Active Collision Avoidance for Human-Robot Interaction With UKF, Expert System, and Artificial Potential Field Method

An Image-Based Human-Robot Collision Avoidance Scheme: A Proof of Concept

OCCUPATIONAL APPLICATIONSIn modern industrial plants, collisions between humans and robots pose a significant risk to occupational safety. To address this concern, we sought to devise a reliable system for human-robot collision avoidance system employing computer vision. This system enables the proactive prevention of dangerous collisions between humans and robots. In contrast to previous approaches, we used a standard RGB camera, making implementation more convenient and cost-effective. Furthermore, the proposed method greatly extends the effective detection range compared to previous studies, thereby enhancing its utility for monitoring large-scale workplaces.

Augmented Reality (AR) for Surgical Robotic and Autonomous Systems: State of the Art, Challenges, and Solutions

Despite the substantial progress achieved in the development and integration of augmented reality (AR) in surgical robotic and autonomous systems (RAS), the center of focus in most devices remains on improving end-effector dexterity and precision, as well as improved access to minimally invasive surgeries. This paper aims to provide a systematic review of different types of state-of-the-art surgical robotic platforms while identifying areas for technological improvement. We associate specific control features, such as haptic feedback, sensory stimuli, and human-robot collaboration, with AR technology to perform complex surgical interventions for increased user perception of the augmented world. Current researchers in the field have, for long, faced innumerable issues with low accuracy in tool placement around complex trajectories, pose estimation, and difficulty in depth perception during two-dimensional medical imaging. A number of robots described in this review, such as Novarad and SpineAssist, are analyzed in terms of their hardware features, computer vision systems (such as deep learning algorithms), and the clinical relevance of the literature. We attempt to outline the shortcomings in current optimization algorithms for surgical robots (such as YOLO and LTSM) whilst providing mitigating solutions to internal tool-to-organ collision detection and image reconstruction. The accuracy of results in robot end-effector collisions and reduced occlusion remain promising within the scope of our research, validating the propositions made for the surgical clearance of ever-expanding AR technology in the future.

A Critical Analysis of Industrial Human-Robot Communication and Its Quest for Naturalness Through the Lens of Complexity Theory

Human-robot communication is one of the actively researched fields to enable efficient and seamless collaboration between a human and an intelligent industrial robotic system. The field finds its roots in human communication with the aim to achieve the “naturalness” inherent in the latter. Industrial human-robot communication pursues communication with simplistic commands and gestures, which is not representative of an uncontrolled real-world industrial environment. In addition, naturalness in communication is a consequence of its dynamism, typically ignored as a design criterion in industrial human-robot communication. Complexity Theory-based natural communication models allow for a more accurate representation of human communication which, when adapted, could also benefit the field of human-robot communication. This paper presents a perspective by reviewing the state of human-robot communication in industrial settings and then presents a critical analysis of the same through the lens of Complexity Theory. Furthermore, the work identifies research gaps in the aforementioned field, fulfilling which, would propel the field towards a truly natural form of communication. Finally, the work briefly discusses a general framework that leverages the experiential learning of data-based techniques and naturalness of human knowledge.

Asymmetric Identification Model for Human-Robot Contacts via Supervised Learning

Human-robot interaction (HRI) occupies an essential role in the flourishing market for intelligent robots for a wide range of asymmetric personal and entertainment applications, ranging from assisting older people and the severely disabled to the entertainment robots at amusement parks. Improving the way humans and machines interact can help democratize robotics. With machine and deep learning techniques, robots will more easily adapt to new tasks, conditions, and environments. In this paper, we develop, implement, and evaluate the performance of the machine-learning-based HRI model in a collaborative environment. Specifically, we examine five supervised machine learning models viz. the ensemble of bagging trees (EBT) model, the k-nearest neighbor (kNN) model, the logistic regression kernel (LRK), the fine decision trees (FDT), and the subspace discriminator (SDC). The proposed models have been evaluated on an ample and modern contact detection dataset (CDD 2021). CDD 2021 is gathered from a real-world robot arm, Franka Emika Panda, when it was executing repetitive asymmetric movements. Typical performance assessment factors are applied to assess the model effectiveness in terms of detection accuracy, sensitivity, specificity, speed, and error ratios. Our experiential evaluation shows that the ensemble technique provides higher performance with a lower error ratio compared with other developed supervised models. Therefore, this paper proposes an ensemble-based bagged trees (EBT) detection model for classifying physical human–robot contact into three asymmetric types of contacts, including noncontact, incidental, and intentional. Our experimental results exhibit outstanding contact detection performance metrics scoring 97.1%, 96.9%, and 97.1% for detection accuracy, precision, and sensitivity, respectively. Besides, a low prediction overhead has been observed for the contact detection model, requiring a 102 µS to provide the correct detection state. Hence, the developed scheme can be efficiently adopted through the application requiring physical human–robot contact to give fast accurate detection to the contacts between the human arm and the robot arm.

A Collision Avoidance Algorithm for Human Motion Prediction Based on Perceived Risk of Collision: Part 1-Model Development

OCCUPATIONAL APPLICATIONSDigital human models have been widely used in occupational biomechanics assessments to prevent potential injury risks, such as automotive assembly lines, box lifting, patient repositioning, and the mining industry. Motion prediction is one of the important capabilities in digital human models, and collision avoidance is involved in human motion prediction. We propose an algorithm that will ensure human motions are predicted realistically, and finally, use of this algorithm could help enhance the accuracy of injury risk assessments using digital human models.

A Collision Avoidance Algorithm for Human Motion Prediction Based on Perceived Risk of Collision: Part 2-Application

Occupational ApplicationDigital human models have been widely used for occupational assessments to reduce potential injury risk, such as automotive assembly lines, box lifting, and in the mining industry. Human motion prediction is one of the important capabilities in digital human models, and collision avoidance is involved in human motion prediction. An algorithm proposed earlier was implemented for human motion prediction, and simulated results were found to have a good correlation with the experimental studies. Use of this algorithm can help ensure that human motion is predicted realistically, and thus can impact the accuracy of injury risk assessments.

Adaptive Obstacle Avoidance for a Class of Collaborative Robots

In a human–robot collaboration scenario, operator safety is the main problem and must be guaranteed under all conditions. Collision avoidance control techniques are essential to improve operator safety and robot flexibility by preventing impacts that can occur between the robot and humans or with objects inadvertently left within the operational workspace. On this basis, collision avoidance algorithms for moving obstacles are presented in this paper: inspired by algorithms already developed by the authors for planar manipulators, algorithms are adapted for the 6-DOF collaborative manipulators by Universal Robots, and some new contributions are introduced. First, in this work, the safety region wrapping each link of the manipulator assumes a cylindrical shape whose radius varies according to the speed of the colliding obstacle, so that dynamical obstacles are avoided with increased safety regions in order to reduce the risk, whereas fixed obstacles allow us to use smaller safety regions, facilitating the motion of the robot. In addition, three different modalities for the collision avoidance control law are proposed, which differ in the type of motion admitted for the perturbation of the end-effector: the general mode allows for a 6-DOF perturbation, but restrictions can be imposed on the orientation part of the avoidance motion using 4-DOF or 3-DOF modes. In order to demonstrate the effectiveness of the control strategy, simulations with dynamic and fixed obstacles are presented and discussed. Simulations are also used to estimate the required computational effort in order to verify the transferability to a real system.

A novel multi-DoF surgical robotic system for brachytherapy on liver tumor: Design and control

Purpose

Radioactive seed implantation is an effective invasive treatment method for malignant liver tumors in hepatocellular carcinomas. However, challenges of the manual procedure may degrade the efficacy of the technique, such as the high accuracy requirement and radiation exposure to the surgeons. This paper aims to develop a robotic system and its control methods for assisting surgeons on the treatment.

Method

We present an interventional robotic system, which consists of a 5 Degree-of-Freedom (DoF) positioning robotic arm (a 3-DoF translational joint and a 2-DoF revolute joint) and a needle actuator used for needle insertion and radioactive seeds implantation. Control strategy is designed for the system to ensure the safety of the motion. In the designed framework, an artificial potential field (APF)-based motion planning and an ultrasound (US) image-based contacting methods are proposed for the control.

Result

Experiments were performed to evaluate position and orientation accuracy as well as validate the motion planning procedure of the system. The mean and standard deviation of targeting error is 0.69 mm and 0.33 mm, respectively. Needle placement accuracy is 1.10 mm by mean. The feasibility of the control strategy, including path planning and the contacting methods, is demonstrated by simulation and experiments based on an abdominal phantom.

Conclusion

This paper presents a robotic system with force and US image feedback in assisting surgeons performing brachytherapy on liver tumors. The proposed robotic system is capable of executing an accurate needle insertion task with by optical tracking. The proposed methods improve the safety of the robot’s motion and automate the process of US probe contacting under the feedback of US-image.