We are very excited to share that our paper “Design and experimental investigation of a vibro-impact self-propelled capsule robot with orientation control” has been accepted for presentation as a contributed paper by the 2022 International Conference on Robotics and Automation (ICRA2022). This work was done the PhD students, Jiajia Zhang and Jiyuan Tian, co-supervised by Dr Dibin Zhu (Exeter) and Dr Shyam Prasad (Royal Devon and Exeter NHS Foundation Trust). Please read the preprint version of this paper from

https://arxiv.org/abs/2202.11784

We are delighted to share this new research article “Feature-based intelligent models for optimisation of percussive drilling” done by the PhD student, Kenneth Afebu, co-supervised with Dr Evangelos Papatheou, published at Neural Networks.

Highlights:
• Impact motions of bit-rock interaction during percussive drilling are studied.
• Feature-based classifiers are adopted in categorising these impact motions.
• Simulated and experimental impact motions data are merged to train network models.
• Feature extraction from raw data is essential for optimising networks’ performance.
• 42% and 67% of the networks yield accuracies greater than 91% and 77% respectively.

https://www.sciencedirect.com/science/article/pii/S0893608022000314

We are happy to share this survey paper on Small Bowel Modelling and its Applications for Capsule Endoscopy published in Mechatronics (Volume 83, May 2022, 102748). Thanks for all the co-authors for your contributions: Jiyuan Tian, Luigi Manfredi, Benjamin S. Terry, Shyam Prasad, Imdadur Rahman, Wojciech Marlicz, and Anastasios Koulaouzidis.

Highlights:

• This survey covers the studies on small-bowel motor activities.
• It studies the modelling of the capsule endoscope in the small intestine.
• The modelling works include analytical and finite element methods.
• Experimental investigation of the small intestine is fully reviewed.
• It is suitable for robotic engineers who are developing intestinal devices.

https://www.sciencedirect.com/science/article/pii/S0957415822000071