Autonomous Task-Based Evolutionary Design of Modular Robots

In an attempt to solve the problem of finding a set of multiple unique modular robotic designs that can be constructed using a given repertoire of modules to perform a specific task, a novel synthesis framework is introduced based on design optimization concepts and evolutionary algorithms to search for the optimal design. Designing modular robotic systems faces two main challenges: the lack of basic rules of thumb and design bias introduced by human designers. The space of possible designs cannot be easily grasped by human designers especially for new tasks or tasks that are not fully understood by designers. Therefore, evolutionary computation is employed to design modular robots autonomously. Evolutionary algorithms can efficiently handle problems with discrete search spaces and solutions of variable sizes as these algorithms offer feasible robustness to local minima in the search space; and they can be parallelized easily to reducing system runtime. Moreover, they do not have to make assumptions about the solution form. This dissertation proposes a novel autonomous system for task-based modular robotic design based on evolutionary algorithms to search for the optimal design. The introduced system offers a flexible synthesis algorithm that can accommodate to different task-based design needs and can be applied to different modular shapes to produce homogenous modular robots. The proposed system uses a new representation for modular robotic assembly configuration based on graph theory and Assembly Incidence Matrix (AIM), in order to enable efficient and extendible task-based design of modular robots that can take input modules of different geometries and Degrees Of Freedom (DOFs). Robotic simulation is a powerful tool for saving time and money when designing robots as it provides an accurate method of assessing robotic adequacy to accomplish a specific task. Furthermore, it is difficult to predict robotic performance without simulation. Thus, simulation is used in this research to evaluate the robotic designs by measuring the fitness of the evolved robots, while incorporating the environmental features and robotic hardware constraints. Results are illustrated for a number of benchmark problems. The results presented a significant advance in robotic design automation state of the art

Swarm Robotics

Collectively working robot teams can solve a problem more efficiently than a single robot, while also providing robustness and flexibility to the group. Swarm robotics model is a key component of a cooperative algorithm that controls the behaviors and interactions of all individuals. The robots in the swarm should have some basic functions, such as sensing, communicating, and monitoring, and satisfy the following properties

Soft Scalable Self-Reconfigurable Modular Cellbot

Hazardous environments such as disaster affected areas, outer space, and radiation affected areas are dangerous for humans. Autonomous systems which can navigate through these environments would reduce risk of life. The terrains in these applications are diverse and unknown, hence there is a requirement for a robot which can self-adapt its morphology and use suitable control to optimally move in the desired manner. Although there exist monolithic robots for some of these applications, such as the Curiosity rover for Mars exploration, a modular robot containing multiple simple units could increase the fault tolerance. A modular design also enables scaling up or down of the robot based on the current task, for example, scaling up by connecting multiple units to cover a wider area or scaling down to pass through a tight space.Taking bio-inspiration from cells, where – based on environmental conditions – cells come together to form different structures to carry out different tasks, a soft modular robot called Cellbot was developed which was composed of multiple units called ‘cells’. Tests were conducted to understand the cellbot movement over different frictional surfaces for different actuation functions, the number of cells connected in a line (1D), and the shapes formed by connecting cells in 2D. A simulation model was developed to test a large range of frictional values and actuation functions for different friction coefficients. Based on the obtained results, cells could be designed using a material with frictional properties lying in the optimal locomotion range. In other cases, where the application has diverse terrains, the number of connected units can be changed to optimise the robot locomotion. Initial tests were conducted using a ‘ball robot’, where the cellbot was designed using balls which touch ground to exploit friction and actuators to provide force to move the robot. The model was extended to develop, a ‘bellow robot’ which was fabricated using hyper-elastic bellows and employed pneumatic actuation. The amount of inflation of a cell and its neighbouring cells determined if the cell would touch the ground or be lifted up. This was used to change cell behaviour where a cell could be touching ground to provide anchoring friction, or lifted to push or pull the cells and thereby move the robot. The cells were connected by magnets which could be disconnected and reconnected by morphing the robot body. The cellbot can thus reconfigure by changing the number of connected units or its shape. The easy detachment can be used to remove and replace damaged cells. Complex cellbot movements can be achieved by either switching between different robot morphologies or by changing actuation control.Future cellbots will be controlled remotely to change their morphology, control, and number of connected cells, making them suitable for missions which require fault tolerance and autonomous shape adaptation. The proposed cellbot platform has the potential to reduce the energy, time and costs in comparison to traditional robots and has potential for applications such as exploration missions for outer space, search and rescue missions for disaster affected areas, internal medical procedures, and nuclear decommissioning.<br/

Self-repair during continuous motion with modular robots

Through the use of multiple modules with the ability to reconfigure to form different morphologies, modular robots provide a potential method to develop more adaptable and resilient robots. Robots operating in challenging and hard-to-reach environments such as infrastructure inspection, post-disaster search-and-rescue under rubble and planetary surface exploration, could benefit from the capabilities modularity offers, especially the inherent fault tolerance which reconfigurability can provide. With self-reconfigurable modular robots self-repair, removing failed modules from a larger structure to replace them with operating modules, allows the functionality of the multi-robot organism as a whole to be recovered when modules are damaged. Previous self-repair work has, for the duration of self-repair procedures, paused group tasks in which the multi-robot organism was engaged, this thesis investigates Self-repair during continuous motion, ``Dynamic Self-repair", as a way to allow repair and group tasks to proceed concurrently. In this thesis a new modular robotic platform, Omni-Pi-tent, with capabilities for Dynamic Self-repair is developed. This platform provides a unique combination of genderless docking, omnidirectional locomotion, 3D reconfiguration possibilities and onboard sensing and autonomy. The platform is used in a series of simulated experiments to compare the performance of newly developed dynamic strategies for self-repair and self-assembly to adaptations of previous work, and in hardware demonstrations to explore their practical feasibility. Novel data structures for defining modular robotic structures, and the algorithms to process them for self-repair, are explained. It is concluded that self-repair during continuous motion can allow modular robots to complete tasks faster, and more effectively, than self-repair strategies which require collective tasks to be halted. The hardware and strategies developed in this thesis should provide valuable lessons for bringing modular robots closer to real-world applications

Towards a new hospital architecture: an exploration of the relationship between hospital space and technology

Present urban acute NHS hospitals are rigid architectural structures composed of spatial and medical planning requirements that are underpinned by complex inter-related relationships. One assumed relationship is medical technology’s affect upon hospital space. There’s limited research exploring the relationship between NHS hospital space and medical technologies. Furthermore, little is known about the implications of emerging technologies (ETs) on future urban acute NHS hospital space. This study investigates the link between hospital space and medical technology to visualise the spatial consequences of incorporating anticipated medical ETs into future urban acute NHS hospitals. A unique single futures prospective methodology is adopted with a mixed methods approach. This includes historical research, a quantitative investigation of four London case studies and a literature exploration of three medical ETs (biotechnology, robotics and cyborgization). Primary data generated from this study forms the basis for creating scenarios of future urban acute hospital environments. Findings reveal that medical technologies impact directly on hospital space, thus, confirming the existence of a link between hospital space and medical technologies. Results also reveal that even without nanotechnology progression, medical technologies decrease in equipment size during the course of their development. This trend contradicts recent medical planning practice which ‘super-sizes’ high-spec hospital rooms (see Chapter 3). Additionally, a campus-styled hospital typology is determined as the preferred flexible design solution for creating sustainable 21st century urban acute NHS hospitals. Findings lead to recommendations that guide medical planners with the future-proofing of acute hospital space by providing insight and alternative medical planning solutions that incorporate medical ETs into future urban acute NHS hospitals

Natural Growth-Inspired Distributed Self-Reconfiguration of UBot Robots

The decentralized self-reconfiguration of modular robots has been a challenging problem. This work proposed a biological method inspired by the plant growth for the distributed self-reconfiguration of UBot systems. L-systems are implemented to construct target topology, and turtle interpretation is extended to lead the self-reconfiguration process. Parametric reproduction rules introduce the external influence to the reconfiguration process by distributed modules’ local sensing. Each module can move independently to change relative positions, and robotic structures develop in the natural growth style. This leads to a convergent and environmentally sensitive control method for the distributed self-reconfiguration. Reconfiguration processes can converge to desired configuration and are scalable to module numbers by reproducing predefined substructures in principle. The overall performance of the proposed strategy is evaluated with simulations and 11 experiments. Simulation and experimental results turn out to be convergent and environmentally sensitive