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Six-dimensional pose estimation task predicts its 3D rotation matrix and 3D translation matrix in the world coordinate system by inputting the color image or depth image of the target object. Existing methods usually use deep neural networks to directly predict or regress object poses based on keypoint methods. The prediction results usually have deviations depending on whether the surface shape of the object is prominent or not and the size of the object. To solve this problem, we propose the six-dimensional pose estimation based on multi-task parameter sharing (PMP) framework to incorporate object category information into the pose estimation network through the form of an object classification auxiliary task. First, we extract the image features and point cloud features of the target object separately, and fuse them point by point; then, we share the confidence of each keypoint in pose estimation task and the knowledge of the classification task, get the key points with higher confidence, and predict the object pose; finally, the obtained object pose is passed through an iterative optimization network to obtain the final pose. The experimental results on the LineMOD dataset show that the proposed method can improve the accuracy of pose estimation and narrow the gap in the prediction accuracy of objects with different shapes. We also tested on a new dataset of small-scale objects, which contains object RGBD images and accurate 3D point cloud information. The proposed method is applied to the grasping experiment on the UR5 robotic arm, which satisfies the real-time pose estimation results during the grasping process. [ABSTRACT FROM AUTHOR]

In engineering materials, surface anisotropy is known in certain textured patterns that appear during the manufacturing process. In biology, there are numerous examples of mechanical systems which combine anisotropic surfaces with the motion, elicited due to some actuation using muscles or stimuli-responsive materials, such as highly ordered cellulose fiber arrays of plant seeds. The systems supplemented by the muscles are rather fast actuators, because of the relatively high speed of muscle contraction, whereas the latter ones are very slow, because they generate actuation depending on the daily changes in the environmental air humidity. If the substrate has ordered surface profile, one can expect certain statistical order of potential trajectories (depending on the order of the spatial distribution of the surface asperities). If not, the expected trajectories can be statistically rather random. The same presumably holds true for the artificial miniature robots that use actuation in combination with frictional anisotropy. In order to prove this hypothesis, we developed numerical model helping us to study abovementioned cases of locomotion in 2D space on an uneven terrain. We show that at extremely long times, these systems tends to behave according to the rules of ballistic diffusion. Physically, it means that their motion tends to be associated with the "channels" of the patterned substrate. Such a motion is more or less the same as it should be in the uniform space. Such asymptotic behavior is specific for the motion in model regular potential and would be impossible on more realistic (and complex) fractal reliefs. However, one can expect that in any kind of the potential with certain symmetry (hexagonal or rhombic, for example), where it is still possible to find the ways, the motion along fixed direction during long (or even almost infinite) time intervals is possible. [ABSTRACT FROM AUTHOR]

To achieve precise grasping and spreading of irregular sheet-like soft objects (such as leather) by robots, this study addresses several challenges, including the irregularity of leather edges and the ambiguity of feature recognition points. To tackle these issues, this paper proposes an innovative method that involves alternately grasping the lowest point twice and using planar techniques to effectively spread the leather. We improved the YOLOV8 algorithm by incorporating the BIFPN network structure and the WIOU loss function, and trained a dedicated dataset for the lowest grasping points and planar grasping points, thereby achieving high-precision recognition. Additionally, we determined the optimal posture for grasping the lowest point and constructed an experimental platform, successfully conducting multiple rounds of leather grasping and spreading experiments with a success rate of 72%. Through an in-depth analysis of the failed experiments, this study reveals the limitations of the current methods and provides valuable guidance for future research. [ABSTRACT FROM AUTHOR]

Improving the energy efficiency of robots remains a crucial challenge in soft robotics, with energy harvesting emerging as a promising approach to address it. This study presents a functional soft robotic composite called OPV-DEA, which integrates flexible organic photovoltaic (OPV) and dielectric elastomer actuator (DEA). The composite can simultaneously generate electrostatic bending actuation and harvest energy from external lights. Owing to its simplicity and inherent flexibility, the OPV-DEA is poised to function as a fundamental building block for soft robots. This study aimed to validate this concept by initially establishing the fabrication process of OPV-DEA. Subsequently, experimental samples are fabricated and characterized. The results show that the samples exhibit a voltage-controllable bending actuation of up to 15.6° and harvested power output of 1.35 mW under an incident power irradiance of 11.7 mW/cm2. These performances remain consistent even after 1000 actuation cycles. Finally, to demonstrate the feasibility of soft robotic applications, an untethered swimming robot equipped with two OPV-DEAs is fabricated and tested. The robot demonstrates swimming at a speed of 21.7 mm/s. The power consumption of the robot is dominated by a high-voltage DC-DC converter, with a value approximately 1.5 W. As a result, the on-board OPVs cannot supply the necessary energy during locomotion simultaneously. Instead, they contribute to the overall system by charging a battery used for the controller on board. Nevertheless, these findings suggest that the OPV-DEA could pave the way for the development of an unprecedented range of functional soft robots. [ABSTRACT FROM AUTHOR]

Medical imaging-based triage is a critical tool for emergency medicine in both civilian and military settings. Ultrasound imaging can be used to rapidly identify free fluid in abdominal and thoracic cavities which could necessitate immediate surgical intervention. However, proper ultrasound image capture requires a skilled ultrasonography technician who is likely unavailable at the point of injury where resources are limited. Instead, robotics and computer vision technology can simplify image acquisition. As a first step towards this larger goal, here, we focus on the development of prototypes for ultrasound probe securement using a robotics platform. The ability of four probe adapter technologies to precisely capture images at anatomical locations, repeatedly, and with different ultrasound transducer types were evaluated across more than five scoring criteria. Testing demonstrated two of the adapters outperformed the traditional robot gripper and manual image capture, with a compact, rotating design compatible with wireless imaging technology being most suitable for use at the point of injury. Next steps will integrate the robotic platform with computer vision and deep learning image interpretation models to automate image capture and diagnosis. This will lower the skill threshold needed for medical imaging-based triage, enabling this procedure to be available at or near the point of injury. [ABSTRACT FROM AUTHOR]

In the realm of robotics and automation, robot teleoperation, which facilitates human–machine interaction in distant or hazardous settings, has surged in significance. A persistent issue in this domain is the delays between command issuance and action execution, causing negative repercussions on operator situational awareness, performance, and cognitive load. These delays, particularly in long-distance operations, are difficult to mitigate even with the most advanced computing advancements. Current solutions mainly revolve around machine-based adjustments to combat these delays. However, a notable lacuna remains in harnessing human perceptions for an enhanced subjective teleoperation experience. This paper introduces a novel approach of sensory manipulation for induced human adaptation in delayed teleoperation. Drawing from motor learning and rehabilitation principles, it is posited that strategic sensory manipulation, via altered sensory stimuli, can mitigate the subjective feeling of these delays. The focus is not on introducing new skills or adapting to novel conditions; rather, it leverages prior motor coordination experience in the context of delays. The objective is to reduce the need for extensive training or sophisticated automation designs. A human-centered experiment involving 41 participants was conducted to examine the effects of modified haptic cues in teleoperations with delays. These cues were generated from high-fidelity physics engines using parameters from robot-end sensors or physics engine simulations. The results underscored several benefits, notably the considerable reduction in task time and enhanced user perceptions about visual delays. Real-time haptic feedback, or the anchoring method, emerged as a significant contributor to these benefits, showcasing reduced cognitive load, bolstered self-confidence, and minimized frustration. Beyond the prevalent methods of automation design and training, this research underscores induced human adaptation as a pivotal avenue in robot teleoperation. It seeks to enhance teleoperation efficacy through rapid human adaptation, offering insights beyond just optimizing robotic systems for delay compensations. [ABSTRACT FROM AUTHOR]

Labor shortages in the United States are impacting a number of industries including the meat processing sector. Collaborative technologies that work alongside humans while increasing production abilities may support the industry by enhancing automation and improving job quality. However, existing automation technologies used in the meat industry have limited collaboration potential, low flexibility, and high cost. The objective of this work was to explore the use of a robot arm to collaboratively work alongside a human and complete tasks performed in a meat processing facility. Toward this objective, we demonstrated proof-of-concept approaches to ensure human safety while exploring the capacity of the robot arm to perform example meat processing tasks. In support of human safety, we developed a knife instrumentation system to detect when the cutting implement comes into contact with meat within the collaborative space. To demonstrate the capability of the system to flexibly conduct a variety of basic meat processing tasks, we developed vision and control protocols to execute slicing, trimming, and cubing of pork loins. We also collected a subjective evaluation of the actions from experts within the U.S. meat processing industry. On average the experts rated the robot's performance as adequate. Moreover, the experts generally preferred the cuts performed in collaboration with a human worker to cuts completed autonomously, highlighting the benefits of robotic technologies that assist human workers rather than replace them. Video demonstrations of our proposed framework can be found here: https://youtu.be/56mdHjjYMVc. [ABSTRACT FROM AUTHOR]

This paper introduces a hybrid robotic swarm system architecture that combines virtual and physical components and enables human–swarm interaction through mixed reality (MR) devices. The system comprises three main modules: (1) the virtual module, which simulates robotic agents, (2) the physical module, consisting of real robotic agents, and (3) the user interface (UI) module. To facilitate communication between the modules, the UI module connects with the virtual module using Photon Network and with the physical module through the Robot Operating System (ROS) bridge. Additionally, the virtual and physical modules communicate via the ROS bridge. The virtual and physical agents form a hybrid swarm by integrating these three modules. The human–swarm interface based on MR technology enables one or multiple human users to interact with the swarm in various ways. Users can create and assign tasks, monitor real-time swarm status and activities, or control and interact with specific robotic agents. To validate the system-level integration and embedded swarm functions, two experimental demonstrations were conducted: (a) two users playing planner and observer roles, assigning five tasks for the swarm to allocate the tasks autonomously and execute them, and (b) a single user interacting with the hybrid swarm consisting of two physical agents and 170 virtual agents by creating and assigning a task list and then controlling one of the physical robots to complete a target identification mission. [ABSTRACT FROM AUTHOR]

Technical advances in microsurgery have enabled complex oncological reconstructions by performing free tissue transfers, nerve and lymphatic reconstructions. However, the manual abilities required to perform microsurgery can be affected by human fatigue and physiological tremor resulting in tissue damage and compromised outcomes. Robotic assistance has the potential to overcome issues of manual microsurgery by improving clinical value and anastomoses' outcomes. The Symani Surgical System, a robotic platform designed for microsurgery, was used in this in-vivo preclinical study using a rat animal model. The tests included anastomoses on veins and arteries performed by microsurgeons manually and robotically, with the latter approach using Symani. The anastomoses were assessed for patency, histopathology, and execution time. Patency results confirmed that the robotic and manual techniques for venous and arterial anastomoses were equivalent after anastomosis, however, the time to perform the anastomosis was longer with the use of the robot (p < 0.0001). Histological analysis showed less total average host reaction score at the anastomotic site in robotic anastomosis for both veins and arteries. This study demonstrates the equivalence of vessel patency after microsurgical anastomoses with the robotic system and with manual technique. Furthermore, robotic anastomosis has proven to be slightly superior to manual anastomosis in terms of decreased tissue damage, as shown by histological analysis. [ABSTRACT FROM AUTHOR]

Soft sensors that can discriminate shear and normal force could help provide machines the fine control desirable for safe and effective physical interactions with people. A capacitive sensor is made for this purpose, composed of patterned elastomer and containing both fixed and sliding pillars that allow the sensor to deform and buckle, much like skin itself. The sensor differentiates between simultaneously applied normal force and shear using summation and differences of signals from four deformable capacitors. Cross talk from shear to normal force is less than 2.5%, and between shear axes is less than 10%. Normal and shear stress sensitivity is 0.49 kPa and 0.31 kPa respectively, with a minimum displacement resolution of 40 μm. In addition, finger proximity is detectable at a range of up to 15 mm. The operation is demonstrated on a simple gripper holding a cup. The combination of features and the straightforward fabrication method make this sensor a candidate for implementation as a sensing skin for humanoid robotics applications. [ABSTRACT FROM AUTHOR]

There has been an increasing demand for robotic coil positioning during repetitive transcranial magnetic stimulation (rTMS) treatment. Accurate coil positioning is crucial because rTMS generally targets specific brain regions for both research and clinical application with other reasons such as safety, consistency and reliability and individual variablity. Some previous studies have employed industrial robots or co-robots and showed they can more precisely stimulate the target cortical regions than traditional manual methods. In this study, we not only developed a custom-TMS robot for better TMS coil placement but also analyzed the therapeutic effects on depression. Treatment effects were evaluated by measuring regional cerebral blood flow (rCBF) using single-photon emission computed tomography and depression severity before and after rTMS for the two positioning methods. The rTMS preparation time with our robotic coil placement was reduced by 53% compared with that of the manual method. The position and orientation errors were also significantly reduced from 11.17 mm and 4.06° to 0.94 mm and 0.11°, respectively, confirming the superiority of robotic positioning. The results from clinical and neuroimaging assessments indicated comparable improvements in depression severity and rCBF in the left dorsolateral prefrontal cortex between the robotic and manual rTMS groups. A questionnaire was used to determine the patients' feelings about the robotic system, including the safety and preparation time. A high safety score indicated good acceptability of robotic rTMS at the clinical site. [ABSTRACT FROM AUTHOR]

Cleaning is a fundamental routine task in human life that is now handed over to leading-edge technologies such as robotics and artificial intelligence. Various floor-cleaning robots have been developed with different cleaning functionalities, such as vacuuming and scrubbing. However, failures can occur when a robot tries to clean an incompatible dirt type. These situations will not only reduce the efficiency of the robot but also impose severe damage to the robots. Therefore, developing effective methods to classify the cleaning tasks performed in different regions and assign them to the respective cleaning agent has become a trending research domain. This article proposes a vision-based system that employs YOLOv5 and DeepSORT algorithms to detect and classify dirt to create a dirt distribution map that indicates the regions to be assigned for different cleaning requirements. This map would be useful for a collaborative cleaning framework for deploying each cleaning robot to its respective region to achieve an uninterrupted and energy-efficient operation. The proposed method can be executed with any mobile robot and on any surface and dirt, achieving high accuracy of 81.0%, for dirt indication in the dirt distribution map. [ABSTRACT FROM AUTHOR]

Earthworms have entirely soft bodies mainly composed of circular and longitudinal muscle bundles but can handle the complexity of unstructured environments with exceptional multifunctionality. Soft robots are naturally appropriate for mimicking soft animal structures thanks to their inherent compliance. Here, we explore the new possibility of using this compliance to coordinate the actuation movements of single-type soft actuators for not only high adaptability but the simultaneous multifunctionality of soft robots. A cross-linked actuator coordination mechanism is proposed and explained with a novel conceptual design of a cross-linked network, characterization of modular coordinated kinematics, and a modular control strategy for multiple functions. We model and analyze the motion patterns for these functions, including grabbing, manipulation, and locomotion. This further enables the combination of simultaneous multi-functions with this very simple actuator network structure. In this way, a soft modular robot is developed with demonstrations of a novel continuous-transportation mode, for which multiple objects could be simultaneously transported in unstructured environments with either mobile manipulation or pick-and-place operation. A comprehensive workflow is presented to elaborate the cross-linked actuator coordination concept, analytical modeling, modular control strategy, experimental validation, and multi-functional applications. Our understanding of actuator coordination inspires new soft robotic designs for wider robotic applications. [ABSTRACT FROM AUTHOR]

This study introduces and compares two optimization techniques, the basic Artificial Bee Colony (ABC) and the enhanced Artificial Bee Colony with multi-elite guidance (MGABC), for determining optimal gains of a Proportional-Integral-Derivative (PID) controller in a 3 degrees of freedom (DOF) rigid link manipulator (RLM) system. The objective function used in the optimization process is a novel function that is based on the well-known Lyapunov stability functions. This function is evaluated against established error-based objective functions commonly used in control systems. The convergence curves of the optimization process demonstrate that the MGABC algorithm outperforms the basic ABC algorithm by effectively exploring the search space and avoiding local optima. The evaluation of the controller's performance in trajectory tracking reveals the superiority of the Lyapunov-based objective function (LBF), with significant improvements over other objective functions such as IAE, ISE, ITAE, MAE and MRSE. The optimized system demonstrates robustness to diverse disturbance conditions and uncertainty in the mass of the payload, while also exhibiting adaptability to joints flexibility without inducing any vibrations in the movement of the end-effector. The proposed techniques and objective function offer promising avenues for the optimization of PID controllers in various robotic applications. [ABSTRACT FROM AUTHOR]

The insertion of conventional colonoscopes can sometimes cause patients to experience pain during the procedure owing to the stretching of the mesentery. In this study, a prototype of a robotic colonoscope with a double-balloon and double-bend tube based on the conventional double-balloon endoscope was developed to simplify insertion and prevent the overstretching of the colon. Both the outer and inner tubes were confirmed to be free from interference from wires and sheaths. Additionally, all functions such as tip bending, inflation and deflation of the balloons, and actuator-driven pulling and pushing of the inner tube were operated properly. During the insertion test, the device could be reached the cecum of a colon model in approximately 442 s when operated by a non-medical operator. In addition, the device did not overstretch the colon model, thereby suggesting that the insertion mechanism can follow the shape of the colon model. As a result, the developed mechanism has the potential to navigate through a highly-bent colon without overstretching. [ABSTRACT FROM AUTHOR]

Metachronal propulsion is widespread in aquatic swarming organisms to achieve performance and maneuverability at intermediate Reynolds numbers. Studying only live organisms limits our understanding of the mechanisms driving these abilities. Thus, we present the design, manufacture, and validation of the Pleobot—a unique krill-inspired robotic swimming appendage constituting the first platform to study metachronal propulsion comprehensively. We combine a multi-link 3D printed mechanism with active and passive actuation of the joints to generate natural kinematics. Using force and fluid flow measurements in parallel with biological data, we show the link between the flow around the appendage and thrust. Further, we provide the first account of a leading-edge suction effect contributing to lift during the power stroke. The repeatability and modularity of the Pleobot enable the independent manipulation of particular motions and traits to test hypotheses central to understanding the relationship between form and function. Lastly, we outline future directions for the Pleobot, including adapting morphological features. We foresee a broad appeal to a wide array of scientific disciplines, from fundamental studies in ecology, biology, and engineering, to developing new bio-inspired platforms for studying oceans across the solar system. [ABSTRACT FROM AUTHOR]

Minimally invasive surgery is usually more beneficial than open surgeries in various fields of surgery. With the newly developed Single-Port (SP) robotic surgical system, even single-site surgery has become easier to access. We compared single-incision robotic cholecystectomy between the Si/Xi and SP systems. This retrospective single-center study enrolled patients who underwent single-incision robotic cholecystectomy between July 2014 and July 2021. The clinical outcomes of the da Vinci Si/Xi and SP systems were compared. In total, 334 patients underwent single-incision robotic cholecystectomy (118 Si/Xi vs. 216 SP). The SP group had more chronic or acute cholecystitis than the Si/Xi group did. There was more bile spillage in the Si/Xi group during the surgery. The total operative and docking times were significantly shorter in the SP group. There was no difference in the postoperative outcomes. The SP system is safe and feasible regarding comparable postoperative complication rates and is more convenient regarding docking and techniques. [ABSTRACT FROM AUTHOR]

Hand gesture recognition (HGR) based on electromyography signals (EMGs) and inertial measurement unit signals (IMUs) has been investigated for human-machine applications in the last few years. The information obtained from the HGR systems has the potential to be helpful to control machines such as video games, vehicles, and even robots. Therefore, the key idea of the HGR system is to identify the moment in which a hand gesture was performed and it's class. Several human-machine state-of-the-art approaches use supervised machine learning (ML) techniques for the HGR system. However, the use of reinforcement learning (RL) approaches to build HGR systems for human-machine interfaces is still an open problem. This work presents a reinforcement learning (RL) approach to classify EMG-IMU signals obtained using a Myo Armband sensor. For this, we create an agent based on the Deep Q-learning algorithm (DQN) to learn a policy from online experiences to classify EMG-IMU signals. The HGR proposed system accuracy reaches up to 97.45 ± 1.02 % and 88.05 ± 3.10 % for classification and recognition respectively, with an average inference time per window observation of 20 ms. and we also demonstrate that our method outperforms other approaches in the literature. Then, we test the HGR system to control two different robotic platforms. The first is a three-degrees-of-freedom (DOF) tandem helicopter test bench, and the second is a virtual six-degree-of-freedom (DOF) UR5 robot. We employ the designed hand gesture recognition (HGR) system and the inertial measurement unit (IMU) integrated into the Myo sensor to command and control the motion of both platforms. The movement of the helicopter test bench and the UR5 robot is controlled under a PID controller scheme. Experimental results show the effectiveness of using the proposed HGR system based on DQN for controlling both platforms with a fast and accurate response. [ABSTRACT FROM AUTHOR]

Existing approaches to robotic manipulation often rely on external mechanical devices, such as hydraulic and pneumatic devices or grippers. Both types of devices can be adapted to microrobots only with difficulties and for nanorobots not all. Here, we present a fundamentally different approach that is based on tuning the acting surface forces themselves rather than applying external forces by grippers. Tuning of forces is achieved by the electrochemical control of an electrode's diffuse layer. Such electrochemical grippers can be integrated directly into an atomic force microscope, allowing for 'pick and place' procedures typically used in macroscopic robotics. Due to the low potentials involved, small autonomous robots could as well be equipped with these electrochemical grippers that will be particularly useful in soft robotics as well as nanorobotics. Moreover, these grippers have no moving parts and can be incorporated in new concepts for actuators. The concept can easily be scaled down and applied to a wide range of objects, such as colloids, proteins, and macromolecules. [ABSTRACT FROM AUTHOR]

With the ever-pressing challenges of societal ageing, robotic technologies for older people are increasingly portrayed as a solution for better independent living for longer. However, the application of human-following robots for elderly citizens has not yet been considered, and any prospective benefits offered by the technology for active ageing have previously been overlooked. This qualitative research aimed to explore older people's needs and requirements towards the human-following robot through the reflexive thematic analysis of semi-structured interview data from 17 independent older adults, supported by a video-based demonstration of the robot. The results indicate that older people believed that human-following robot has the potential to provide social benefits to an independent older adult by encouraging walking trips and prompting social interaction with others in the community. Practical limitations and cost of the robot are barriers to adoption at present. The findings indicate that there is potential for human-following robots to support active ageing, through increasing opportunities for the social participation of an older adult, but further development of the robot is needed for this potential to be realised. [ABSTRACT FROM AUTHOR]

Laparoscopic (LPD) and robotic pancreaticoduodenectomy (RPD) are both challenging procedures. The feasibility and safety of simultaneously developing LPD and RPD remain unreported. We retrospectively reviewed the data of patients undergoing LPD or RPD between 2014 and 2021. A total of 114 patients underwent minimally invasive pancreaticoduodenectomy (MIPD): 39 LPDs and 75 RPDs. The learning process of LPD and RPD were similar. The cutoff points of the learning curve were LPD, 13th patient (the 27th patient of MIPD), and RPD, 18th patient (the 31st patient of MIPD) according the cumulative sum analysis of operative time. A decrease in the operative time was associated with the case sequence (p < 0.001) but not with the surgical approach (p = 0.36). The overall surgical outcomes were comparable between both the LPD and RPD groups. When evaluating the learning curve impact on MIPD, LPD had higher major complication (≧ Clavien–Dindo grade III), bile leak and wound infection rates in the pre-learning curve phase than those in the after-learning curve phase, while RPD had similar surgical outcomes between two phases. Simultaneous development of LPD and RPD is feasible and safe for experienced surgeons, with similar learning process and comparable surgical outcomes. [ABSTRACT FROM AUTHOR]

Author Correction: Quantifying changes over 1 year in motor and cognitive skill after transient ischemic attack (TIA) using robotics Graph: Figure 4 Line plots of task scores over time in the TIA cohort at all 4 assessments with significant changes corrected for the false discovery rate (FDR). Graph: Figure 2 Scatterplots of one-sided Task Scores at first- and second assessments in TIA (a-c) and migraine (d-f) cohorts. [Extracted from the article]

Assembly is a critical step in the manufacturing process. Robotic assembly technology in automatic production lines has greatly improved the production efficiency. However, in assembly process, dynamic disturbances such as processing time change and advance delivery may occur, which cause the scheduling deviation. Traditional scheduling methods are not sufficient to meet the real-time and adaptive requirements in smart manufacturing. Digital twin (DT) has the characteristics of virtual-reality interaction and real-time mapping. In this paper, we propose a DT-based framework of task rescheduling for robotic assembly line (RAL) and its key methodologies, thus to realize the timely and dynamic adjustment of scheduling plan under uncertain interferences. First, a DT model of RAL task rescheduling composed of physical entity (PE), virtual entity (VE), and virtual-reality interaction mechanism is proposed. Then, a mathematical model is established. By analyzing the adaptive objective thresholds from the perspectives of event trigger and user demand trigger, a DT-driven multi-level (production unit level and line level) rescheduling strategy is proposed. Taking both the computing time and solution quality into consideration, the precedence graph is introduced to propose a rescheduling approach based on an improved discrete fireworks algorithm. Finally, the effectiveness of the proposed model and approach are verified by task scheduling experiments of RAL. [ABSTRACT FROM AUTHOR]

Three-dimensional exoscopes have been designed to overcome certain insufficiencies of operative microscopes. We aimed to explore the clinical use in various spinal surgeries. We performed surgery on patients with different spine entities in a neurosurgical department according to the current standard operating procedures over a 4-week period of time. The microsurgical part has been performed with Aesculap AEOS 3D microscope. Three neurosurgeons with different degree of surgical expertise completed a questionnaire with 43 items based on intraoperative handling and feasibility after the procedures. We collected and analyzed data from seventeen patients (35% male/65% female) with a median age of 70 years [CI 47–86] and median BMI of 25.8 kg/m2 [range 21–33]. We included a variety of spinal pathologies (10 degenerative, 4 tumor and 3 infectious cases) with different level of complexity. Regarding setup conflicts we observed issues with adjustment of the monitor position or while using additional equipment (e.g. fluoroscopy in fusion surgery) (p = 0.007/p = 0.001). However image resolution and sharpness as well as 3D-depth perception were completely satisfactory for all surgeons in all procedures. The utilization of the exoscopic arm was easy for 76.5% of the surgeons, and all of them declared a significant improvement of the surgical corridor. The 3D-exoscope implementation appears to achieve very satisfactory results in spinal procedures especially with minimally invasive approaches. [ABSTRACT FROM AUTHOR]

Ingestible robotic capsules with locomotion capabilities and on-board sampling mechanism have great potential for non-invasive diagnostic and interventional use in the gastrointestinal tract. Real-time tracking of capsule location and operational state is necessary for clinical application, yet remains a significant challenge. To this end, we propose an approach that can simultaneously determine the mechanism state and in-plane 2D pose of millimeter capsule robots in an anatomically representative environment using ultrasound imaging. Our work proposes an attention-based hierarchical deep learning approach and adapts the success of transfer learning towards solving the multi-task tracking problem with limited dataset. To train the neural networks, we generate a representative dataset of a robotic capsule within ex-vivo porcine stomachs. Experimental results show that the accuracy of capsule state classification is 97%, and the mean estimation errors for orientation and centroid position are 2.0 degrees and 0.24 mm (1.7% of the capsule's body length) on the hold-out test set. Accurate detection of the capsule while manipulated by an external magnet in a porcine stomach and colon is also demonstrated. The results suggest our proposed method has the potential for advancing the wireless capsule-based technologies by providing accurate detection of capsule robots in clinical scenarios. [ABSTRACT FROM AUTHOR]

This study analyzed learning curve and the surgical outcome of robotic assisted colorectal surgery with ERAS program. The study results serve as a reference for future robotic colorectal surgeon who applied ERAS in clinical practice. This was a retrospective case–control study to analyze the learning curve of 141 robotic assisted colorectal surgery (RAS) by Da Vinci Xi (Xi) system and compare the surgical outcomes with 147 conventional laparoscopic (LSC) surgery in the same team. Evaluation for maturation was performed by operation time and the CUSUM plot. Patients were recruited from 1st February 2019 to 9th January 2022; follow-up was conducted at 30 days, and the final follow-up was conducted on 9th February 2022. It both took 31 cases for colon and rectal robotic surgeries to reach the maturation phase. Teamwork maturation was achieved after 60 cases. In the maturation stage, RAS required a longer operation time (mean: colon: 249.5 ± 46.5 vs. 190.3 ± 57.3 p < 0.001; rectum 314.9 ± 59.6 vs. 223.6 ± 63.5 p < 0.001). After propensity score matching, robotic surgery with ERAS program resulted in significant shorter length of hospital stay (mean: colon: 5.5 ± 4.5 vs. 10.0 ± 11.9, p < 0.001; rectum: 5.4 ± 3.5 vs. 10.1 ± 7.0, p < 0.001), lower minor complication rate (colon: 6.0% vs 20.0%, p = 0.074 ; rectum: 11.1% vs 33.3%, p = 0.102), and no significant different major complication rate (colon: 2.0% vs 6.0%, p = 0.617; rectum: 7.4% cs 7.4%, p = 1.0) to conventional LSC. Learning curve for robotic assisted colorectal surgery takes 31 cases. Robotic surgery with ERAS program brings significant faster recovery and fewer complication rate compared to laparoscopy in colorectal surgery. [ABSTRACT FROM AUTHOR]

Stair climbing is one of the most important capabilities of mobile robots. Therefore, stair-climbing mobile robots have become a field of study and diverse stair-climbing mobile robots have been developed. Although tri-wheel-based stair-climbing robotic platforms were developed to overcome the challenges posed by stair climbing, they have shown limitations such as impact during locomotion and damage owing to friction with the nosing of the stairs. In this study, several tail mechanisms were proposed and designed to solve the limitations of tri-wheel-based stair-climbing robots. A comparative analysis of the tail mechanisms was performed through dynamic simulations based on various performance indices. It was observed that the tail mechanism improved the stability and stair-climbing performance of the tri-wheel-based stair-climbing robots. The experimental verification confirmed the reliability of the comparative analysis results based on the simulation. These findings can be used to design mobile stair-climbing robots. [ABSTRACT FROM AUTHOR]

Laboratory procedures are often considered so unique that automating them is not economically justified – time and resources invested in designing, building and calibrating the machines are unlikely to pay off. This is particularly true if cheap labour force (technicians or students) is available. Yet, with increasing availability and dropping prices of many off-the-shelf components such as motorised stages, grippers, light sources (LEDs and lasers), detectors (high resolution, fast cameras), as well as user-friendly programmable microprocessors, many of the repeatable tasks may soon be within reach of either custom-built or universal lab robots. Building on our previous work on fabrication, characterization and applications of light-responsive liquid crystal elastomers (LCEs) in micro-robotics and micro-mechanics, in this paper we present a robotic workstation that can make LCE films with arbitrary molecular orientation. Based on a commercial 3D printer, the RoboLEC (Robot for LCE fabrication) performs precision component handling, structured light illumination, liquid dispensing and UV-triggered polymerization, within a four-hour-long procedure. Thus fabricated films with patterned molecular orientation are compared to the same, but handmade, structures. [ABSTRACT FROM AUTHOR]

Cone-beam computed tomography (CBCT) has been shown to be a powerful tool for 3D imaging of the appendicular skeleton, allowing for detailed visualization of bone microarchitecture. This study was designed to compare artifacts in the presence of osteosynthetic implants between CBCT and multidetector computed tomography (MDCT) in cadaveric wrist scans. A total of 32 scan protocols with varying tube potential and current were employed: both conventional CBCT and MDCT studies were included with tube voltage ranging from 60 to 140 kVp as well as additional MDCT protocols with dedicated spectral shaping via tin prefiltration. Irrespective of scanner type, all examinations were conducted in ultra-high-resolution (UHR) scan mode. For reconstruction of UHR-CBCT scans an additional iterative metal artifact reduction algorithm was employed, an image correction tool which cannot be used in combination with UHR-MDCT. To compare applied radiation doses between both scanners, the volume computed tomography dose index for a 16 cm phantom (CTDIvol) was evaluated. Images were assessed regarding subjective and objective image quality. Without automatic tube current modulation or tube potential control, radiation doses ranged between 1.3 mGy (with 70 kVp and 50.0 effective mAs) and 75.2 mGy (with 140 kVp and 383.0 effective mAs) in UHR-MDCT. Using the pulsed image acquisition method of the CBCT scanner, CTDIvol ranged between 2.3 mGy (with 60 kVp and 0.6 mean mAs per pulse) and 61.0 mGy (with 133 kVp and 2.5 mean mAs per pulse). In essence, all UHR-CBCT protocols employing a tube potential of 80 kVp or more were found to provide superior overall image quality and artifact reduction compared to UHR-MDCT (all p <.050). Interrater reliability of seven radiologists regarding image quality was substantial for tissue assessment and moderate for artifact assessment with Fleiss kappa of 0.652 (95% confidence interval 0.618–0.686; p < 0.001) and 0.570 (95% confidence interval 0.535–0.606; p < 0.001), respectively. Our results demonstrate that the UHR-CBCT scan mode of a twin robotic X-ray system facilitates excellent visualization of the appendicular skeleton in the presence of metal implants. Achievable image quality and artifact reduction are superior to dose-comparable UHR-MDCT and even MDCT protocols employing spectral shaping with tin prefiltration do not achieve the same level of artifact reduction in adjacent soft tissue. [ABSTRACT FROM AUTHOR]

All CRISPR/CAS systems utilize CRISPR guide RNAs (crRNAs), the design of which depend on the type of CAS protein, genetic target and the environment/matrix. While machine learning approaches have recently been developed to optimize some crRNA designs, candidate crRNAs must still be screened for efficacy under relevant conditions. Here, we demonstrate a high-throughput method to screen hundreds of candidate crRNAs for activation of Cas13a collateral RNA cleavage. Entire regions of a model gene transcript (Y. pestis lcrV gene) were tiled to produce overlapping crRNA sets. We tested for possible effects that included crRNA/target sequence, size and secondary structures, and the commercial source of DNA oligomers used to generate crRNAs. Detection of a 981 nt target RNA was initially successful with 271 out of 296 tested guide RNAs, and that was improved to 287 out of 296 (97%) after protocol optimizations. For this specific example, we determined that crRNA efficacy did not strongly depend on the target region or crRNA physical properties, but was dependent on the source of DNA oligomers used for RNA preparation. Our high-throughput methods for screening crRNAs has general applicability to the optimization of Cas12 and Cas13 guide RNA designs. [ABSTRACT FROM AUTHOR]

This paper addresses coordinated problem of uncertain robotic manipulators with matrix-weighted network. Given interconnections between agents are weighted by nonnegative definite matrices, we present a sufficient and necessary condition about zero eigenvalues of matrix-weighted Laplacian and types of coordinated behaviors for multiple agents. Based on the condition, two novel control schemes are proposed for the networked robots by introducing matrix-weighted network. We employ the decomposition approach and Lyapunov-like approach to show coordinated motions of the networked system, and demonstrate that the proposed controls are capable of ensuring the robotic agents reach complete/cluster consensus and complete/cluster synchronization. Finally, some numerical examples and simulations demonstrate the obtained theoretical results. [ABSTRACT FROM AUTHOR]

Locoregional recurrent thyroid cancer is commonly treated with re-do operation. This study aimed to investigate the feasibility of using robotic system for re-do operation in locoregional recurrent thyroid cancer. Sixty-five patients who underwent re-do robotic operation using trans-axillary approach for locoregional recurrent thyroid cancer from October 2007 to April 2021 at Yonsei University Hospital were analyzed. Completion total thyroidectomy (CTT) was performed in 26 cases, CTT and modified radical neck node dissection (mRND) in 16, and mRND in 23. Most of the re-do robotic operations were performed at site of previous incision. All patients were diagnosed with papillary thyroid carcinoma (PTC). CTT with central compartment neck dissection (CCND) took 117.6 ± 26.3 min, CTT with mRND 255.6 ± 38.6 min, and mRND, 211.7 ± 52.9 min. Transient hypocalcemia occurred in 17 (26.2%) patients and permanent hypocalcemia occurred in 3 (4.6%). There was one case of recurrent laryngeal nerve(RLN) injury. One patient was diagnosed with structural recurrence after re-do robotic operation. Median follow-up duration was 50.7 ± 37.1 months. Re-do robotic operation can be an alternative for patients who are diagnosed with locoregional recurrent thyroid cancer after thyroidectomy, with no increase in morbidity, similar oncologic outcomes, and superior cosmetic satisfaction. [ABSTRACT FROM AUTHOR]

The supernumerary robotic limb system expands the motor function of human users by adding extra artificially designed limbs. It is important for us to embody the system as if it is a part of one's own body and to maintain cognitive transparency in which the cognitive load is suppressed. Embodiment studies have been conducted with an expansion of bodily functions through a "substitution" and "extension". However, there have been few studies on the "addition" of supernumerary body parts. In this study, we developed a supernumerary robotic limb system that operates in a virtual environment, and then evaluated whether the extra limb can be regarded as a part of one's own body using a questionnaire and whether the perception of peripersonal space changes with a visuotactile crossmodal congruency task. We found that the participants can embody the extra-limbs after using the supernumerary robotic limb system. We also found a positive correlation between the perceptual change in the crossmodal congruency task and the subjective feeling that the number of one's arms had increased (supernumerary limb sensation). These results suggest that the addition of an extra body part may cause the participants to feel that they had acquired a new body part that differs from their original body part through a functional expansion. [ABSTRACT FROM AUTHOR]

The robotic manipulation of a heavy industrial cable is challenging to model and control because of the high number of degrees of freedom and the rigid-flexible coupling dynamics. In this paper, we report the development of modeling the cable effect and control methodology for robotic cable manipulation. Our cable effect model is based on the 2D convolutional neural network, which is a deep learning-based method uses the effective cable representation method to achieve the accurate, generalizable, and efficient estimation of the cable coupling forces and torques. Practical problems such as the measurement limits and time efficiency are considered in our method for real applications. With these approaches, we are the first to solve the problem of dynamic payload effect caused by heavy industrial cables in experimental cases. The used control methodology combines the active disturbance rejection control framework with the sliding mode control method, which can acquire promising tracking performance. We integrate our cable effect model into the control scheme, and demonstrate it satisfies the high-quality robotic manipulation of heavy cables. The performance of the proposed method is assessed with both a simulated system and real robot system. The results show that our method can estimate the cable coupling effect with over 85% accuracy and accomplish manipulation with a positioning error less than 0.01 mm. This reveals that our method is promising for robotic manipulation of heavy industrial cables and can accomplish the challenging cable insertion task. [ABSTRACT FROM AUTHOR]

The original article can be found online at https://doi.org/10.1038/s41598-023-41143-z.Correction to: Scientific Reportshttps://doi.org/10.1038/s41598-023-41143-z, published online 27 August 2023The original version of this Article contained an error in Affiliation 2, which was incorrectly given as 'IV Clinica Ortoplastica, IRCCS Istituto Ortopedico Rizzoli, Università Di Bologna, Bologna, Italy.'The correct affiliation is listed below:IV Clinica Ortoplastica, IRCCS Istituto Ortopedico Rizzoli, Bologna, Italy.By Gerardo Malzone; Giulio Menichini; Marco Innocenti and Alberto BallestínReported by Author; Author; Author; Author [Extracted from the article]

Service robotics is a fast-developing sector, requiring embedded intelligence into robotic platforms to interact with the humans and the surrounding environment. One of the main challenges in the field is robust and versatile manipulation in everyday life activities. An appealing opportunity is to exploit compliant end-effectors to address the manipulation of deformable objects. However, the intrinsic compliance of such grippers results in increased difficulties in grasping control. Within the described context, this work addresses the problem of optimizing the grasping of deformable objects making use of a compliant, under-actuated, sensorless robotic hand. The main aim of the paper is, therefore, finding the best position and joint configuration for the mentioned robotic hand to grasp an unforeseen deformable object based on collected RGB image and partial point cloud. Due to the complex grasping dynamics, learning-from-simulations approaches (e.g., Reinforcement Learning) are not effective in the faced context. Thus, trial-and-error-based methodologies have to be exploited. In order to save resources, a samples-efficient approach has to be employed. Indeed, a Bayesian approach to address the optimization of the grasping strategy is proposed, enhancing it with transfer learning capabilities to exploit the acquired knowledge to grasp (partially) new objects. A PAL Robotics TIAGo (a mobile manipulator with a 7-degrees-of-freedom arm and an anthropomorphic underactuated compliant hand) has been used as a test platform, executing a pouring task while manipulating plastic (i.e., deformable) bottles. The sampling efficiency of the data-driven learning is shown, compared to an evenly spaced grid sampling of the input space. In addition, the generalization capability of the optimized model is tested (exploiting transfer learning) on a set of plastic bottles and other liquid containers, achieving a success rate of the 88%. [ABSTRACT FROM AUTHOR]

Target-driven visual navigation is essential for many applications in robotics, and it has gained increasing interest in recent years. In this work, inspired by animal cognitive mechanisms, we propose a novel navigation architecture that simultaneously learns exploration policy and encodes environmental structure. First, to learn exploration policy directly from raw visual input, we use deep reinforcement learning as the basic framework and allow agents to create rewards for themselves as learning signals. In our approach, the reward for the current observation is driven by curiosity and calculated by a count-based approach and temporal distance. While agents learn exploration policy, we use temporal distance to find waypoints in observation sequences and incrementally describe the structure of the environment in a way that integrates episodic memory. Finally, space topological cognition is integrated into the model as a path planning module and combined with a locomotion network to obtain a more generalized approach to navigation. We test our approach in the DMlab, a visually rich 3D environment, and validate its exploration efficiency and navigation performance through extensive experiments. The experimental results show that our approach can explore and encode the environment more efficiently and has better capability in dealing with stochastic objects. In navigation tasks, agents can use space topological cognition to effectively reach the target and guide detour behaviour when a path is unavailable, exhibiting good environmental adaptability. [ABSTRACT FROM AUTHOR]

Delayed cerebral ischemia (DCI) secondary to vasospasm is a determinate of outcomes following non-traumatic subarachnoid hemorrhage (SAH). SAH patients are monitored using transcranial doppler (TCD) to measure cerebral blood flow velocities (CBFv). However, the accuracy and precision of manually acquired TCD can be operator dependent. The NovaGuide robotic TCD system attempts to standardize acquisition. This investigation evaluated the safety and efficacy of the NovaGuide system in SAH patients in a Neuro ICU. We retrospectively identified 48 NovaGuide scans conducted on SAH patients. Mean and maximum middle cerebral artery (MCA) CBFv were obtained from the NovaGuide and the level of agreement between CBFv and computed tomography angiography (CTA) for vasospasm was determined. Safety of NovaGuide acquisition of CBFv was evaluated based on number of complications with central venous lines (CVL) and external ventricular drains (EVD). There was significant agreement between the NovaGuide and CTA (Cohen's Kappa = 0.74) when maximum MCA CBFv ≥ 120 cm/s was the threshold for vasospasm. 27/48 scans were carried out with CVLs and EVDs present without negative outcomes. The lack of adverse events associated with EVDs/CVLs and the strong congruence between maximal MCA CBFv and CTA illustrates the diagnostic utility of the NovaGuide. [ABSTRACT FROM AUTHOR]

Autonomous robotic search problems deal with different levels of uncertainty. When uncertainty is low, deterministic strategies employing available knowledge result in most effective searches. However, there are domains where uncertainty is always high since information about robot location, environment boundaries or precise reference points is unattainable, e.g., in cave, deep ocean, planetary exploration, or upon sensor or communications impairment. Furthermore, latency regarding when search targets move, appear or disappear add to uncertainty sources. Here we study intrinsic and environmental factors that affect low-informed robotic search based on diffusive Brownian, naive ballistic, and superdiffusive strategies (Lévy walks), and in particular, the effectiveness of their random exploration. Representative strategies were evaluated considering both intrinsic (motion drift, energy or memory limitations) and extrinsic factors (obstacles and search boundaries). Our results point towards minimum-knowledge based modulation approaches that can adjust distinct spatial and temporal aspects of random exploration to lead to effective autonomous search under uncertainty. [ABSTRACT FROM AUTHOR]

This research evaluates the effect on herbal crops of mechanical stress induced by two specially developed robotic platforms. The changes in plant morphology, metabolite profiles, and element content are evaluated in a series of three empirical experiments, conducted in greenhouse and CNC growing bed conditions, for the case of basil plant growth. Results show significant changes in morphological features, including shortening of overall stem length by up to 40% and inter-node distances by up to 80%, for plants treated with a robotic mechanical stress-induction protocol, compared to control groups. Treated plants showed a significant increase in element absorption, by 20–250% compared to controls, and changes in the metabolite profiles suggested an improvement in plants' nutritional profiles. These results suggest that repetitive, robotic, mechanical stimuli could be potentially beneficial for plants' nutritional and taste properties, and could be performed with no human intervention (and therefore labor cost). The changes in morphological aspects of the plant could potentially replace practices involving chemical treatment of the plants, leading to more sustainable crop production. [ABSTRACT FROM AUTHOR]

Using a simple neuroscience-inspired procedure to beam human subjects into robots, we previously demonstrated by visuo-motor manipulations that embodiment into a robot can enhance the acceptability and closeness felt towards the robot. In that study, the feelings of likeability and closeness toward the robot were significantly related to the sense of agency, independently of the sensations of enfacement and location. Here, using the same paradigm we investigated the effect of a purely sensory manipulation on the sense of robotic embodiment associated to social cognition. Wearing a head-mounted display, participants saw the visual scene captured from the robot eyes. By positioning a mirror in front of the robot, subjects saw themselves as a robot. Tactile stimulation was provided by stroking synchronously or not with a paintbrush the same location of the subject and robot faces. In contrast to the previous motor induction of embodiment which particularly affected agency, tactile induction yields more generalized effects on the perception of ownership, location and agency. Interestingly, the links between positive social feelings towards the robot and the strength of the embodiment sensations were not observed. We conclude that the embodiment into a robot is not sufficient in itself to induce changes in social cognition. [ABSTRACT FROM AUTHOR]

Contemporary robotics gives us mechatronic capabilities for augmenting human bodies with extra limbs. However, how our motor control capabilities pose limits on such augmentation is an open question. We developed a Supernumerary Robotic 3rd Thumbs (SR3T) with two degrees-of-freedom controlled by the user's body to endow them with an extra contralateral thumb on the hand. We demonstrate that a pianist can learn to play the piano with 11 fingers within an hour. We then evaluate 6 naïve and 6 experienced piano players in their prior motor coordination and their capability in piano playing with the robotic augmentation. We show that individuals' augmented performance with the SR3T could be explained by our new custom motor coordination assessment, the Human Augmentation Motor Coordination Assessment (HAMCA) performed pre-augmentation. Our work demonstrates how supernumerary robotics can augment humans in skilled tasks and that individual differences in their augmentation capability are explainable by their individual motor coordination abilities. [ABSTRACT FROM AUTHOR]

Robotic nipple-sparing mastectomy (RNSM) provides better cosmetic outcomes and improves the quality of life of women with breast cancer. However, this has not been widely adapted due to the lack of well-structured training programs. The present study aimed to report the establishment of cadaveric and animal skill laboratory training programs for RNSM and the participants' perception on the training programs. We performed 24 RNSMs using 11 cadavers and one porcine model. Then, the skill laboratory characteristics were reviewed. Five trainers and 10 trainees participated in the programs. The first four cadaveric RNSMs with latissimus dorsi flaps and implants were performed using the da Vinci Si® system. We performed 14 and six RNSMs using the Xi® and SP® systems, respectively. The scores for questionnaires on the satisfaction with the training consisted of the trainees' perceived goals in attending the course, teaching/learning environment, and teaching staff performance. The scores were excellent. Cadaveric or porcine RNSM skill laboratory training may be essential programs that can provide safe and efficient training. [ABSTRACT FROM AUTHOR]

MRI-targeted prostate biopsy improves detection of clinically significant prostate cancer (PCa). However, up to 70% of PCa lesions display intralesional tumor heterogeneity. Current target sampling strategies do not yet adequately account for this finding. This prospective study included 118 patients who underwent transperineal robotic assisted biopsy of the prostate. We identified a total of 58 PCa-positive PI-RADS lesions. We compared diagnostic accuracy of a target-saturation biopsy strategy to accuracy of single, two, or three randomly selected targeted biopsy cores and analysed potential clinical implications. Intralesional detection of clinically significant cancer (ISUP ≥ 2) was 78.3% for target-saturation biopsy and 39.1%, 52.2%, and 67.4% for one, two, and three targeted cores, respectively. Target-saturation biopsies led to a more accurate characterization of PCa in terms of Gleason score and reduced rates of significant cancer missed. Compared to one, two, and three targeted biopsy cores, target-saturation biopsies led to intensified staging procedures in 21.7%, 10.9, and 8.7% of patients, and ultimately to a potential change in therapy in 39.1%, 26.1%, and 10.9% of patients. This work presents the concept of robotic-assisted target saturation biopsy. This technique has the potential to improve diagnostic accuracy and thus individual staging procedures and treatment decisions. [ABSTRACT FROM AUTHOR]

Low-invasive soft robotic techniques can potentially be used for developing next-generation body–machine interfaces. Most soft robots require complicated fabrication processes involving 3D printing and bonding/assembling. In this letter, we describe a monolithic soft microrobot fabrication process for the mass production of soft film robots with a complex structure by simple 2D processing of a robotic actuator film. The 45 µg/mm2 lightweight film robot can be driven at a voltage of CMOS compatible 5 V with 0.15 mm−1 large curvature changes; it can generate a force 5.7 times greater than its self-weight. In a durability test, actuation could be carried out over 8000 times without degradation. To further demonstrate this technique, three types of film robots with multiple degrees of freedom and a moving illuminator robot were fabricated. This technique can easily integrate various electrical circuits developed in the past to robotic systems and can be used for developing advanced wearable sensing devices; it can be called "Kinetic electronics". [ABSTRACT FROM AUTHOR]

It is likely that when using an artificially augmented hand with six fingers, the natural five plus a robotic one, corticospinal motor synergies controlling grasping actions might be different. However, no direct neurophysiological evidence for this reasonable assumption is available yet. We used transcranial magnetic stimulation of the primary motor cortex to directly address this issue during motor imagery of objects' grasping actions performed with or without the Soft Sixth Finger (SSF). The SSF is a wearable robotic additional thumb patented for helping patients with hand paresis and inherent loss of thumb opposition abilities. To this aim, we capitalized from the solid notion that neural circuits and mechanisms underlying motor imagery overlap those of physiological voluntary actions. After a few minutes of training, healthy humans wearing the SSF rapidly reshaped the pattern of corticospinal outputs towards forearm and hand muscles governing imagined grasping actions of different objects, suggesting the possibility that the extra finger might rapidly be encoded into the user's body schema, which is integral part of the frontal-parietal grasping network. Such neural signatures might explain how the motor system of human beings is open to very quickly welcoming emerging augmentative bioartificial corticospinal grasping strategies. Such an ability might represent the functional substrate of a final common pathway the brain might count on towards new interactions with the surrounding objects within the peripersonal space. Findings provide a neurophysiological framework for implementing augmentative robotic tools in humans and for the exploitation of the SSF in conceptually new rehabilitation settings. [ABSTRACT FROM AUTHOR]

Synchronization of human networks is fundamental in many aspects of human endeavour. Recently, much research effort has been spent on analyzing how motor coordination emerges in human groups (from rocking chairs to violin players) and how it is affected by coupling structure and strength. Here we uncover the spontaneous emergence of leadership (based on physical signaling during group interaction) as a crucial factor steering the occurrence of synchronization in complex human networks where individuals perform a joint motor task. In two experiments engaging participants in an arm movement synchronization task, in the physical world as well as in the digital world, we found that specific patterns of leadership emerged and increased synchronization performance. Precisely, three patterns were found, involving a subtle interaction between phase of the motion and amount of influence. Such patterns were independent of the presence or absence of physical interaction, and persisted across manipulated spatial configurations. Our results shed light on the mechanisms that drive coordination and leadership in human groups, and are consequential for the design of interactions with artificial agents, avatars or robots, where social roles can be determinant for a successful interaction. [ABSTRACT FROM AUTHOR]

Architected modular origami structures show potential for future robotic matter owing to their reconfigurability with multiple mobilities. Similar to modular robots, the units of modular origami structures do not need to be assembled in a fully packed fashion; in fact, disconnection can provide more freedom for the design of mobility and functionality. Despite the potential of expanded design freedom, the effect of the disconnection of units on the mobility and physical properties has not yet been explored in modular origami structures. Determining the mobility and weak spots of modular origami structures is significant to enable transformation with minimum energy. Herein, we investigate the effect of the disconnection of units on the mobility and stiffness of architected modular origami structures with deformable units using angular kinematics of geometry and topology of units and closed loops. Angular kinematics provides a valuable tool for investigating the complex mobility of architected modular origami structures with the disconnection of loops. The mobility of the network structure is a function not only of the number of disconnections but also of the topology of the loop. In contrast to the conventional negative perception of defects or disconnection in these materials, the disconnection can potentially be used to expand the design space of mobility for future robotic matter. Our findings can be used to develop powerful design guidelines for topologically reconfigurable structures for soft modular robots, active architected materials, implanted modular devices, deployable structures, thermal metamaterials, and active acoustic metamaterials. [ABSTRACT FROM AUTHOR]

When performing willed actions, we have the unified and coherent experience of owning and controlling our body. Body ownership is believed to emerge from the integration of coherent multisensory signals, while agency is believed to emerge from the coherence between predicted and perceived outcomes of actions. As a consequence, body ownership and agency can both be modulated by multisensory conflicts. The contribution of active movement generation to ownership and agency has not been parametrically explored. Here, we investigated the contribution of interaction force between the agent and the environment to the sense of hand ownership (SO) and the sense of hand agency (SA). By combining robotics and virtual reality, we manipulated the sensorimotor and visual information during immersive scenarios to induce and quantify altered states of SO and SA. First, we demonstrated that SO and SA could be successfully manipulated by our experimental paradigms. Second, we showed that interaction force strongly contributes to SA, but to a lesser extent to SO. Finally, we showed that SO and SA interact beyond their common multisensory basis. Our results, based on two independent studies, provide a direct link between sensorimotor interactions and subjective body experience and demonstrate a new dissociation between SO and SA. [ABSTRACT FROM AUTHOR]

This paper presents an autonomous navigation system for cost-effective magnetic-assisted colonoscopy, employing force-based sensors, an actuator, a proportional–integrator controller and a real-time heuristic searching method. The force sensing system uses load cells installed between the robotic arm and external permanent magnets to derive attractive force data as the basis for real-time surgical safety monitoring and tracking information to navigate the disposable magnetic colonoscope. The average tracking accuracy on magnetic field navigator (MFN) platform in x-axis and y-axis are 1.14 ± 0.59 mm and 1.61 ± 0.45 mm, respectively, presented in mean error ± standard deviation. The average detectable radius of the tracking system is 15 cm. Three simulations of path planning algorithms are presented and the learning real-time A* (LRTA*) algorithm with our proposed directional heuristic evaluation design has the best performance. It takes 75 steps to complete the traveling in unknown synthetic colon map. By integrating the force-based sensing technology and LRTA* path planning algorithm, the average time required to complete autonomous navigation of a highly realistic colonoscopy training model on the MFN platform is 15 min 38 s and the intubation rate is 83.33%. All autonomous navigation experiments are completed without intervention by the operator. [ABSTRACT FROM AUTHOR]