Computational aspects of cellular intelligence and their role in artificial intelligence.

The work presented in this thesis is concerned with an exploration of the computational aspects of the primitive intelligence associated with single-celled organisms. The main aim is to explore this Cellular Intelligence and its role within Artificial Intelligence. The findings of an extensive literature search into the biological characteristics, properties and mechanisms associated with Cellular Intelligence, its underlying machinery - Cell Signalling Networks and the existing computational methods used to capture it are reported. The results of this search are then used to fashion the development of a versatile new connectionist representation, termed the Artificial Reaction Network (ARN). The ARN belongs to the branch of Artificial Life known as Artificial Chemistry and has properties in common with both Artificial Intelligence and Systems Biology techniques, including: Artificial Neural Networks, Artificial Biochemical Networks, Gene Regulatory Networks, Random Boolean Networks, Petri Nets, and S-Systems. The thesis outlines the following original work: The ARN is used to model the chemotaxis pathway of Escherichia coli and is shown to capture emergent characteristics associated with this organism and Cellular Intelligence more generally. The computational properties of the ARN and its applications in robotic control are explored by combining functional motifs found in biochemical network to create temporal changing waveforms which control the gaits of limbed robots. This system is then extended into a complete control system by combining pattern recognition with limb control in a single ARN. The results show that the ARN can offer increased flexibility over existing methods. Multiple distributed cell-like ARN based agents termed Cytobots are created. These are first used to simulate aggregating cells based on the slime mould Dictyostelium discoideum. The Cytobots are shown to capture emergent behaviour arising from multiple stigmergic interactions. Applications of Cytobots within swarm robotics are investigated by applying them to benchmark search problems and to the task of cleaning up a simulated oil spill. The results are compared to those of established optimization algorithms using similar cell inspired strategies, and to other robotic agent strategies. Consideration is given to the advantages and disadvantages of the technique and suggestions are made for future work in the area. The report concludes that the Artificial Reaction Network is a versatile and powerful technique which has application in both simulation of chemical systems, and in robotic control, where it can offer a higher degree of flexibility and computational efficiency than benchmark alternatives. Furthermore, it provides a tool which may possibly throw further light on the origins and limitations of the primitive intelligence associated with cells

The Design and Development of a Mobile Colonoscopy Robot

The conventional colonoscopy is a common procedure used to access the colon. Despite it being considered the Gold Standard procedure for colorectal cancer diagnosis and treatment, it has a number of major drawbacks, including high patient discomfort, infrequent but serious complications and high skill required to perform the procedure. There are a number of potential alternatives to the conventional colonoscopy, from augmenting the colonoscope to using Computed Tomography Colonography (CTC) - a completely non-invasive method. However, a truly effective, all-round alternative has yet to be found. This thesis explores the design and development of a novel solution: a fully mobile colonoscopy robot called “RollerBall”. Unlike current passive diagnostic capsules, such as PillCam, this device uses wheels at the end of adjustable arms to provide locomotion through the colon, while providing a stable platform for the use of diagnostic and therapeutic tools. The work begins by reviewing relevant literature to better understand the problem and potential solutions. RollerBall is then introduced and its design described in detail. A robust prototype was then successfully fabricated using a 3D printing technique and its performance assessed in a series of benchtop experiments. These showed that the mechanisms functioned as intended and encouraged the further development of the concept. Next, the fundamental requirement of gaining traction on the colon was shown to be possible using hexagonal shaped, macro-scale tread patterns. A friction coefficient ranging between 0.29 and 0.55 was achieved with little trauma to the tissue substrate. The electronics hardware and control were then developed and evaluated in a series of tests in silicone tubes. An open-loop strategy was first used to establish the control algorithm to map the user inputs to motor outputs (wheel speeds). These tests showed the efficacy of the locomotion technique and the control algorithm used, but they highlighted the need for autonomy. To address this, feedback was included to automate the adjusting of the arm angle and amount of force applied by the device; a forward facing camera was also used to automate the orientation control by tracking a user-defined target. Force and orientation control were then combined to show that semi-autonomous control was possible and as a result, it was concluded that clinical use may be feasible in future developments