York's research and expertise is characterised by multi-disciplinary approaches to the development of advanced, ethical, safe, trusted, reliable, and certifiable mobile and autonomous robots.
Extended Robotic Instruments: Investigating computer controlled musical instruments as extended sound producing and processing systems for composition and performance
Previous research in Robotic Musicianship has focused on emulating human performance on conventional instruments, such as pianos and guitars, played with standard techniques (mostly focusing on the production of pitched sounds) in traditional composed and improvised settings. However, in recent years, a considerable amount of practice-based research in contemporary music has examined timbral transformations through extended techniques in acoustic instruments as well as live transformation of instrumental sound through analog and digital signal processing. This project examines the use of robotics in creating musical instruments with the objective of generating new sounds based on extended instrumental techniques and through systems that combine sound production and processing techniques in multiple domains: mechanical/acoustic, analog and digital. Through a study of existing extended techniques and live electronic performance in contemporary music, new robotic instruments are being designed and implemented in a live performance context focusing on the transformation of timbre as the main objective. The robotic instruments will play alongside human musicians through interactive music systems using technologies such as machine listening and learning, music information retrieval (MIR), rule-based compositional systems, as well as computational models of improvisation. A modular approach to the design of the robotic instruments will allow different configurations and signal chains integrating acoustic, analog and digital sound production and processing modules. The extended robotic instruments will be used in the creation of new compositions and performances.
A Fully-Automated Robotic System for Intelligent Chemical Reaction Screening
Automation, by intelligent chemical synthesis, is revolutionising how chemical reactions are conducted, particularly how they are optimised and characterised, improving product yields and sustainability, lowering process costs, and reducing unwanted side-products (imputrities) in final (valuable) products, e.g. agrochemical, advanced materials or pharmaceutical target compounds. The production of large chemical reaction datasets are helping considerably in this endeavour, which traditionally has been a labour-intensive process, typically with reactions being conducted one at a time. Intelligent chemical synthesis is enabling scientists to focus on data analysis and improving final product outcomes.
Autonomous Robotics Evolution: Cradle to Grave
Imagine an environment where autonomous systems - robots - are not designed by humans, or indeed designed at all, but are created through a series of steps that follow evolutionary processes. These robots will be “born” through the use of 3D manufacturing, with novel materials and a hybridised hardware-software evolutionary architecture.
Collective adaptive systems : theory and design
Collective adaptive systems consist of many autonomous units that interact in a variety of ways over multiple scales. We focus our work primarily on swarm robotic systems, developing novel approaches to self-healing systems: endowing collectives with the ability to detect, diagnose and repair failures for themselves.
Control methods for flexible and bio-inspired superlight tensegrity rovers
Dr Mark A. Post
Autonomous Robotics vehicles for planetary exploration must be responsive, energy efficient, lightweight for transport, and mechanically robust. To accomplish these goals, a small rover platform driven by the need for light weight, simplicity and reconfigurability is being developed with a minimalist mechanical structure and a flexible software design model to facilitate additions and multiple use cases. The advantages of this platform are resilience and transportability due to the low mass thin structural members of the tensegrity spine chassis, and the combined adaptability of wheeled and full-body movement. The use of flexible and distributed structures allows the rover to better adapt physically to complex and varied terrains such as those encountered on Martian slopes.
Knowledge-based self-reconfiguration for modular space robots
Dr Mark A. Post, Prof. Jim Austin
Self-configuring modular robots have the potential to revolutionize the way that tasks can be done autonomously and adaptively. One key application is in modular satellites, which can be re-configured and re-supplied with new function-specific modules to replace old or faulty ones without the risk and waste of de-orbiting the entire satellite. This project aims to extend the MOSAR (MOdular Satellite Assembly and Reconfiguration) Space Robotics SRC project with the capability, based on intelligent semantic reasoning, for a modular satellite to “know” its own capabilities and how to re-configure them to achieve science and industrial goals in orbit without human intervention.
Omni-Pi-tent modular robot platform and Dynamic Self-repair
One of the key strengths of modular robotic platforms is their ability to self-repair, ejecting failed units from their multi-robot structures and bringing in fresh modules to replace them. R.H.Peck, A.M.Tyrrell and J.Timmis's Dynamic Self-repair project seeks to pioneer ways of performing these repairs in ways which allow the robot group to maintain collective action while the ejection and replacement take place. To perform hardware experiments within this project a modular robot platform, known as Omni-Pi-tent, has been developed which provides a unique set of features. These include: an omnidirectional drive for motion across the ground to allow docking of multiple moving modules, genderless docking interfaces to let any robot attach to any other, a 2 Degree-of-Freedom hinge enabling reconfiguration in 3 dimensions, a full suite of onboard sensors to avoid dependence on external control infrastructure, and a user-friendly Raspberry Pi as each module's main computer.
RoboCalc: A Calculus for Software Engineering of Mobile and Autonomous Robots
This project involves developing a framework for integrated modelling, simulation, and programming of mobile and autonomous robots covering the full life cycle of development. The project adopts similar notations to those already in widespread use, but enriched with facilities to specify the environment and timed and probabilistic behaviours. For simulation, a language that captures facilities of major tools will be identified. The framework ensures that models and simulations are consistent and properties established by analysis and simulation are preserved in the robotic platform. The purpose is not to change current practice but to enrich it with sound validation and verification techniques. Challenges will be sound combination of notations and techniques, automation, and scalability.
RoboTest: Systematic Model-Based Testing and Simulation of Mobile Autonomous Robots
Self-repairing hardware paradigms based on astrocyte-neuron models (SPANNER)
In contrast to the human brain, modern electronic systems design typically relies on a single controller or processor, which has very limited self-repair capabilities. There is a pressing need to progress beyond current approaches and look for inspiration from biology to inform electronic systems design.
This project aims to develop a new generation of self-repairing algorithms.
The social driving simulator
The Social Driving Simulator is designed to immerse research participants in a variety of everyday road-related situations in which they must interact with other vehicles as either drivers or pedestrians. These interactions can involve both human and autonomously controlled vehicles. As a consequence, we can study the types of interactions that naturally emerge on any shared road (from traffic jams to merging to turn-taking at ambiguous intersections) while measuring participants’ cognitive, behavioural, and physiological responses.