Existing visual simultaneous localization and mapping (SLAM) systems struggle with loop closure under significant viewpoint variations, such as revisiting the same place orthogonally or oppositely. This limitation primarily stems from the lack of viewpoint invariance in both macroscopic place definition and microscopic feature description. Based on the crucial insight that geometric information is more viewpoint-invariant than visual information, we create map segments via leveraging the position and coplanarity distribution of map elements within the map constructed by monocular SLAM to overcome the limitations of frame-based place definition and extract perspective-invariant ORB (PRIOR) features via accounting for local surface perspective distortions to enhance perspective invariance without the need for costly perspective invariance estimation or additional depth data. We further utilize map segments and PRIOR features to hierarchically detect and correct loop closures with a coarse-to-fine geometric consistency check. We integrate our novelties into the prevalent SLAM framework, thereby proposing PRIOR-SLAM, which achieves state-of-the-art performance in feature matching and retrieval, visual place recognition, and loop closure under large viewpoint changes.

This paper reports the design, simulation, and experiment of a robotic leg prototype named Cury, which has the potential to achieve minimal clearance and excellent backdrivability. Inspired by human walking data, the actuator design incorporates four-bar linkages and ball screws and is further optimized to meet the torque requirement. The Webots simulation is used to obtain the closed-loop chain description of the robotic leg from fits the URDF specification, and this simulation is used to assess the actuator output requirements at a given predefined trajectory. Leveraging customized ac motors and drives, Cury demonstrates satisfactory trajectory tracking performance using a simple controller. The motor drive design files and Webots simulation files are open-sourced.

The limited workspace and complex singularity issues are predominant factors impeding the clinical applicability of fracture reduction parallel robots. To address these challenges, this paper proposes a novel redundant parallel mechanism (NRPM) for robotic-assisted fracture reduction with an enlarged workspace and enhanced dexterity capabilities based on the traditional Stewart parallel mechanism (SPM). With six redundant degrees-of-freedom (DOFs) added to the novel mechanism, the kinematics of NRPM needs to be thoroughly analyzed. Furthermore, the calculation of its workspace and determination of its dexterity are deduced. Both the analytical simulation and real experiment results demonstrated the effectiveness and superior performance of the proposed NRPM compared to SPM.

Accurate robot kinematic modelling is a major component for autonomous robot control to guarantee safety and precision during task execution. In surgical robotics complex robotic structures and actuation mechanisms are generally employed, therefore machine learning techniques can be adopted to build the model of the robot. Probabilistic neural networks are a class of learning approaches that provide information about the uncertainty of the learnt models. In this work we compare two different probabilistic neural networks (Bayesian and Evidential Neural Networks) to model the kinematics of a surgical robotic instrument and propose a control strategy based on Hierarchical Quadratic Programming (HQP) capable of exploiting the model uncertainty to improve the accuracy and safety of the controller. Simulation and real world experiments on different autonomous path tracking tasks show that the model uncertainty highly affects the control performances and prove the effectiveness of the proposed controller in improving task execution.Note to Practitioners—The push towards reducing invasiveness and patient’s traumas in surgery has lead to the requirement of miniaturized and highly articulated robots. This however comes at the cost of having systems that are hard to model and control, which is one of the major limitations for autonomy in surgical robotics. Machine learning has become very effective in modelling complex systems and probabilistic approaches additionally allow estimating the confidence of the learnt model. In robotics field where high precision is required to perform an autonomous task, like in minimally invasive surgery, the robot model needs to be very accurate and controllers need to guarantee safety in performing the desired task, while satisfying additional motion constraints imposed by the application scenario. This work proposes the use of probabilistic neural networks to model the complexity of a surgical robotic instrument and a control strategy capable of ensuring safety by maximizing model’s confidence and guaranteeing satisfaction of imposed motion constraints. In this work a macro-micro manipulator setup is employed, consisting of an articulated surgical robotic instrument connected to a serial-link manipulator. The proposed modelling and control approaches can be used in any other field where controllers need to highly rely on the robot model due to limitations in using external sensors and where leveraging information about model’s confidence can be beneficial. Currently, the work focuses only on pure kinematic modelling and control, thus neglecting any possible interaction with the environment. Future work will focus on addressing this limitation in order to ensure proper force control and effective autonomy.

The stiffness analysis of tendon-driven continuum robots is highly demanded in human-robot interaction applications. Although pretension is a common approach to enhance the stiffness of these robots with tendon-displacement-controlled strategies, investigating and analyzing the relationship between stiffness and pretension is challenging. To address this, we proposed a novel Cosserat rod-based model for tendon-driven continuum robots, which takes into account both tendon displacement and tendon elasticity constraints. Through simulation tests, we established the relationship between stiffness analysis and the pretension effect based on the proposed methods.

Hearing aid devices have been around for decades, while most of them focus on sound amplification and SNR improvement. This paper proposes an artificial ear based on the vibrotactile feedback. The speech signal is converted into the vibrotactile devices placed around the subject’s ear through the speech recognition algorithm and pattern coding method. Preliminary experiments on the prototype consisting of six motors which has shown that the recognition accuracy of letters and daily sentences reached 90%. The learning time of interpreting the vibrotactile signals could be less than four times that in real-time conversation, proving the feasibility of the proposed device for real-life application.

Most conventional training schemes require trainees to perform repetitive operations. In this way, novices usually require a long learning period and lack personalized training assistance. To address the above issues, we propose a new training framework based on fuzzy inference. Firstly, a modified fuzzy C-means (FCM) classifier is utilised to partition the tasks based on human motor behaviours. Operation performance is comprehensively assessed in each subtask by task-based criterion and the corresponding results are fed back to the trainee in real time during the subtask execution. Once the trainee reaches the operational standard of each subtask, he/she can proceed the next one. Distinguished from traditional repetitive training without intervention, the trainee can leverage purposeful modifications and repetitions in a closed-loop manner. The expertise degree of trainees can lead to differences in training time, avoiding unnecessarily long training sessions for experienced trainees. The proposed method is therefore suitable for the parallel training with different levels of operation. Comparative experiments on microvascular anastomosis task have demonstrated higher training efficiency of the proposed training strategy.

In minimally invasive robotic surgery, the surgical instrument is usually inserted inside the patient’s body through a small incision, which acts as a remote center of motion (RCM). Serial-link manipulators can be used as macro robots on which microsurgical robotic instruments are mounted to increase the number of degrees of freedom of the system and ensure safe task and RCM motion execution. However, the surgical instrument needs to be placed in an appropriate configuration when completing the motion tasks. The contribution of this article is to present a novel framework that preoperatively identifies the best base configuration, in terms of Roll, Pitch, and Yaw angles, of the microsurgical instrument with respect to the macro serial-link manipulator’s end effector in order to achieve the maximum accuracy and dexterity in performing specified tasks. The framework relies on hierarchical quadratic programming for the control, genetic algorithm for the optimization, and on a resilience to error strategy to make sure deviations from the optimum do not affect the system’s performance. Simulation results show that the mounting configuration of the surgical instrument significantly impacts the performance of the whole macro–micro manipulator in executing the desired motion tasks, and both the simulation and experimental results demonstrate that the proposed optimization method improves the overall performance.

The traditional master-slave teleoperation relies on human expertise without correction mechanisms, resulting in excessive physical and mental workloads. To address these issues, a co-pilot-in-the-loop control framework is investigated for cooperative teleoperation. A deep deterministic policy gradient (DDPG) based agent is realised to effectively restore the master operators' intents without prior knowledge on time delay. The proposed framework allows for introducing an operator (i.e., copilot) to generate commands at the slave side, whose weights are optimally assigned online through DDPG-based arbitration, thereby enhancing the command robustness in the case of possible human operational errors. With the help of interval type-2 (IT2) Takagi-Sugeno (T-S) fuzzy identification, force feedback can be reconstructed at the master side without a sense of delay, thus ensuring the telepresence performance in the force-sensor-free scenarios. Two experimental applications validate the effectiveness of the proposed framework.

Robot modelling is an essential part to properly understand how a robotic system moves and how to control it. The kinematic model of a robot is usually obtained by using Denavit-Hartenberg convention, which relies on a set of parameters to describe the end-effector pose in a Cartesian space. These parameters are assigned based on geometrical considerations of the robotic structure, however, the assigned values may be inaccurate. The purpose of robot kinematic calibration is therefore to find optimal parameters which improve the accuracy of the robot model. In this work we present Kalibrot, an open source Matlab package for robot kinematic calibration. Kalibrot has been designed to simplify robot calibration and easily assess the calibration results. Beside computing the optimal parameters, Kalibrot provides a visualization layer showing the values of the calibrated parameters, what parameters can be identified, and the calibrated robotic structure. The capabilities of the package are here shown through simulated and real world experiments.

Robotic dual-arm twisting is a common but very challenging task in both industrial production and daily services, as it often requires dexterous collaboration, a large scale of end-effector rotating, and good adaptivity for object manipulation. Meanwhile, safety and efficiency are primary concerns for robotic dual-arm coordinated manipulation. Thus, the normally adopted fully automated task execution approaches based on environmental perception and motion planning techniques are still inadequate and problematic for the arduous twisting tasks. To this end, this paper presents a novel strategy of the dual-arm coordinated control for twisting manipulation based on the combination of optimized motion planning for one arm and real-time telecontrol with human intelligence for the other. The analysis and simulation results showed it can achieve collision and singularity free for dual arms with enhanced dexterity, safety, and efficiency.

Snake-like robots for endoscopic surgery make it possible to reach deep-seated lesions. With the use of small flexible tendon-driven instruments, it is possible to perform bimanual micro-surgical tasks that are challenging for standard endoscopic surgeries. Existing devices, however, lack articulated wrists and rolling motion of the end-effector. This paper presents a new instrument design with a distal-roll gripper for snake-like robots. The developed 5 DoFs miniaturized instruments with a diameter of 3 mm enable the deployment into narrow endoluminal channels. Issues related to actuation coupling, tendon slack, and backlash are addressed. Experimental results show that the distal-roll gripper can rotate 106°, and the actuated joints can achieve good repeatability and accuracy with the proposed compensation control scheme.