurements. In addition, each of the robots has sensitive anemometric vanes for determining the velocity and the direction of the wind. The selection of the sequence of locations, where the robot should go to collect the gas concentration measurements, is performed in real-time and is based on the minimization of the uncertainty of the estimated parameters and on reducing the time to convergence of this estimation problem. Simulation results are presented in order to confirm the described approach, which has significantly lower computational requirements than other well-known techniques based on exhaustive global search. I.

First and for most, I would like to thank my supervisor Prof. Alan FT Winfield for his guide and advise to complete this work. I really appreciate the freedom that Alan gave me in choosing the research direction and method. Along the way I have benefited a lot from the discussion with him, both from formal supervision meeting and informal research chatting every Friday lunch time. I would like especially to thank Alan for the help to correct all the grammars in English through the whole thesis with great patient. Without the help from Alan, this thesis couldn’t reach its final form. I am also grateful to my second supervisor Dr. Jin Sa for the insightful discussion about the thesis and the project. I want to thank Jin for personally supporting me in settling down in Bristol at the beginning of my study, which makes the life much easier. I would like to thank the director of the Bristol Robotics Laboratory, Prof. Chris Melhuish for providing an extremely friendly and stimulating research environment. I would like also to thank all the colleagues in the lab for all the suggestions and kindless help during last three years. A special thank goes to Jan Dyre Bjerknes for the useful and helpful discussion in swarm robotics, and for his organisation of all kinds of parties and activities.