In an era marked by the rapid advancement of information technology, individuals now have the ability to enhance their organic bodies with mechanical and computational devices, revolutionizing their capabilities in everyday activities. However, the seamless integration of biosignals with mechanical counterparts continues to pose significant challenges. To address this, fuzzy logic (FL) emerges as a potential key to these challenges, offering a promising solution for handling the uncertainties and ambiguity inherent in biosignals. It thereby facilitates improved cyborg intelligence in an array of applications, including but not limited to wireless body area networks, brain–computer interfaces, prosthetics, and exoskeletons. Although previous works have highlighted the enhancement of cyborg intelligence using fuzzy sets, all of them only dived single aspects of the cyborg intelligence enhancement applications, leading to a lack of comprehensive understanding. Therefore, we provide a holistic overview of the state-of-the-art applications of FL in cyborg enhancement technology, encompassing its benefits, challenges, and potential directions for future research.

Robot-human object handover has been extensively studied in recent years for a wide range of applications. However, it is still far from being as natural as human-human handovers, largely due to the robots’ limited sensing capabilities. Previous approaches in the literature typically simplify the handover scenarios, including one or more of (a) conducting handovers at fixed locations, (b) not adapting to human preferences, or (c) only focusing on single-arm handover with small objects due to the sensor occlusions caused by large objects. To advance the state of the art toward a human-human level of handover fluency, this paper investigates a bimanual handover scenario in a naturalistic, complex setup. Specifically, we target robot-to-human box transfer while the human partner is on a ladder, and ensure that the object is adaptively delivered based on human preferences. To address the occlusion problem that arises in a complex environment, we develop an onboard multi-sensor perception system for the bimanual robot, introduce a measurement confidence estimation technique, and propose an occlusion-resilient multi-sensor fusion technique by positioning visual perception sensors in distinct locations on the robot with different fields of view. In addition, we establish a Cartesian space controller with a quaternion approach and a leader-follower control structure for compliant motion. Four distinct experiments are conducted, covering different human preferences (such as the box delivered above or below the hands) and significant handover location changes once the process has begun. For validation, the proposed multi-sensor fusion technique was compared to a single-sensor approach for both top and bottom sensors separately, and to simple averaging of both sensors. 30 repetitions were performed for each experiment (four experiments, four methods), the equivalent of 480 handover repetitions in total. Multi-sensor fusion approach achieved a handover success rate above$\textbf{86.7\%}$for all experiments by successfully combining the strengths of both fields of view for human pose tracking under significant occlusions without sacrificing handover duration. In contrast, due to the occlusions, the single-sensor and simple averaging approaches completely failed during challenging experiments, illustrating the importance of multi-sensor fusion in complex handover scenarios.Note to Practitioners—This paper is motivated by enabling naturalistic robot-to-human bimanual object handovers in complex environments, which is a challenging problem due to occlusions. Existing approaches in the literature do not benefit from multi-sensor fusion to handle occlusions, which is essential in such physical human-robot interaction scenarios. To this aim, we have developed a multi-sensor fusion technique to improve the perception capabilities of robots with respect to human co-workers. The developed framework has been tested with Microsoft Azure Kinect sensors and a bimanual mobile Baxter robot, but it can be adapted to any depth perception sensor and bimanual robotic platform. Furthermore, the introduced multi-sensor fusion technique is comprehensive and generic, as it can be applied to any intermittent sensor data, such as human pose tracking via RGBD sensors. The presented approach shows that increasing the field of view of robots‘ perception used with enhanced data fusion could drastically improve the robot‘s sensing capability. For future work, data fusion can be improved by introducing Bayesian filters, and the system can be validated with different sensors and robotic platforms. Moreover, the handover detection method of physical interaction could further benefit from the incorporation of force sensors.

The increasingly pervasive usage of robotic surgery not only calls for advances in clinical application but also implies high availability for preliminary medical education using virtual reality. Virtual reality is currently upgrading medical education by presenting complicated medical information in an immersive and interactive way. A system that allows multiple users to observe and operate via simulated surgical platforms using wearable devices has become an efficient solution for teaching where a real surgical platform is not available. This article developed a bioinspired virtual reality toolkit (BioVRbot) for education and training in robot-assisted minimally invasive surgery. It allows multiple users to manipulate the robots working on cooperative virtual surgery using bioinspired control. The virtual reality scenario is implemented using unity and can be observed with independent virtual reality headsets. A MATLAB server is designed to manage robot motion planning of incremental teleoperation compliance with the remote center of motion constraints. Wearable sensorized gloves are adopted for continuous control of the tooltip and the gripper. Finally, the practical use of the developed surgical virtual system is demonstrated with cooperative operation tasks. It could be further spread into the classroom for preliminary education of robot-assisted surgery for early-stage medical students.

Under-actuated bionic hands have achieved tremendous popularity in many fields because of their advantages of lightweight, budget-friendly, satisfactory flexibility, and adaptability. Except for the bionic mechanical design, various anthropomorphic control strategies have been proposed and investigated in the last decades. However, due to its under-actuated characteristic, there are still many challenges for anthropomorphic control of all the degrees of freedom (DOFs) using less input. It is challenging to map the human hand kinematic synergies on robotic hands, particularly for a dynamic environment. Therefore, it is worth studying how to control the under-actuated bionic hand effectively in a dynamic environment. In this paper, an anthropomorphic control method is proposed using sensor fusion of hand kinematic inputs to control the under-actuated bionic hand. In order to map the kinematics of human fingers to the bionic hand, a novel finger bending angle is defined to represent the posture of human fingers. Multiple Leap Motion Controllers (LMC) are fused to estimate the stable and accurate finger bending angles to avoid the occlusion problem. Finally, experiments with real-time control of the under-actuated bionic hand are implemented to demonstrate the proposed approach’s effectiveness.

Learning manipulation skills from open surgery provides more flexible access to the organ targets in the abdomen cavity and this could make the surgical robot working in a highly intelligent and friendly manner. Teaching by demonstration (TbD) is capable of transferring the manipulation skills from human to humanoid robots by employing active learning of multiple demonstrated tasks. This work aims to transfer motion skills from multiple human demonstrations in open surgery to robot manipulators in robot-assisted minimally invasive surgery (RA-MIS) by using TbD. However, the kinematic constraint should be respected during the performing of the learned skills by using a robot for minimally invasive surgery. In this article, we propose a novel methodology by integrating the cognitive learning techniques and the developed control techniques, allowing the robot to be highly intelligent to learn senior surgeons' skills and to perform the learned surgical operations in semiautonomous surgery in the future. Finally, experiments are performed to verify the efficiency of the proposed strategy, and the results demonstrate the ability of the system to transfer human manipulation skills to a robot in RA-MIS and also shows that the remote center of motion (RCM) constraint can be guaranteed simultaneously. Note to Practitioners-This article is inspired by limited access to the manipulation of laparoscopic surgery under a kinematic constraint at the point of incision. Current commercial surgical robots are mostly operated by teleoperation, which is representing less autonomy on surgery. Assisting and enhancing the surgeon's performance by increasing the autonomy of surgical robots has fundamental importance. The technique of teaching by demonstration (TbD) is capable of transferring the manipulation skills from human to humanoid robots by employing active learning of multiple demonstrated tasks. With the improved ability to interact with humans, such as flexibility and compliance, the new generation of serial robots becomes more and more popular in nonclinical research. Thus, advanced control strategies are required by integrating cognitive functions and learning techniques into the processes of surgical operation between robots, surgeon, and minimally invasive surgery (MIS). In this article, we propose a novel methodology to model the manipulation skill from multiple demonstrations and execute the learned operations in robot-assisted minimally invasive surgery (RA-MIS) by using a decoupled controller to respect the remote center of motion (RCM) constraint exploiting the redundancy of the robot. The developed control scheme has the following functionalities: 1) it enables the 3-D manipulation skill modeling after multiple demonstrations of the surgical tasks in open surgery by integrating dynamic time warping (DTW) and Gaussian mixture model (GMM)-based dynamic movement primitive (DMP) and 2) it maintains the RCM constraint in a smaller safe area while performing the learned operation in RA-MIS. The developed control strategy can also be potentially used in other industrial applications with a similar scenario.

In this paper, an Internet of Things-based human-robot collaborative control scheme is developed in Robot-assisted Minimally Invasive Surgery scenario. A hierarchical operational space formulation is designed to exploit the redundancies of the 7-DoFs redundant manipulator to handle multiple operational tasks based on their priority levels, such as guaranteeing a remote center of motion constraint and avoiding collision with a swivel motion without influencing the undergoing surgical operation. Furthermore, the concept of the Internet of Robotic Things is exploited to facilitate the best action of the robot in human-robot interaction. Instead of utilizing compliant swivel motion, HTC VIVE PRO controllers, used as the Internet of Things technology, is adopted to detect the collision. A virtual force is applied to the robot elbow, enabling a smooth swivel motion for human-robot interaction. The effectiveness of the proposed strategy is validated using experiments performed on a patient phantom in a lab setup environment, with a KUKA LWR4+ slave robot and a SIGMA 7 master manipulator. By comparison with previous works, the results show improved performances in terms of the accuracy of the RCM constraint and surgical tip.