The vast majority of applications of robots do not involve just one isolated task (such as grasping an object) but instead carefully choregraphed sequences of such tasks, with dependencies between tasks not just in terms of what comes after what but also how the previous task should be performed (in a quantitative sense at the level of motor control) in order to set up for the next. Moreover, there are numerous subjective variables in these tasks, e.g., how close should it come to a delicate object or how hard should it pull on a cable?
EPSRC-Eligible and EU applicants
Below, we list PhD topics for EPSRC-Eligible and EU applicants only. For overseas applicants see here.
To be EPSRC-Eligible for a full award, an applicant must have no restrictions on how long they can stay in the UK and have been ordinarily resident in the UK for at least 3 years prior to the start of the studentship (with some further constraint regarding residence for education).
Robotic applications spread to a variety of application domains, from autonomous cars and drones to domestic robots and personal devices. Each application domain comes with a rich set of requirements such as legal policies, safety and security standards, company values, or simply public perception. They must be realised as verifiable properties of software and hardware. Consider the following policy: a self-driving car must never break the highway code.
A large variety of robotic applications strongly involve handling various objects as the core process for task completion. To date, most of these jobs are still performed by people. Although some are automated by robots, those solutions primarily rely on pre-designed rules or tele-operation (limited operational time due to cognitive overload), which unavoidably limits the performance in changing environments. This project consists of multiple challenging research topics in robotic manipulation.
Safety critical robotic and autonomous systems, such as Unmanned Air Vehicles (UAVs) that operate beyond visual line of sight, require the highest level of certification. Certifiers are concerned with how such systems behave within their environment – as defined by system wide requirements, e.g. compliance with the rules-of-the-air (i.e. SERA). In contrast, software developer’s focus on specifications - how the system software should behave based upon operational modes and input signals. Many catastrophic system failures, e.g.
Optical cameras have been very successfully used for 3D vision and robotic navigation in texture rich environments and good visibility conditions. However, they have strong limitations in more complex scenarios where the environment is either very dynamic or visibility is poor. In this thesis, you will explore new sensor modalities and how they can help solve these problems.
Lidar point clouds have been widely used to segment large 3D scenes such as urban areas and vegetated regions (forests, crops, …), and to build elevation profiles. However, efficient point cloud analysis in the presence of complex scenes and partially transparent objects (e.g, forest canopy) is still an unsolved challenge.
Soft actuator materials are being actively pursued owing to their importance in soft robotics, artificial muscles, biomimetic devices, and beyond. Electrically-, chemically-, and light-activated actuators are mostly explored soft actuators. Recently, significant efforts have been made to reduce the driving voltage and temperature of thermoresponsive actuators, develop chemical actuators that can function in air, and enhance the energy efficiency of light-responsive actuators.
Wearable sensor technologies have recently attracted tremendous attention due to their potential applications in soft robotics, human motion detection, prosthetics, and personalized healthcare monitoring. Remarkable advances in materials science, nanotechnology, and biotechnology have led to the development of various wearable and stretchable sensors. For example, researchers including us have developed resistive and capacitive-type strain and pressure sensors and demonstrated their use in soft robotics, tactile sensing and perception, and human body motion detection.
Automotive sensing must be robust or resilient. For example, optical sensors become rapidly ineffective in heavy rain or fog, and radar sensors provide low resolution data that is inadequate for scene mapping and object identification. Further, most autononous or semi-autonomous vehicle trials are conducted in sparse sensor environments, so that interference is rarely a problem, and assume pre-learnt road network data and continuous GPS availability