Tunnel-like environments are often extremely long and large-scale, particularly narrow and confined. This paper investigates the problem of autonomously guiding a robot through a tunnel-like environment. To address this challenge, we initially create a skeleton-based reference curve to compute the normal and tangential direction of the tunnel-like environment. Then we create a barrier function to delineate the impenetrable nature of the environment’s boundary. Subsequently, we employ the principle of vector field to design a novel navigation law that guarantees the continuous advancement of the robot within the tunnel meanwhile simultaneously maintaining a predefined safety margin from the bounds of the environment. Conducted simulation experiments validate the effectiveness of the proposed vector field-based strategy for safely navigating a robot through tunnel-like environments.

Fiber jamming modules (FJMs) offer flexibility and quick stiffness variation, making them suitable for followthe-leader (FTL) motions in continuum robots, which is ideal for minimally invasive surgery (MIS). However, their potential has not been fully exploited, particularly in designing and manufacturing small-sized FJMs with high stiffness variation. Although existing research has focused on factors like fiber materials and geometry to maximize stiffness variation, the results often do not apply to FJMs for MIS due to size constraints. Meanwhile, other factors such as fiber number and packing density, less significant to large FJMs but critical to smallsized FJMs, have received insufficient investigation regarding their impact on the stiffness variation for FTL deployment. In this paper, we design and fabricate FJMs with a diameter of 4mm. Through theoretical and experimental analysis, we find that fiber number and packing density significantly affect both absolute stiffness and stiffness variation. Our experiments confirm the feasibility of using FJMs in a medical FTL robot design. The optimal configuration is a 4mm FJM with 0.4mm fibers at a 56% packing density, achieving up to 3400% stiffness variation. A video demonstration of a prototype robot using the suggested parameters for achieving FTL motions can be found at https://youtu.be/7pI5U0z7kcE.

Accurate time synchronization is crucial for multisensor fusion, which is widely used in mobile robotics, autonomous driving, and virtual reality. Despite many advancements, precise multi-sensor synchronization is still challenging due to the sensors’ internal characteristics, data filtering, disjointed clock reference, and transmission delay caused by operation system scheduling. This paper proposes a novel hardware-based synchronization solution to achieve synchronization in microsecond-level precision. By introducing a Sensor Adaptor board that provides a unified clock reference, the proposed hardware architecture enables high-precision synchronization across multiple sensors. Furthermore, we develop a method for Visual-Inertial time synchronization that actively controls the exposure duration using an ambient light sensor. By managing the IMU clock signal and exposure trigger, we align the camera’s sampling moment with the authentic IMU sampling time and significantly reduce the time discrepancy in the Visual-Inertial system. Experiments are conducted to evaluate the efficiency of the proposed method and system, including comparisons with previous work. The results indicate that our method can achieve precise time synchronization and be successfully implemented in multi-sensor systems.

A common limitation of autonomous tissue manipulation in robotic minimally invasive surgery (MIS) is the absence of force sensing and control at the tool level. Recently, our team has developed miniature force-sensing forceps that can simultaneously measure the grasping and pulling forces during tissue manipulation. Based on this design, here we further present a method to automate tissue traction that comprises grasping and pulling stages. During this process, the grasping and pulling forces can be controlled either separately or simultaneously through force decoupling. The force controller is built upon a static model of tissue manipulation, considering the interaction between the force-sensing forceps and soft tissue. The efficacy of this force control approach is validated through a series of experiments comparing targeted, estimated, and actual reference forces. To verify the feasibility of the proposed method in surgical applications, various tissue resections are conducted on ex vivo tissues employing a dual-arm robotic setup. Finally, we discuss the benefits of multiforce control in tissue traction, evidenced through comparative analyses of various ex vivo tissue resections with and without the proposed method, and the potential generalization with traction on different tissues. The results affirm the feasibility of implementing automatic tissue traction using miniature forceps with multiforce control, suggesting its potential to promote autonomous MIS.

Misalignment between the camera frame and the operator frame is commonly seen in a teleoperated system and usually degrades the operation performance. The effects of such misalignment have not been fully investigated foreye-in-handsystems–systems that have the camera (eye) mounted to the end-effector (hand) to gain compactness in confined spaces such as in endoscopic surgery. This article provides a systematic study on the effects of the camera frame misalignment in a teleoperatedeye-in-handrobot and proposes a simple correction method in the view display. A simulation is designed to compare the effects of the misalignment under different conditions. Users are asked to move a rigid body from its initial position to the specified target position via teleoperation, with different levels of misalignment simulated. It is found that misalignment between the input motion and the output view is much more difficult to compensate by the operators when it is in the orthogonal direction ($\sim$40 s) compared with the opposite direction ($\sim$20 s). An experiment on a real concentric tube robot with aneye-in-handconfiguration is also conducted. Users are asked to telemanipulate the robot to complete a pick-and-place task. Results show that with the correction enabled, there is a significant improvement in the operation performance in terms of completion time (mean 40.6%, median 38.6%), trajectory length (mean 34.3%, median 28.1%), difficulty (50.5%), unsteadiness (49.4%), and mental stress (60.9%).

Keyhole surgery requires highly dexterous snake-like robotic arms capable of bending around anatomical obstacles to access clinical targets that diverge from the direct port-of-access. Design optimization for these robots under patient-specific anatomical constraints is still lacking, particularly concerning the critical metric of dexterity. In this article, we propose an end-to-end design and production workflow for patient-specific surgical manipulators, assessing dexterity using orientability constrained by task space obstacles. In our work, parametric evolutionary optimization maximizes dexterity in patient-specific task spaces for challenging knee arthroscopy operations. We implement our framework in the design of SnakeRaven—a 3-D printed tool to be attached to the RAVEN II surgical robot in a phantom study for knee arthroscopy. The solution achieved more than three times the dexterity of a state-of-the-art rigid instrument and more than twice the dexterity of a volume-based approach for the same task. We further assemble and validate this design by teleoperating the robot to reach the desired clinical targets in a phantom. We also investigate the changes in the design morphology to changes in the task objectives and found an advantage in task specialization. We also observe guidelines for achieving a dexterous design produced by our algorithms.

In minimally invasive surgery, miniaturisation and in situ adjustable stiffness of robotic manipulators are desired features. Previous research proposed a simple and effective tendon-driven curve-joint manipulator design using a variable neutral-line mechanism, which highly satisfies both criteria. A kinematic model was developed for such a manipulator based on the geometry of the structure. However, such a model assumes that joint angles are all equal between disks without a rigorous derivation, and fails if not all the shapes of the disks are identical. Moreover, the model does not involve an analysis of the tension of each tendon. This paper suggested a static model for predicting the articulation of such a manipulator given the applied tensions on driving tendons. It validates the assumption of equally distributed joint angles and works for manipulators with more general configurations of disks and tendons. It also sets a foundation for further development of tension based control and external force estimation. Simulations on Adams were conducted to prove the correctness of the proposed model. A video demonstrating the simulation results can be found via https://youtu.be/MXhL1LGwLtw

Telerobotic systems combined with miniaturised snake-like or elephant-trunk robotic arms can improve the ergonomics and accessibility in minimally invasive surgical tasks such as knee arthroscopy. Such systems, however, are usually designed in a specific and integral approach, making it expensive to adapt to various procedures or patient anatomies. 3D printed instruments with a detachable design can bring the benefits of patient-specific customisation, affordability, and adaptability to new clinical scenarios. However, the integration of such snake-like instruments to standard telerobotic systems can be challenging in terms of design and control. In this study, a teleoperation system is developed to control and steer the pose of SnakeRaven: a 3D printed, customisable snake-like end-effector attached to the RAVEN II platform for the application of fibre-optic knee arthroscopy. Algorithms for the parametric inverse kinematics and mapping between the RAVEN II joint space to the coupled tendon-driven rolling joints are developed. The controller is tested and validated on the physical prototype interfacing with the RAVEN II platform in a teleoperation experiment. A video demonstrating the main results of this paper can be found via https://youtu.be/ApJjR853kIQ

This paper presents a multi-input piezoelectric (PE) energy harvesting circuit, which integrates a Series Synchronized Switch Harvesting on Inductor (S-SSHI) and a Voltage Doubler (VD). The proposed circuit uses the VD topology to reduce the number of diodes used in the resonant loop of the S-SSHI circuit, and hence more power available for the load. Besides, the circuit adopts a parallel connection for multiple different PE transducers, in which an inductor and the VD are re-used to harvest the energy. It improves the inductor's utilization and reduces the complexity of the circuit. The circuit is self-powered and capable of starting even if the battery is drained. Discrete components prototyped the circuit with a cost- effective implementation. The measured results show that compared with full-bridge topology, the proposed circuit increases the power harvested from two different PE transducer three times at the same excitation condition.

introduction Share on Introduction to the Special Section on Cognitive Robotics on 5G/6G Networks Authors: Huimin Lu Kyushu Institute of Technology, Japan Kyushu Institute of Technology, JapanView Profile , Liao Wu University of New South Wales, Australia University of New South Wales, AustraliaView Profile , Giancarlo Fortino University of Calabria (Unical), Italy University of Calabria (Unical), ItalyView Profile , Schahram Dustdar Vienna University of Technology, Austria Vienna University of Technology, AustriaView Profile Authors Info & Claims ACM Transactions on Internet TechnologyVolume 21Issue 4November 2021 Article No.: 91epp 1–3https://doi.org/10.1145/3476466Published:28 September 2021Publication History 1citation36DownloadsMetricsTotal Citations1Total Downloads36Last 12 Months26Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access

Soft robot has demonstrated promise in unstructured and dynamic environments due to unique advantages, such as safe interaction, adaptiveness, easy to actuate, and easy fabrication. However, the highly dissipative nature of elastic materials results in small stiffness of soft robot which limits certain functions, such as force transmission, position accuracy, and load capability. In this paper, we present a novel articulated soft robot with variable stiffness. The robot is constructed by rigid joints and compliant bistable structures in series. Each joint can be independently locked through triggering the bistable structure to touch the mechanical constrain. Thus, the bending stiffness of the joint can be magnified which increases the stiffness of the articulated soft robot. Through this construction method, even driven by only one servomotor, the robot demonstrates variable workspace and stiffness which have the potential of dexterous manipulation and maintaining shape under tip load.

While Product of Exponentials (POE) formula has been gaining maturity in modeling the kinematics of a serial-link robot, the Denavit-Hartenberg (D-H) notation is still the most widely used due to its intuitive and concise geometric interpretation of the robot. This paper has developed an analytical solution to automatically convert a POE model into a D-H model for a robot with revolute, prismatic, and helical joints, which are the complete set of three basic one degree of freedom lower pair joints for constructing a serial-link robot. The conversion algorithm developed can be used in applications such as calibration where it is necessary to convert the D-H model to the POE model for identification and then back to the D-H model for compensation. The equivalence of the two models proved in this paper also benefits the analysis of the identifiability of the kinematic parameters. It is found that the maximum number of identifiable parameters in a general POE model is 5h+4r+2t+n+6 where h, r, t, and n stand for the number of helical, revolute, prismatic, and general joints, respectively. It is also suggested that the identifiability of the base frame and the tool frame in the D-H model is restricted rather than the arbitrary six parameters as assumed previously.

Endowing continuum robots with compliance while it interacts with the internal environment of the human body is essential to prevent damage to the robot and the surrounding tissues. Compared with passive compliance, active compliance has the advantages in terms of increasing the force transmission ability and improving safety with monitored force output. Previous studies have demonstrated that active compliance can be achieved based on a complex model of the mechanics combined with a traditional machine learning technique such as a support vector machine. This paper proposes a recurrent neural network (RNN) based approach that avoids the complexity of modeling while capturing nonlinear factors such as hysteresis, friction and delay of the electronics that are not easy to model. The approach is tested on a 3-tendon single-segment continuum robot with force sensors on each cable. Experiments are conducted to demonstrate that the continuum robot with an RNN based feed-forward controller is capable of responding to external forces quickly and entering an unknown environment compliantly.

Robotic challenges like the Amazon Picking Challenge (APC) or the DARPA Challenges are an established and important way to drive scientific progress. They make research comparable on a well-defined benchmark with equal test conditions for all participants. However, such challenge events occur only occasionally, are limited to a small number of contestants, and the test conditions are very difficult to replicate after the main event. We present a new physical benchmark challenge for robotic picking: the ACRV Picking Benchmark. Designed to be reproducible, it consists of a set of 42 common objects, a widely available shelf, and exact guidelines for object arrangement using stencils. A well-defined evaluation protocol enables the comparison of complete robotic systems - including perception and manipulation - instead of sub-systems only. Our paper also describes and reports results achieved by an open baseline system based on a Baxter robot.

When a robot is required to perform specific tasks defined in the world frame, there is a need for finding the coordinate transformation between the kinematic base frame of the robot and the world frame. The kinematic base frame used by the robot controller to define and evaluate the kinematics may deviate from the mechanical base frame constructed based on structural features. Besides, by using kinematic modeling rules such as the product of exponentials (POE) formula, the base frame can be arbitrarily located, and does not have to be related to any feature of the mechanical structure. As a result, the kinematic base frame cannot be measured directly. This paper proposes to find the kinematic base frame by solving a hand-eye calibration problem using 3D position measurements only, which avoids the inconvenience and inaccuracy of measuring orientations and thus significantly facilitates practical operations. A closed-form solution and an iterative solution are explicitly formulated and proved effective by simulations. Comprehensive analyses of the impact of key parameters to the accuracy of the solution are also carried out, providing four guidelines to better conduct practical operations. Finally, experiments on a 7-DOF industrial robot are performed with an optical tracking system to demonstrate the superiority of the proposed method using position data only over the method using full pose data.

Conventional concentric tube robots (CTRs) have low dexterity at the tip, which does not fit the requirements of complicated operations in minimally invasive surgery. A 2mm diameter cable-driven micro end effector is designed and fabricated for CTRs to increase dexterity in confined spaces. The end-effector is made by a simple fabrication procedure and is a combination of readily available materials such as polyolefin tube, acrylic and steel strings. If mass produced, the end effector has the potential to be made into a single use disposable medical tool. This paper discusses the geometric design, fabrication process and force analysis of the end effector. Experiments are conducted on the prototype to validate the derivation. In addition, cases are discussed around the use of the end effector.

Comprised of multiple telescoptic precurved tubes that can independently rotate and translate, concentric tube robots (CTRs) are favorable in minimally invasive surgeries thanks to their small size and considerable dexterity along with curvilinear accessibility. However, there is a lack of investigation on improvement of the surgeons' perception which in turn can be used to guide the telemanipulation. In this work, we proposed an eye-in-hand configuration for the concentric tube robot by adding an endoscope to the tip of the inner tube, which provides direct and intuitive visual sensing ability for the operator. Based on this visual feedback, we further developed two frameworks for the hybrid control of CTR, namely Teleoperation Before Visual Servoing (TBVS) and Teleoperation During Visual Servoing (TDVS). The structures of these two frameworks were elaborated with key algorithms derived. The effectiveness of the proposed methods were demonstrated through a series of experiments both in free space and in a confined environment (inside a skull model). The results manifested that the visual guidance had the potential of assisting the operator to control the CTR more efficiently.

Multirobot comanipulation shows great potential in surpassing the limitations of single-robot manipulation in complicated tasks such as robotic surgeries. However, a dynamic multirobot setup in unstructured environments poses great uncertainties in robot configurations. Therefore, the coordination relationships between the end-effectors and other devices, such as cameras (hand–eye calibration) and tools (tool–flange calibration), as well as the relationships among the base frames (robot–robot calibration) have to be determined timely to enable accurate robotic cooperation for the constantly changing configuration of the systems. We formulated the problem of hand–eye, tool–flange, and robot–robot calibration to a matrix equation$\mathbf{AXB=YCZ}$. A series of generic geometric properties and lemmas were presented, leading to the derivation of the final simultaneous algorithm. In addition to the accurate iterative solution, a closed-form solution was also introduced based on quaternions to give an initial value. To show the feasibility and superiority of the simultaneous method, two nonsimultaneous methods were compared through thorough simulations under various robot movements and noise levels. Comprehensive experiments on real robots were also performed to further validate the proposed methods. The comparison results from both simulations and experiments demonstrated the superior accuracy and efficiency of the proposed simultaneous calibration method.

Continuum tubular robots, which are constructed by telescoping pre-curved elastic tubes, are capable of balancing the force application and steerability during minimally invasive surgeries. These devices are able to reach the desired surgical sites in body cavities without colliding with critical blood vessels, nerves and tissues. However, the motion planning of continuum tubular robots is quite challenging because of their complicated kinematics as well as the high dimensional configuration space. In this paper, a sampling-based motion planning method is proposed based on the Rapidly-exploring Random Tree (RRT) algorithm for continuum tubular robots in 3D environments, such as medullary cavities. The proposed motion planner enables a continuum tubular robot to maneuver roughly along the central axis of the statistical humerus atlas in an approximate follow-the-leader manner. The experiment results have demonstrated the effectiveness and superiority of the proposed motion planning algorithm.

This paper proposes an algorithm for robotic kinematic calibration based on a minimal product of exponentials (POE)-based model for the applications where only position measurements are required. Both joint zero-offset errors and initial frame twist error can be involved in this model. Analysis of the identifiability of these errors shows that at most six elements of these parameters can be identified. It also suggests that at least three noncollinear points on the end-effector should be measured to maximize the identifiability. Compared with the traditional POE-based model with full pose (position and orientation) measurements, the minimal model with only position measurements outperforms in terms of convenience, efficiency, and accuracy. Note to Practitioners-Kinematic calibration is pivotal to improve the position accuracy of a robot. To avoid the disadvantages of measuring the orientation of the end-effector during calibration, an algorithm using only position measurements is presented, with which one needs only position measurements of several points fixed on the end-effector without orientation information during the whole calibration process. This will greatly facilitate the scheme design as well as the practical operations. The identifiability of the parameters is then analyzed with two conclusions: 1) at most six elements of the joint zero-offsets and the initial frame twist in total can be identified simultaneously and 2) at least three points on the end-effector which are not collinear should be measured so as to make the identifiability maximum. According to these conclusions, one should carefully select parameters to formulate the error model and measure sufficient points on the end-effector during the calibration procedure.

Tasks that are too hard for single robot can be easily carried out by multiple robots in a cooperative manner. If some/all robots have mobile bases, the cooperation is subjected to great uncertainties in both the robotic system and environment. Therefore, the relationships among all the base frames (robot-robot calibration) and the relationships between the end-effectors and the other devices such as cameras and tools (hand-eye and tool-flange calibrations) have to be calculated to enable the robots to cooperate. To address these challenges, in this paper, we propose a simultaneous hand-eye, tool-flange and robot-robot calibration method. Thorough simulations are conducted to show the superiority of the proposed simultaneous method under different noise levels and various numbers of robot movements. Furthermore, the comparison to two non-simultaneous calibration methods has also been carried out to show the efficiency and robustness of the proposed simultaneous method.