Research Associate in Swarm Robotics

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Overview

We are seeking an experienced researcher to lead the development of decentralised swarm mapping and localisation algorithms for underground pipe operations as part of an international research programme funded by the Engineering and Physical Sciences Research Council (EPSRC). The programme is developing machine intelligence for teams of autonomous robots working in buried water infrastructure.

You will lead the design of methods that enable robot swarms to explore pipe networks, share information, support anomaly detection, and coordinate reporting and dispatch decisions in environments with limited communication, sensing and power. The role also includes validating these methods through simulations and physical robot experiments. The development of uncertainty aware methods, such as Gaussian processes with learning capabilities is essential, including data association and other Bayesian methods.

You will join a collaborative research environment at the University of Sheffield, working across the School of Electrical and Electronic Engineering, Centre for Machine Intelligence and Sheffield Robotics with international partners including TU Darmstadt, MIT and Boston University. There is an opportunity for extensive international travel to work closely with our overseas partners and to present research to peers at key international conferences. You will help translate ambitious ideas into robust algorithms and convincing experimental demonstrations. You will also contribute to the strategic direction of this research area, support publications and project coordination, and help develop future funding applications and collaboration opportunities.

Main Duties And Responsibilities

• Lead the development of novel decentralised swarm algorithms for mapping in underground pipe operations, with particular focus on exploration, collective decision-making, information sharing, reporting and robot navigation.
• Develop sensor data fusion algorithms that are an essential element of the joint robot mapping tasks in the presence of data origin uncertainties. Knowledge of Bayesian approaches such as Gaussian process regression, particle filters, Bayesian networks, graph-based approaches. Probabilistic -based uncertainty quantification is also essential.
• Lead the design, implementation and evaluation of bio-inspired, optimisation-based and machine-learning-enabled methods that enable robot teams to operate under constraints on communication, sensing, computation and power.
• Lead the development and maintenance of high-quality research software for simulation, computer vision-based algorithms (with optical videos, Lidar and other data), sing and deployment on robotic platforms, using reproducible and well-documented development practices.
• Develop centralised and decentralised algorithms for the swarm of robots performing underground inspections.
• Lead the planning and delivery of simulation studies and laboratory experiments to validate swarm algorithms, including coordinating their integration with related mapping, sensing and robotic platform activities across the wider programme.
• Lead the analysis and interpretation of simulation and experimental data, using the findings to direct method refinement and inform project priorities, research decisions and longer-term development.
• Determine research objectives and develop, implement and adapt a programme of research in line with project aims, milestones and emerging opportunities.
• Contribute to the strategic direction and day-to-day coordination of this area of research, including planning work over several months ahead to meet project deadlines, publication targets and collaboration milestones.
• Work closely with academic staff, research colleagues and international partners, including international visits to support delivery of shared research objectives, project outputs and reports.
• Prepare high-quality publications for leading journals and conferences, and present findings to academic, industry and other stakeholder audiences.
• Supervise or co-supervise undergraduate and postgraduate project students and, where appropriate, contribute to the support or day-to-day guidance of research staff working in related areas.
• Contribute to the development of research proposals and funding applications, identify opportunities for follow-on funding, and support the growth of research partnerships and impact activity.
• Contribute to the effective management of research resources, including time, equipment, materials, data and project-related budgets, in line with funder and University requirements.
• Carry out other duties, commensurate with the grade and remit of the post