This work addresses the gravity balancing of a 2R1T (2 rotations – 1 translation) mechanism with remote center of motion. A previously developed balancing solution is modified and applied to a prototype, and test results are presented. The mechanism is an endoscope holder for minimally invasive transnasal pituitary gland surgery. In this surgery, the endoscope is inserted through a nostril of the patient through a natural path to the pituitary gland. During the surgery, it is vital for the manipulator to be statically balanced so that in case of a motor failure, the patient is protected against any harmful motion of the endoscope. Additionally, static balancing takes the gravitational load from the actuators and hence facilitates the control of the mechanism. The mechanism is a 2URRR-URR type parallel manipulator with three legs. The payload mass is distributed to the legs on the sides. By using counter-masses for two links of each leg, the center of mass of each leg is lumped on the proximal link which simplifies the problem of balancing of a two degree-of-freedom inverted pendulum. The two proximal links with the lumped mass are statically balanced via springs. Dynamic simulations indicate that when the mechanism is statically balanced, generated actuator torques are reduced by 93.5%. Finally, the balancing solution is implemented on the prototype of the manipulator. The tests indicate that the manipulator is statically balanced within its task space when the actuators are disconnected. When the actuators are connected, the torque requirements decrease by about 37.8% with balancing.

Exoskeleton robot can assist people and reduce energy consumption when they walk with heavy weight, so as to protect their health and travel longer distances. This work analyzes the cross gait during walking and designs a dual-drive four joint time-sharing assistance exoskeleton system, which controls the four joints through two motors to realize the assistance to the wearer’s movement process. The control curve and adaptive control algorithm are designed to help different people with various walking gaits and speeds, the effectiveness of exoskeleton system is proved by testing metabolism. When the exoskeleton wearer carries 25 kg weight (load equal to 36% of body mass) and travels at the average speed of 5 km/h, the metabolic rate of the exoskeleton wearers decreases by an average of 7.79%, the reduction magnitude is comparable to the effect of taking off 7.33 kg during walking.

Clinical observations and subjective judgements have traditionally been used to evaluate patients with muscular and neurological disorders. As a result, identifying and analyzing functional improvements are difficult, especially in the absence of expertise. Quantitative assessment, which serves as the motivation for this study, is an essential prerequisite to forecast the task of the rehabilitation device in order to develop rehabilitation training. This work provides a quantitative assessment tool for muscle weakness in the human upper limbs for robotic-assisted rehabilitation. The goal is to map the assessment metrics to the recommended rehabilitation exercises. Measurable interaction forces and muscle correlation factors are the selected parameters to design a framework for muscular nerve cell condition detection and appropriate limb trajectory selection. In this work, a data collection setup is intended for extracting muscle intervention and assessment using MyoMeter, Goniometer and surface electromyography data for upper limbs. Force signals and human physiological response data are evaluated and categorized to infer the relevant progress. Based upon the most influencing muscles, curve fitting is performed. Trajectory-based data points are collected through a scaled geometric Open-Sim musculoskeletal model that fits the subject’s anthropometric data. These data are found to be most suitable to prescribe relevant exercise and to design customized robotic assistance. Case studies demonstrate the approach’s efficacy, including optimally synthesized automated configuration for the desired trajectory.

Cable-driven parallel robots (CDPRs) are still gaining attention thanks to their interesting characteristics compared to serial or classic parallel manipulators. However, the limited range of rotation of their end-effectors reduces their application fields to predominantly translational movements. In this context, the issue of extending the rotational workspace of a CDPR while maintaining a compact robot structure is addressed in this paper. This work is motivated by the need to find the optimal CDPR for upper limb rehabilitation allowing to assist the patient’s hand along a set of prescribed tasks. Firstly, a reconfigurable robot, where the motors’ locations are movable, is proposed in order to help reaching all the prescribed poses. Although this solution presents promising results compared to classical CDPRs, it involves a sizable robot structure inadequate to rehabilitation application. To improve the obtained solution, another approach is proposed, based on combining the large translational workspace of CDPRs and the large rotational workspace of serial manipulators. The optimal structure of a hybrid robot will be considered for the prototype design.

Although robotic or robot-assisted surgery has been increasingly used by many surgical disciplines, its application in cranial or skull base surgery is still in its infancy. Master-slave teleoperation setting of these robotic systems enables these surgical procedures to be replicated in a virtual reality environment for surgeon training purposes. A variety of teleoperation modes were previously determined with respect to the motion capability of the surgeon’s hand that wears the ring as the surgeon handles a surgical tool inside the surgical workspace. In this surgery training simulator developed for a robot-assisted endoscopic skull base surgery, a new strategy is developed to identify the preferred motion axes of the surgeon. This simulator is designed specifically for tuning the teleoperation system for each surgeon via the identification. This tuning capability brings flexibility to adjust the system operation with respect to the motion characteristics of the surgeon.

Robot-assisted minimally invasive surgery (MIS) has shown tremendous advances over the traditional technique. A crucial challenge for developing a MIS robot is the kinematic design of the remote center-of-motion (RCM) mechanism. In this paper, a class of spatial RCM mechanism is analyzed. They are obtained by generating virtual parallelograms. The main process is to construct a line that passes through a fixed point under the mechanical constraint. The axis of the surgical tool is then constrained to parallel with that line. Hence, due to the geometrical feature of the parallel lines, the axis of the surgical tool will always pass through a fixed point, i.e., the RCM point. Due to the specially designed structure, the fixed point does not need to be physically belonging to the mechanism. The geometrical analysis method is employed to obtain the closed-form solution of the forward kinematics of the proposed mechanisms. Due to the high load capacity of parallel mechanism, the robots based on the proposed RCM mechanisms have promising applications as an external positioner to be used in robotic single-port surgeries.

The autonomous underwater vehicle (AUV) has a problem with feature loss when recognizing small targets underwater. At present, algorithms usually use multi-scale feature extraction to solve the problem, but this method increases the computational effort of the algorithm. In addition, low underwater light and turbid water result in incomplete information on target features. This paper proposes an enhanced dilated convolution framework (EHDC) for underwater blurred target recognition. Firstly, this paper extracts small target features through hybrid dilated convolution networks, increasing the perceptive field of the algorithm without increasing the computational power of the algorithm. Secondly, the proposed algorithm learns spatial semantic features through an adaptive correlation matrix and compensates for the missing features of the target. Finally, this paper fuses spatial semantic features and visual features for the recognition of small underwater blurred targets. Experiments show that the proposed method improves the recognition accuracy by 1.04% compared to existing methods when recognizing small underwater blurred targets.

One of the major challenges in the science of maxillofacial radiology imaging is the various artifacts created in images taken by cone beam computed tomography (CBCT) imaging systems. Among these artifacts, motion artifact, which is created by the patient, has adverse effects on image quality. In this paper, according to the conditions and limitations of the CBCT imaging room, the goal is the design and development of a cable-driven parallel robot to create repeatable movements of a dry skull inside a CBCT scanner for studying motion artifacts and building up reference datasets with motion artifacts. The proposed robot allows a dry skull to execute motions, which were selected on the basis of clinical evidence, with 3-degrees of freedom during imaging in synchronous manner with the radiation beam. The kinematic model of the robot is presented to investigate and describe the correlation between the amount of motion and the pulse width applied to DC motors. This robot can be controlled by the user through a smartphone or laptop wirelessly via a Wi-Fi connection. Using wireless communication protects the user from harmful radiation during robot driving and functioning. The results show that the designed robot has a reproducibility above 95% in performing various movements.

A welding path can be planned effectively for spot welding robots using the ant colony algorithm, but the initial parameters of the ant colony algorithm are usually selected through human experience, resulting in an unreasonable planned path. This paper combines the ant colony algorithm with the particle swarm algorithm and uses the particle swarm algorithm to train the initial parameters of the ant colony algorithm to plan an optimal path. Firstly, a mathematical model for spot welding path planning is established using the ant colony algorithm. Then, the particle swarm algorithm is introduced into the ant colony algorithm to find the optimal combination of parameters by treating the initial parameters $\alpha$ and $\beta$ of the ant colony algorithm and as two-dimensional coordinates in the particle swarm algorithm. Finally, the simulation analysis was carried out using MATLAB to obtain the paths of the improved ant colony algorithm for six different sets of parameters with an average path length of 10,357.7509 mm, but the average path length obtained by conventional algorithm was 10,830.8394 mm. Convergence analysis of the improved ant colony algorithm showed that the average number of iterations was 17. Therefore, the improved ant colony algorithm has higher solution quality and converges faster.

There are various matrices to represent parallel mechanisms. It is essential to design a kind of approach to not only denote the parallel structures but also disclose the joint directions. In this paper, a novel methodology called the kinematic joint matrix (KJM) is proposed. It possesses the mapping relations with parallel manipulators with three kinds of kinematic joints. The size of such matrix is smaller when compared with that of topology matrix. A series of two to six degrees-of-freedom parallel architectures is denoted by the KJM. A convenient approach using a special block diagram is introduced to distinguish various kinds of kinematic joint matrices. In addition, detailed comparisons between KJM and topology matrix are investigated. Three regulations are proposed for the latter to be applicable to parallel mechanisms.

Effective searching for target objects in indoor scenes is essential for household robots to perform daily tasks. With the establishment of a precise map, the robot can navigate to a fixed static target. However, it is difficult for mobile robots to find movable objects like cups. To address this problem, we establish an object search framework that combines navigation map, semantic map, and scene graph. The robot updates the scene graph to achieve a long-term target search. Considering the different start positions of the robots, we weigh the distance the robot walks and the probability of finding objects to achieve global path planning. The robot can continuously update the scene graph in a dynamic environment to memorize the position relation of objects in the scene. This method has been realized in both simulation and real-world environments. The experimental results show the feasibility and effectiveness of this method.

The lack of specialized personnel and assistive technology to assist in rehabilitation therapies is one of the challenges facing the health sector today, and it is projected to increase. For researchers and engineers, it represents an opportunity to innovate and develop devices that improve and optimize rehabilitation services for the benefit of society. Among the different types of injuries, hand injuries occur most frequently. These injuries require a rehabilitation process in order for the hand to regain its functionality. This article presents the fabrication and instrumentation of an end-effector prototype, based on a five-bar configuration, for finger rehabilitation that executes a natural flexion-extension movement. The dimensions were obtained through the gradient method optimization and evaluated through Matlab. Experimental tests were carried out to demonstrate the prototype’s functionality and the effectiveness of a five-bar mechanism acting in a vertical plane, where gravity influences the mechanism’s performance. Position control using fifth-order polynomials with via points was implemented in the joint space. The design of the end-effector was also evaluated by performing a theoretical comparison, calculated as a function of a real flexion-extension trajectory of the fingers and the angle of rotation obtained through an IMU. As a result, controlling the two degrees of freedom of the mechanism at several points of the trajectory assures the end-effector trajectory and therefore the fingers’ range of motion, which helps for full patient recovery.

With the excellent characteristic of intrinsic compliance, pneumatic artificial muscle can improve the interaction comfort of wearable robotic devices. This paper resolves the safety tracking control problem of a pneumatically actuated lower limb exoskeleton system. A single-parameter adaptive fuzzy control strategy is proposed with high control precision and full state constraints for the safe gait training tasks. Based on the barrier Lyapunov function, all signals in the closed-loop system can be bounded in finite time, which guarantees the deviation of the exoskeleton’s moving trajectory within a bounded range. Furthermore, with the proposed single-parameter adaptive law, the computational burden and the complexity of the control are reduced significantly. Finally, numerical simulations, no-load tracking experiments, and passive and active gait training experiments with healthy subjects validate the effectiveness of the proposed method.

In the field of robot reinforcement learning (RL), the reality gap has always been a problem that restricts the robustness and generalization of algorithms. We propose Simulation Twin (SimTwin) : a deep RL framework that can help directly transfer the model from simulation to reality without any real-world training. SimTwin consists of a RL module and an adaptive correct module. We train the policy using the soft actor-critic algorithm only in a simulator with demonstration and domain randomization. In the adaptive correct module, we design and train a neural network to simulate the human error correction process using force feedback. Subsequently, we combine the above two modules through digital twin to control real-world robots, correct simulator parameters by comparing the difference between simulator and reality automatically, and then generalize the correct action through the trained policy network without additional training. We demonstrate the proposed method in an open cabinet task; the experiments show that our framework can reduce the reality gap without any real-world training.

Electronic skin (e-skin) is playing an increasingly important role in health detection, robotic teleoperation, and human-machine interaction, but most e-skins currently lack the integration of on-site signal acquisition and transmission modules. In this paper, we develop a novel flexible wearable e-skin sensing system with 11 sensing channels for robotic teleoperation. The designed sensing system is mainly composed of three components: e-skin sensor, customized flexible printed circuit (FPC), and human-machine interface. The e-skin sensor has 10 stretchable resistors distributed at the proximal and metacarpal joints of each finger respectively and 1 stretchable resistor distributed at the purlicue. The e-skin sensor can be attached to the opisthenar, and thanks to its stretchability, the sensor can detect the bent angle of the finger. The customized FPC, with WiFi module, wirelessly transmits the signal to the terminal device with human-machine interface, and we design a graphical user interface based on the Qt framework for real-time signal acquisition, storage, and display. Based on this developed e-skin system and self-developed robotic multi-fingered hand, we conduct gesture recognition and robotic multi-fingered teleoperation experiments using deep learning techniques and obtain a recognition accuracy of 91.22%. The results demonstrate that the developed e-skin sensing system has great potential in human-machine interaction.

In dynamic outdoor environments characterized by turbulent airflow and intermittent odor plumes, robotic odor plume tracking remains challenging, because existing algorithms heavily rely on manually tuning or learning from expert experience, which are hard to implement in an unknown environment. In this paper, a multi-continuous-output Takagi–Sugeno–Kang fuzzy system was designed and tuned with reinforcement learning to solve the robotic odor source localization problem in dynamic odor plumes. Based on the Lévy Taxis plume tracking controller, the proposed fuzzy system determined the parameters of the controller based on the robot’s observation and guided the robot to turn and move towards the odor source at each searching step. The trained fuzzy system was tested in simulated filament-based odor plumes dispersed by a changing wind field. The results showed that the performance of the proposed fuzzy system-based controller trained with reinforcement learning can achieve a similar success rate and higher efficiency compared with a manually tuned and well-designed fuzzy system-based controller. The fuzzy system-based plume tracking controller was also validated through real robotic experiments.

Transbronchial lung biopsy is an effective and less-invasive treatment for the early diagnosis of lung cancer. However, the limited dexterity of existing endoscopic instruments and the complexity of bronchial access prevent the application of such procedures mainly for biopsy and diagnosis. This paper proposes a flexible robot for transbronchial lung biopsy with a cable-driven mechanism-based flexible manipulator. The robotic system of transbronchial lung biopsy is presented in detail, including the snake-bone end effector, the flexible catheters and the actuation unit. The kinematic analysis of the snake-bone end effector is conducted for the master-slave control. The experimental results show that the end effector reaches the target nodule through a narrow and tortuous pathway in a bronchial model. In conclusion, the proposed robotic system contributes to the field of advanced endoscopic surgery with high flexibility and controllability.

A method is presented for configuration selection to obtain the best tip-over stability of a modular reconfigurable mobile manipulator (MRMM) under various application situations. The said MRMM consists of a modular reconfigurable robot (MRR) mounted on a mobile platform. The MRR in different configurations creates different wrenches onto the mobile platform, leading to different tip-over moments of the MRMM, even though the joint speeds or tip speeds remain the same. The underlying problem pertains to selecting one configuration of MRR for reconfiguration that would obtain the best tip-over stability under a given application. First, all the permissible configurations are identified through an enumeration method. Then, the feasible configurations are determined based on application-oriented workspace classifications. At last, two workspace indices, vertical reach and horizontal reach, are used to select an optimal configuration. The tip-over stability analysis and evaluation of MRMM are carried out for verification for three cases including vertical, horizontal, and general 3D space applications. The results demonstrate the effectiveness of the proposed method.