This article provides a tutorial on the dynamic modeling of continuum robots. Continuum robots (CRs) have gained popularity in recent years due to their flexible backbone structure. Modeling and control of CRs motivate accurate and efficient dynamic models. Such models will enable simulation of dynamic behavior, improved structural design, and the development of dynamics-based control systems for CRs. As a unified underlying approach, the Cosserat rod-based modeling of tendon-driven CRs is used as the basis of the modeling techniques discussed in this paper. In addition to conventional continuum robot assemblies, new and emerging assemblies such as tendon-bent concentric tube and co-operative robots are also considered. The governing equations of motion for conventional CRs are first summarized and then extended for other tendon-driven CRs. This tutorial also contributes to developing a MATLAB code package for simulation of the dynamic response of these robots. The presented method and codes provide a useful and compact resource for readers and can be used for further development of tendon-driven continuum robots.

The effective disinfection of hospitals is paramount in lowering the COVID-19 transmission risk to both patients and medical personnel. Autonomous mobile robots can perform the surface disinfection task in a timely and cost-effective manner, while preventing the direct contact of disinfecting agents with humans. This paper proposes an end-to-end coverage path planning technique that generates a continuous and uninterrupted collision-free path for a mobile robot to cover an area of interest. The aim of this work is to decrease the disinfection task completion time and cost by finding an optimal coverage path using a new graph-based representation of the environment. The results are compared with other existing state-of-the-art coverage path planning approaches. It is shown that the proposed approach generates a path with shorter total travelled distance (fewer number of overlaps) and smaller number of turns.

Minimally invasive robotic interventions have highlighted the need to develop efficient techniques to measure forces applied to the soft tissues. Since the last decade, many scholars have focused on micro-scale and macro-scale robotic manipulations. Early articles used the model of soft tissue mathematically and tracked the displacement of the contour of the object in the vision system to provide the corresponding force to the user. Lack of knowledge of different materials and the computational complexity led to a transition from model-based to learning-based approaches to interpret the relation between object deformations, extracted from the vision system, and the real forces applied to the object. The dramatic growth of machine learning techniques and its integration with computer vision has brought novel learning-based visual data processing methods to the area. The application of the image-based force estimation methods in a controlled medical intervention has also received significant attention in the last five years. A decent number of surveys have been published on micromanipulation in recent years, especially for cell microinjection. However, the state of the art in meso- and macro-scale medical robotic interventions has not been reviewed. The aim and contribution of this paper are to fill the stated gap by reviewing the recent advances in image-based force estimation in robotic interventions. The survey shows that learning-based force estimation methods are growing significantly by using deep learning-based methods. The survey will encourage researchers and surgeons to apply learning-based algorithms to real-time medical and health-related operations.