Minimally invasive surgery (MIS) is a surgical technique that offers distinct advantages in reducing pain and patients' recovery time. However, the drawback due to the lack of force and tactile feedback presents a great deal of limitations in MIS procedures. Tissue palpation, which is easily conducted during traditional open surgery to examine tissue properties and abnormalities, is not possible when performing surgery in a minimally invasive manner. This paper proposes a specially designed miniature 3-axis distal force sensor that can be used to perform tissue palpation, measuring tissue interaction forces at the tip of a surgical instrument. Relying on an optical sensing scheme, the sensor can measure forces within measurement ranges of ±3 N in axial direction and ±1.5 N in radial direction. The resolution is 0.02 N. It is compatible with laparoscopic operations and can be used to localize tissue lesions or relatively hard nodules buried under an organ's surface, which are not detectable by visual means.

This paper introduces a pose-sensing system for soft robot arms integrating a set of macrobend stretch sensors. The macrobend sensory design in this study consists of optical fibres and is based on the notion that bending an optical fibre modulates the intensity of the light transmitted through the fibre. This sensing method is capable of measuring bending, elongation and compression in soft continuum robots and is also applicable to wearable sensing technologies, e.g. pose sensing in the wrist joint of a human hand. In our arrangement, applied to a cylindrical soft robot arm, the optical fibres for macrobend sensing originate from the base, extend to the tip of the arm, and then loop back to the base. The connectors that link the fibres to the necessary opto-electronics are all placed at the base of the arm, resulting in a simplified overall design. The ability of this custom macrobend stretch sensor to flexibly adapt its configuration allows preserving the inherent softness and compliance of the robot which it is installed on. The macrobend sensing system is immune to electrical noise and magnetic fields, is safe (because no electricity is needed at the sensing site), and is suitable for modular implementation in multi-link soft continuum robotic arms. The measurable light outputs of the proposed stretch sensor vary due to bend-induced light attenuation (macrobend loss), which is a function of the fibre bend radius as well as the number of repeated turns. The experimental study conducted as part of this research revealed that the chosen bend radius has a far greater impact on the measured light intensity values than the number of turns (if greater than five). Taking into account that the bend radius is the only significantly influencing design parameter, the macrobend stretch sensors were developed to create a practical solution to the pose sensing in soft continuum robot arms. Henceforward, the proposed sensing design was benchmarked against an electromagnetic tracking system (NDI Aurora) for validation.

We describe a novel approach for the localization of tissue abnormalities during minimally invasive surgery using a force-sensitive wheeled probe. The concept is to fuse the kinaesthetic information from the wheel-tissue rolling interaction into a pseudocolor rolling mechanical image (RMI) to visualize the spatial variation of stiffness within the internal tissue structure. Since tissue abnormalities are often firmer than the surrounding organ or parenchyma, a surgeon then can localize abnormalities by analyzing the image. Initially, a testing facility for validating the concept in an ex vivo setting was developed and used to investigate rolling ¿wheel-tissue¿ interaction. A silicone soft-tissue phantom with embedded hard nodules was constructed to allow for experimental comparison between an RMI and a known soft-tissue structure. Tests have also been performed on excised porcine organs to show the efficacy of the method when applied to biological soft tissues. Results indicate that the RMI technique is particularly suited to identifying the stiffness distribution within a tissue sample, as the continuous force measurement along a given rolling trajectory provides repeatable information regarding relative variations in the normal tissue response. When compared to multiple discrete uniaxial indentations, the continuous measurement approach of RMI is shown to be more sensitive and facilitates coverage of a large area in a short period of time. Furthermore, if parametric classification of tissue properties based on a uniaxial tissue indentation model is desirable, the rolling indentation probe can be easily employed as a uniaxial indenter.

Flexible colonoscopy remains the prime mean of screening for colorectal cancer (CRC) and the gold standard of all population-based screening pathways around the world. Almost 60% of CRC deaths could be prevented with screening. However, colonoscopy attendance rates are affected by discomfort, fear of pain and embarrassment or loss of control during the procedure. Moreover, the emergence and global thread of new communicable diseases might seriously affect the functioning of contemporary centres performing gastrointestinal endoscopy. Innovative solutions are needed: artificial intelligence (AI) and physical robotics will drastically contribute for the future of the healthcare services. The translation of robotic technologies from traditional surgery to minimally invasive endoscopic interventions is an emerging field, mainly challenged by the tough requirements for miniaturization. Pioneering approaches for robotic colonoscopy have been reported in the nineties, with the appearance of inchworm-like devices. Since then, robotic colonoscopes with assistive functionalities have become commercially available. Research prototypes promise enhanced accessibility and flexibility for future therapeutic interventions, even via autonomous or robotic-assisted agents, such as robotic capsules. Furthermore, the pairing of such endoscopic systems with AI-enabled image analysis and recognition methods promises enhanced diagnostic yield. By assembling a multidisciplinary team of engineers and endoscopists, the paper aims to provide a contemporary and highly-pictorial critical review for robotic colonoscopes, hence providing clinicians and researchers with a glimpse of the major changes and challenges that lie ahead.

Using a tactile array sensor to recognize an object often requires multiple touches at different positions. This process is prone to move or rotate the object, which inevitably increases difficulty in object recognition. To cope with the unknown object movement, this paper proposes a new tactile-SIFT descriptor to extract features in view of gradients in the tactile image to represent objects, to allow the features being invariant to object translation and rotation. The tactile-SIFT segments a tactile image into overlapping subpatches, each of which is represented using a dn-dimensional gradient vector, similar to the classic SIFT descriptor. Tactile-SIFT descriptors obtained from multiple touches form a dictionary of k words, and the bag-of-words method is then used to identify objects. The proposed method has been validated by classifying 18 real objects with data from an off-the-shelf tactile sensor. The parameters of the tactile-SIFT descriptor, including the dimension size dn and the number of subpatches sp, are studied. It is found that the optimal performance is obtained using an 8-D descriptor with three subpatches, taking both the classification accuracy and time efficiency into consideration. By employing tactile-SIFT, a recognition rate of 91.33% has been achieved with a dictionary size of 50 clusters using only 15 touches.