PhD Scholarship Opportunities:
1) Mimicking Neuro-Biomechanical Nonlinear Dynamics to Control Movements in Sliding Surface Joints

Supervisors: Dr Carlo Tiseo and Dr Jimena Berni

Animals exploit the intrinsic properties of their dynamics in their interaction with the world. Many combinations between rigid and continuum mechanisms are present in nature, allowing animals to solve complex interaction problems robustly and efficiently in challenging, unknown environments. These conditions are challenging for most artificial controllers, which are often fragile and computationally onerous due to the lack of an accurate system dynamics model (i.e., body + environment). The project explores recent advantages in nonlinear model-free control to develop hybrid mechanisms (i.e., rigid + continuum dynamics) to interact with challenging external environments. Specifically, we will investigate the properties of sliding surface joints tuned by an interconnected lattice of tendons similar to the vertebrate biomechanics structure. In parallel, we will develop algorithms and controllers to replicate the patterned behaviour generated by the spinal cord to coordinate joint actuation and movement. Bringing these two aspects together, we will build robots exploiting neuro-biomechanical nonlinear dynamics to develop control continuum mechanisms.

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References:

[1] Pulver SR, Bayley TG, Taylor AL, Berni J, Bate M, Hedwig B. Imaging fictive locomotor patterns in larval Drosophila. Journal of Neurophysioly 114(5), 2564-77 (2015)

[2] Berni J. Genetic dissection of a regionally differentiated network for exploratory behavior in Drosophila larvae. Current Biology 25(10), 1319-26 (2015)

[3] J. Gjorgjieva, J. Berni, J.F. Evers, S. Eglen. Neural Circuits for Peristaltic Wave Propagation in Crawling Drosophila Larvae: Analysis and Modeling. Front. Comput. Neurosc., 2013,7:1-19

[4] C. Tiseo, V. Ivan, W. Merkt, I. Havoutis, M. Mistry and S. Vijayakumar, “A Passive Navigation Planning Algorithm for Collision-free Control of Mobile Robots,” 2021 IEEE International Conference on Robotics and Automation (ICRA), 2021, pp. 8223-8229, doi: 10.1109/ICRA48506.2021.9561377.

[5] Babarahmati, K.K., Tiseo, C., Smith, J. et al. Fractal impedance for passive controllers: a framework for interaction robotics. Nonlinear Dyn (2022). https://doi.org/10.1007/s11071-022-07754-3

2) Designing energy efficient searching robots exploiting patterns of neuronal activity.
Project No. 2305
PRIORITY PROJECT

Primary Supervisor
Dr Jimena Berni – University of Sussex

Co-Supervisor(s)
Dr Carlo Tiseo – University of Sussex

Dr Melissa Andrews – University of Southampton

Summary
Biological systems have evolved for aeons in symbiosis with the environment, and successful strategies have been kept during evolutions across multiple species.
For example, the circuits controlling rhythmic movement (CPG) are present in the spinal cords of insects and humans and can generate efficient search behaviours.

Earlier studies on fruit fly larvae show how the patterns of neuronal activity are modulated to promote foraging exploration in unknown environments. In contrast, our artificial systems (e.g., robots) can navigate discretely well in known conditions due to efficient computational algorithms developed in the last few decades; however, they highly rely on environmental modelling, which is not available for unknown environments. Furthermore, these methods are not energy efficient.

This project explores new methods to design a robot capable of solving a foraging task in an energy-efficient way. We will implement patterned stereotyped strategies in an artificial system inspired by our experimental observation of fruit larvae while severely limiting the computational power available on the system to minimise energy consumption. We will use two methods: a traditional microcontroller technology and it will explore the algorithmic implementation of a network of coordinated nonlinear oscillators mimicking the CPG. The second platform will use electronics based on memristors, capable of reproducing the neuronal activity to generate a hardware implementation of a CPG equivalent of our system. The goal of our multidisciplinary team is to reduce at least 10 times the computational cost of solving this type of problem.

Post-Doc Opportunities:

There are no openings at the moment.

I will be happy to support applications for Post-Doctoral fellowships such as UKRI Post-Doctoral Fellowships, Marie Skłodowska-Curie Actions, Royal Academy of Engineering Fellowship, and Newton International Fellowships.