This PhD opportunity is part of the Centre of Doctoral Training in Autonomous Robotic Systems for Laboratory Experiments (Albert). It is focused on developing the science, engineering, and socio-technology that underpins building robots required for laboratory automation. Albert will contribute to the development of autonomous robots that conduct laboratory experiments that are cleaner, greener, safer, and cheaper than anything achievable with today's conventional techniques and technologies. Albert research will tackle significant socio-technical problems for science, engineering, social sciences, and the humanities. The YorRobots Executive and the Institute for Safe Autonomy will provide international leadership for this research area. The students will be provided with a rich research environment offering world-class labs and training opportunities.
Recent advances in AI and robotics have enabled robots to perform highly precise tasks in settings `such as chemistry laboratory experiments (Arthur, 2020). However, despite stringent quality assurance and the use of advanced sensors, there remains a notable gap in detecting subtle but significant changes in robots (Ng et al., 2022). For instance, the unnoticed gradual wear of a robot's arm joint might result in imprecise solution mixing, thereby compromising the experimental results. This issue acts as one of the major barriers in the deployment of high-autonomy robotic systems within chemical laboratories, a concern shared in the scope of the ALBERT CDT.
Many components in a robotic system—especially the physical ones—undergo gradual degradation that often goes unmonitored, limited by the scope of sensor deployment (which is constrained by cost-effectiveness and feasibility). These subtle changes may not immediately affect a system’s primary functions, but they can accumulate and create long-term issues. For instance, dual-armed robotic systems used for transferring and mixing chemical solutions may experience gradual deterioration, leading to a loss of synchronisation and compromised task precision. This can result in poorly mixed solutions and incorrect experimental results. Humans, being naturally good at situational awareness, might detect these issues through subtle indicators like solution colour or clarity. The challenge is to develop robots with comparable situational awareness capabilities, enabling them to be highly autonomous and perform prolonged tasks without human intervention in chemistry laboratory experiments.
Objective and Research Questions:
This PhD project aims to develop a novel framework enabling robots to detect and adapt to operational changes that are not directly monitored by the on-board sensors. The core of this framework will be a stochastic model designed to continuously monitor, analyse, and adapt to the robot's behaviour, pinpointing deviations in operational performance that might otherwise go unnoticed and before they cause issues. This framework will be particularly focused on armed robotic systems used in chemistry laboratories (Jiang et al., 2023). To achieve these goals, the following research questions will guide the project:
- How can robots effectively detect, model, and quantify operational changes that are not directly monitored, utilising available data sources such as sensor readings and system logs?
- What methodologies can be developed and employed to accurately assess the impact of these changes on the overall performance of robotic systems?
- How effective is the framework in identifying and responding to critical changes, and what are the implications in terms of computational needs and resource overhead?
This project contributes to advancing robotic reliability and marks a step forward in future development of high-autonomy robotic systems in assisting with tedious and potentially hazardous chemistry laboratory experiments, which directly connects to the aims of the ALBERT CDT.
Plan of Work:
The selected candidate is expected to undertake the following tasks:
- Conduct a thorough review of existing situational awareness technologies within the robotics field, with a specific focus on applications within chemistry laboratory experiments and the use of stochastic models. This will involve an in-depth exploration of contemporary approaches and their respective benefits and limitations.
- Collaborate closely with experts and stakeholders connected to this CDT. Activities may include conducting interviews or surveys with domain experts, participating in relevant workshops, and attending CDT-organised events or relevant graduate-level modules in the Department of Computer Science, School of Physics, Engineering and Technology, and the Department of Chemistry.
- Design and implement the proposed stochastic modelling framework. Initial development will take place in a simulated environment to allow for rapid iteration and refinement. Insights from the literature review and stakeholder engagement will play a crucial role in guiding this process. Following this, the framework will be applied to and tested on an actual armed robotic system.
- Conduct a series of controlled experiments to rigorously test and evaluate the framework. This will involve assessing its effectiveness in detecting and responding to operational changes, with a focus on the research questions outlined. Collaboration with domain experts will be essential during this phase to ensure that the framework's performance is thoroughly vetted against real-world standards and requirements.