A Novel Concept for Safe, Stiffness-Controllable Robot Links

The recent decade has seen an astounding increase of interest and advancement in a new field of robotics, aimed at creating structures specifically for the safe interaction with humans. Softness, flexibility and variable stiffness in robotics have been recognised as highly desirable characteristics for many applications. A number of solutions were proposed ranging from entirely soft robots (such as those composed mainly from soft materials such as silicone), via flexible continuum and snake-like robots, to rigid-link robots enhanced by joints that exhibit an elastic behaviour either implemented in hardware or achieved purely by means of intelligent control. Although these are very good solutions paving the path to safe human-robot interaction, we propose here a new approach which focuses on creating stiffness controllability for the linkages between the robot joints. This paper proposes a replacement for the traditionally rigid robot link – the new link is equipped with an additional capability of stiffness controllability. With this added feature, a robot can accurately carry out manipulation tasks (high stiffness), but can virtually instantaneously reduce its stiffness when a human is nearby or in contact with the robot. The key point of the invention described here is a robot link made of an airtight chamber formed by a soft and flexible, but high-strain resistant combination of a plastic mesh and silicone wall. Inflated with air to a high pressure, the mesh-silicone chamber behaves like a rigid link; reducing the air pressure, softens the link and rendering the robot structure safe. This paper investigates a number of our link prototypes and shows the feasibility of the new concept. Stiffness tests have been performed, showing that a significant level of stiffness can be achieved - up to 40 N reaction force along the axial direction, for a 25 mm diameter sample at 60 kPa, at an axial deformation of 5 mm. The results confirm that this novel concept to linkages for robot manipulators exhibits the beam-like behaviour of traditional rigid links when fully pressurised and significantly reduced stiffness at low pressure. The proposed concept has the potential to easily create safe robots, augmenting traditional robot designs

Fingertip Proximity Sensor with Realtime Visual-based Calibration

Proximity and distance estimation sensors are broadly used in robotic hands to enhance the quality of grasping during grasp planning, grasp correction and in-hand manipulation. This paper presents a fiber optical proximity sensor that is integrated with a tactile sensing fingertip of a robotic hand of a mobile robot. The distance estimation of proximity sensors are typically influenced by the reflective properties of an object, such as color or surface roughness. With the approach proposed in this paper, the accuracy of the proximity sensor is enhanced using the information collected by the vision system of the robot. A camera is employed to obtain RGB values of the object to be grasped. Further on, the data obtained from the camera is used to obtain the correct calibration for the proximity sensor. Based on the experimental evidence, it is shown that our approach can be effectively used to reduce the distance estimation error

Imaging skins: stretchable and conformable on-organ beta particle detectors for radioguided surgery

While radioguided surgery (RGS) traditionally relied on detecting gamma rays, direct detection of beta particles could facilitate the detection of tumour margins intraoperatively by reducing radiation noise emanating from distant organs, thereby improving the signal-to-noise ratio of the imaging technique. In addition, most existing beta detectors do not offer surface sensing or imaging capabilities. Therefore, we explore the concept of a stretchable scintillator to detect beta-particles emitting radiotracers that would be directly deployed on the targeted organ. Such detectors, which we refer to as imaging skins, would work as indirect radiation detectors made of light-emitting agents and biocompatible stretchable material. Our vision is to detect scintillation using standard endoscopes routinely employed in minimally invasive surgery. Moreover, surgical robotic systems would ideally be used to apply the imaging skins, allowing for precise control of each component, thereby improving positioning and task repeatability. While still in the exploratory stages, this innovative approach has the potential to improve the detection of tumour margins during RGS by enabling real-time imaging, ultimately improving surgical outcomes

Multi-axis Stiffness Sensing Device for Medical Palpation

This paper presents an innovative hand-held device able to compute stiffness when interacting with a soft object. The device is composed of four linear indenters and a USB camera. The stiffness is computed in real-time, tracking the movements of spherical features in the image of the camera. Those movements relate to the movements of the four indenters when interacting with a soft surface. Since the indenters are connected to springs with different spring constants, the displacement of the indenters varies when interacting with a soft object. The proposed multi-indenting device allows measuring the object's stiffness as well as the pan and tilt angles between the sensor and the surface of the soft object. Tests were performed to evaluate the accuracy of the proposed palpation mechanism against commercial springs of known stiffness. Results show that the accuracy and sensitivity of the proposed device increases with the softness of the examined object. Preliminary tests with silicon show the ability of the sensing mechanism to characterize phantom soft tissue for small indentation. It is noted that the results are not affected by the orientation of the device when probing the surface. The proposed sensing device can be used in different applications, such as external palpation for diagnosis or, if miniaturized, embedded on an endoscopic camera and used in Minimally Invasive Surgery (MIS)

Autonomous pick-and-place using the dVRK.

PURPOSE: Robotic-assisted partial nephrectomy (RAPN) is a tissue-preserving approach to treating renal cancer, where ultrasound (US) imaging is used for intra-operative identification of tumour margins and localisation of blood vessels. With the da Vinci Surgical System (Sunnyvale, CA), the US probe is inserted through an auxiliary access port, grasped by the robotic tool and moved over the surface of the kidney. Images from US probe are displayed separately to the surgical site video within the surgical console leaving the surgeon to interpret and co-registers information which is challenging and complicates the procedural workflow. METHODS: We introduce a novel software architecture to support a hardware soft robotic rail designed to automate intra-operative US acquisition. As a preliminary step towards complete task automation, we automatically grasp the rail and position it on the tissue surface so that the surgeon is then able to manipulate manually the US probe along it. RESULTS: A preliminary clinical study, involving five surgeons, was carried out to evaluate the potential performance of the system. Results indicate that the proposed semi-autonomous approach reduced the time needed to complete a US scan compared to manual tele-operation. CONCLUSION: Procedural automation can be an important workflow enhancement functionality in future robotic surgery systems. We have shown a preliminary study on semi-autonomous US imaging, and this could support more efficient data acquisition

Autonomous Object Handover Using Wrist Tactile Information

Grasping in an uncertain environment is a topic of great interest in robotics. In this paper we focus on the challenge of object handover capable of coping with a wide range of different and unspecified objects. Handover is the action of object passing an object from one agent to another. In this work handover is performed from human to robot. We present a robust method that relies only on the force information from the wrist and does not use any vision and tactile information from the fingers. By analyzing readings from a wrist force sensor, models of tactile response for receiving and releasing an object were identified and tested during validation experiments

A Fluidic Soft Robot for Needle Guidance and Motion Compensation in Intratympanic Steroid Injections

Intratympanic steroid injections are commonly employed in treating ear diseases, such as sudden sensorineural hearing loss or Meniere's disease through drug delivery via the middle ear. Whilst being an effective treatment, the procedure has to be performed by a trained surgeon to avoid delicate regions in the patient's anatomy and is considered painful despite the use of topical anaesthesia. In this letter we introduce a fluid-driven soft robotic system which aims at increasing patient-comfort during the injection by counteracting unwanted needle motion, reducing the cognitive load of the clinician by autonomously identifying sensitive regions in the ear and de-risking the procedure by steering the needle towards the desired injection site. A design comprising of six embedded fluidic actuators is presented, which allow for translation and rotation of the needle as well as adaptive stiffening in the coupling between needle and ear canal. The system's steering-capabilities are investigated and the differential kinematics derived to demonstrate trajectory tracking in Cartesian space. A vision system is developed which enables tracking of anatomical landmarks on the tympanic membrane and thus locating the desired needle insertion site. The integrated system shows the ability to provide a safe guide for the inserted needle towards a desired target direction while significantly reducing needle motion. The proposed tracking algorithm is able to identify the desired needle insertion site and could be employed to avoid delicate anatomical regions

Static kinematics for an antagonistically actuated robot based on a beam-mechanics-based model

Soft robotic structures might play a major role in the 4th industrial revolution. Researchers have successfully demonstrated advantages of soft robotics over traditional robots made of rigid links and joints in several application areas including manufacturing, healthcare and surgical interventions. However, soft robots have limited ability to exert higher forces when it comes to interaction with the environment, hence, change their stiffness on demand over a wide range. One stiffness mechanism embodies tendon-driven and pneumatic air actuation in an antagonistic way achieving variable stiffness values. In this paper, we apply a beammechanics-based model to this type of soft stiffness controllable robot. This mathematical model takes into account the various stiffness levels of the soft robotic manipulator as well as interaction forces with the environment at the tip of the manipulator. The analytical model is implemented into a robotic actuation system made of motorised linear rails with load cells (obtaining applied forces to the tendons) and a pressure regulator. Here, we present and analyse the performance and limitations of our model

Localization of Interaction using Fibre-Optic Shape Sensing in Soft-Robotic Surgery Tools

Minimally invasive surgery requires real-time tool tracking to guide the surgeon where depth perception and visual occlusion present navigational challenges. Although vision-based and external sensor-based tracking methods exist, fibre-optic sensing can overcome their limitations as they can be integrated directly into the device, are biocompatible, small, robust and geometrically versatile. In this paper, we integrate a fibre Bragg grating-based shape sensor into a soft robotic device. The soft robot is the pneumatically attachable flexible (PAF) rail designed to act as a soft interface between manipulation tools and intra-operative imaging devices. We demonstrate that the shape sensing fibre can detect the location of the tools paired with the PAF rail, by exploiting the change in curvature sensed by the fibre when a strain is applied to it. We then validate this with a series of grasping tasks and continuous US swipes, using the system to detect in real-time the location of the tools interacting with the PAF rail. The overall location-sensing accuracy of the system is 64.6%, with a margin of error between predicted location and actual location of 3.75 mm