Safe Robot Research Articles

A novel stiffness-controllable joint using antagonistic actuation principles

The inherent safety of collaborative robots is essential for enhancing human–robot interaction. The primary challenge in creating soft components for these robots is achieving sufficient force and stiffness. This paper presents a joint design for collaborative robots that addresses this challenge by incorporating an antagonistic actuation principle, allowing for adjustable stiffness. The novelty of our variable-stiffness joint lies in achieving a wide range of stiffness variation at any bending angle through antagonistic actuation. This bio-inspired principle results from the activation of two opposing actuation chambers. Compared to existing joints, our proposed joint is compact and utilises a high percentage of soft materials, enabling safer human–robot interaction. The paper outlines the joint’s design and fabrication process, highlighting the feasibility of our innovative concept. Kinematic and stiffness models are introduced to analyse the bending and stiffness characteristics, which are further validated through experimental testing. The stiffness experiments demonstrate significant stiffness changes achievable through the antagonistic actuation principle. Additionally, force experiments reveal our joint can generate 20 N force at a 1.5×105 Pa pressure. A constant force output experiment confirms the joint’s advantages in providing consistent force compared to motors. Finally, a case study showcases how our proposed joint can be embedded in serial robots with variable stiffness capabilities under loading and safe human–robot interaction.

Novel Adaptive Path-Smoothening Optimization Method for Mobile Robots

A safe and smooth operating path is a prerequisite for mobile robots to accomplish tasks. Although the existing path optimization methods improve the smoothness of the planned path by introducing Bezier curve to locally optimize the path with regard to turning points, most of these methods manually select the position of control points and subjectively analyze the feasibility of the optimized path. It is argued unfavorably that it exhibits strong subjectivity and cumbersome selection process. To fill this gap, an adaptive path-smoothening optimization method is proposed in this study, which combines neural network, genetic algorithm, and Bezier curve to transform the path smoothing problem into an optimization problem. It rapidly determines the position of the optimal control point based on comprehension of constraints, e.g., path safety, curvature and kinematic restrains of the robot. The currently proposed method resolves the long-standing problems of strong subjectivity, cumbersome steps, and thus low efficiency in the selection process of control points, and lays the theoretical groundwork for smoothening the locus and path. To start with, according to the actual working conditions, the dataset corresponding to the position of the control point and the path deviation is constructed, and the neural network algorithm is used to solve the prediction model of the path deviation, so as to obtain the mapping relationship between the length and included angle of the control edge in the second-order Bezier curve and the path deviation. Subsequently, with reference to the prediction model of path deviation, a reliability evaluation function is formulated by comprehending multiple influential factors of mobile robot motion safety and path smoothness. The genetic algorithm is then introduced to detect the satisfactory control points in different environments. The currently proposed method is verified by experiments in different operating environments. The study results show that the currently proposed adaptive path-smoothening optimization method exhibits remarkably superior applicability and effectiveness compared to the currently prevailing methods. It demonstrates advantages of fast path planning, reduced path turning points, and desirable path smoothness. In addition, it can also ensure the safety of mobile robot along the planned path as availed by a pre-set criterion.

Reliable and Accurate Implicit Neural Representation of Multiple Swept Volumes with Application to Safe Human–Robot Interaction

In automated production using collaborative robots in a manufacturing cell, a crucial aspect is to avoid collisions to ensure the safety of workers and robots in human–robot interaction. One approach for detecting collisions is using the swept volume (SV) to identify safe protective space for operation. We learn an accurate and reliable signed distance function (SDF) network from raw point clouds of a pre-computed SV to represent a class of linear joint motion trajectories. The network requires only a set of parameters and constant execution time, thus reducing the computational time and memory of collision checking due to the complexity of explicit geometry during task execution. The distance of collision danger foresaw by the learned SDF is exploited to reduce the frequency of collision detection calls in the dynamic environment and reduce the computational cost further. We assess the relative merits of the implicit neural representation of multiple SVs in terms of F1-score, error distance from the surface of the truth geometry, and 3D visualization by comparing favorably with a binary voxel network for learning a single SV with similar inference time. All the predicted errors of the geometry lie within a distance of 4 voxels from the surface of the truth geometry, and most reconstruction errors are within 3 voxels. A simulation of pick-and-place task execution in the human–robot interaction scenarios by leveraging the learned SDF as an efficient continuous collision detector is performed. The improvement in execution time and collision detection number is validated in the simulation.

Research on high-precision tracking and localization of industrial robots based on iGPS

High-precision tracking and localization of the end of industrial robots is one of the most important technologies to realize high efficiency, high precision, and high safety of robots. Indoor global positioning system (iGPS), as a widely used indoor measurement system, has many advantages that cannot be replaced by other measurement devices, such as high scalability, simultaneous measurement of multiple targets, etc. However, due to the unique measurement principle of iGPS, it does not have good dynamic measurement performance. In order to enhance the dynamic measurement performance of iGPS and maximize its advantages, this paper proposes a correction algorithm that can effectively improve the dynamic measurement accuracy of iGPS based on the ideas of ‘transforming motion into static’ and ‘equivalent substitution’ after in-depth research on the measurement principle of iGPS. To verify the correctness of the mathematical principle of the algorithm and the value of engineering application, this paper has carried out a MATLAB simulation test and real measurement test. The experimental results show that the proposed correction algorithm can effectively improve the dynamic measurement accuracy of iGPS, and the correction algorithm can reduce the global error of the measurement results to within 0.1 mm in any robot trajectory and reasonable speed range, which is in line with the general requirements of industrial measurement.

Structural topic modeling-based systematic review for service robots’ fault diagnosis: content, popularity and regional differences

Abstract The market for service robots is expanding as labor costs continue to rise. Faced with intricate working environments, fault detection and diagnosis are crucial to ensure the proper functioning of service robots. The objective of this review is to systematically investigate the realm of service robots’ fault diagnosis through the application of Structural Topic Modeling. A total of 289 papers were included, culminating in ten topics, including advanced algorithm application, data learning-based evaluation, automated equipment maintenance, actuator diagnosis for manipulator, non-parametric method, distributed diagnosis in multi-agent systems, signal-based anomaly analysis, integrating complex control framework, event knowledge assistance, mobile robot particle filtering method. These topics spanned service robot hardware and software failures, diverse service robot systems, and a range of advanced algorithms for fault detection in service robots. Asia-Pacific, Europe, and the Americas, recognized as three pivotal regions propelling the advancement of service robots, were employed as covariates in this review to investigate regional disparities. The review found that current research tends to favor the use of artificial intelligence (AI) algorithms to address service robots’ complex system faults and vast volumes of data. The topics of algorithms, data learning, automated maintenance, and signal analysis are advancing with the support of AI, gaining increasing popularity as a burgeoning trend. Additionally, variations in research focus across different regions were found. The Asia-Pacific region tends to prioritize algorithm-related studies, while Europe and the Americas show a greater emphasis on robot safety issues. The integration of diverse technologies holds the potential to bring forth new opportunities for future service robot fault diagnosis.Simultaneously, regional standards about data, communication, and other aspects can streamline the development of methods for service robots’ fault diagnosis.

Design of Human-Machine Integration System to Meet Diverse Interactive Tasks

A flexible production model in which humans and robots collaborate has become an urgent need for industrial production. Therefore, research on human-machine integration systems has received more and more attention in recent years. Human-computer interaction is widely used in industrial production and entertainment life, such as industrial control, VR games, etc. In this article, we designed and implemented a human-machine fusion system that meets different interactive tasks. Human movement estimation and robot interactive control are the key technologies. First, we propose a human posture solution method based on information fusion of multiple cameras and inertial measurement units. This method constructs an optimization problem, fuses the 2D joint detection information under multiple cameras and the measurement information of the worn inertial measurement unit, and optimizes the estimation of human kinematic posture, which improves the problems of incomplete posture information and sensitivity to noise under a single sensor. Therefore, this method improves the accuracy of pose estimation. Secondly, combining the kinematic characteristics of the robot and the characteristics of human-computer interaction, we designed a robot control strategy based on target point tracking and model predictive control, so that the robot can adapt to dynamic environments and different interaction needs by adjusting control parameters. At the same time, our design ensures the safety of robots and operators. Finally, we conducted human-computer interaction experiments such as action following, item delivery, and active obstacle avoidance. The experimental results show the effectiveness and reliability of the designed robot interaction system in a human-machine fusion environment.

Actuators for Improving Robotic Arm Safety While Maintaining Performance: A Comparison Study

Since robotic arms operating close to people are becoming increasingly common, there is a need to better understand how they can be made safe when unintended contact occurs, while still providing the required performance. Several actuators and methods for improving robot safety are studied and compared in this paper. A robotic arm moving its end effector horizontally and colliding with a person’s head is simulated. The use of a conventional electric actuator (CEA), series elastic actuator (SEA), pneumatic actuator (PA) and hybrid pneumatic electric actuator (HPEA) with model-based controllers are studied. The addition of a compliant covering to the arm and the use of collision detection and reaction strategies are also studied. The simulations include sensor noise and modeling error to improve their realism. A systematic method for tuning the controllers fairly is proposed. The motion control performance and safety of the robot are quantified using root mean square error (RMSE) between the desired and actual joint angle trajectories and maximum impact force (MIF), respectively. The results show that the RMSE values are similar when the CEA, SEA, and HPEA drive the robot’s first joint. Regarding safety, using the PA or HPEA with a compliant covering can reduce the MIF below the safety limit established by the International Organization for Standardization (ISO). To satisfy this ISO safety limit with the CEA and SEA, a collision detection and reaction strategy must be used in addition to the compliant covering. The influences of the compliant covering’s stiffness and the detection delay are also studied.

Design and analysis of an autonomous warehouse robot system with 6-DOF manipulator

With the increasing need for efficiency and accuracy in warehouse operations, the functions and market demands of automated warehouse robots are constantly increasing. This study presents the design, simulation, and implementation of a warehouse robot, showcasing effective automation solution. Leveraging the Robot Operating System (ROS) and Gazebo, a robot with a six-degree-of-freedom robotic arm for diverse manipulation tasks and a differential drive base for broad-spectrum navigation was designed. The simulation environment in Gazebo faithfully replicates real-world warehouse conditions, enabling comprehensive path planning and real-time modifications, powered by move_base. A camera sensor serves as the robot's safety system, designed to detect moving obstacles and initiate appropriate responses, contributing to the enhancement of warehouse safety standards. Simulation results demonstrate the robot's effectiveness in performing pick-and-place tasks while successfully navigating through the environment, indicating the significant potential for real-world warehouse automation applications. Therefore, this work provides a foundation reference for future research aimed at optimizing and expanding the capabilities of autonomous warehouse robots.

An Opposed Piston Driving Pneumatic Stepper Motor for MR Intervention Robotics

Magnetic resonance (MR) safe robots offer many advantages to robot-assisted surgeries in the magnetic resonance imaging (MRI) environment. As one of the key components, the design of actuators that work in the MR environment is critical for MR safe robots. This article proposed a new piston motor powered by compressed air for MR safe robots, in which the working principle is investigated, and the dynamic model is analyzed. Linear motion is achieved by applying the lead screw transmission. Experiments demonstrate that the rotary motor has a maximum speed of 800 r/min and maximum output torque of 132 mN·m, and the linear motor has an output force of 50 N, both at a speed of 2 steps/s. The step response and error analysis are also conducted. MR tests show that there are no obvious image artifacts caused by the motor.

Enhanced robot obstacle avoidance strategy: efficient distance estimation and collision avoidance for hidden robots

Human-robot interaction is crucial for the future of smart factories and new industrial systems. Safety in robotics has always been a top priority, with external sensors being studied to construct safety perception systems for robots. This paper proposes an obstacle avoidance strategy based on an efficient distance estimation method using a vision sensor to address the challenge of robot occlusion. The method fuses depth images with a predefined robot skeleton model to estimate robot pose in real time, and uses the optimized potential field model to achieve full-body collision avoidance. Comparative experiments validate the efficiency of the proposed method, which represents a significant contribution to enhancing human–robot interaction and safety in industrial settings.

Assessing risk of AR and organizational changes factors in socio-technical robotic manufacturing

Technological changes such as the use of Augmented Reality (AR) along with the advent of new organizational changes such as digitalization are on the one hand positively changing the way of working but on the other hand they are introducing new risks, potentially leading to not only normal but also post-normal accidents. In our previous work, we have incrementally proposed a novel framework, called FRAAR, for risk assessment of AR-equipped socio-technical systems (i.e., systems integrating human, organizational and technical entities (such as AR)). We have also partly evaluated our framework via an industrial automotive study and by providing comparison and positioning with respect to other related works in a systematic literature review. In this paper, we conduct a new study to evaluate the applicability and effectiveness of our framework in a different domain. To do that, we choose a digitalized socio-technical factory system, focusing on the human–robot collaboration for a realistic diesel engine assembly task using AR-based user interface in an organization affected by organizational changes. Then, we design and execute our study to apply our framework and we discuss about the extent the conceptualizations provided by the framework are effective to capture the essential information for risk assessment in socio-technical robotic manufacturing, the extent the robotic safety standards are supported (to demonstrate the applicability of the framework in the robotic domain) and the extent of effectiveness of the risk assessment with respect to AR and organizational changes. Finally, we discuss about validity of our work and we provide our findings and intended future work.

Perceived Safety Assessment of Interactive Motions in Human-Soft Robot Interaction.

Soft robots, especially soft robotic hands, possess prominent potential for applications in close proximity and direct contact interaction with humans due to their softness and compliant nature. The safety perception of users during interactions with soft robots plays a crucial role in influencing trust, adaptability, and overall interaction outcomes in human-robot interaction (HRI). Although soft robots have been claimed to be safe for over a decade, research addressing the perceived safety of soft robots still needs to be undertaken. The current safety guidelines for rigid robots in HRI are unsuitable for soft robots. In this paper, we highlight the distinctive safety issues associated with soft robots and propose a framework for evaluating the perceived safety in human-soft robot interaction (HSRI). User experiments were conducted, employing a combination of quantitative and qualitative methods, to assess the perceived safety of 15 interactive motions executed by a soft humanoid robotic hand. We analyzed the characteristics of safe interactive motions, the primary factors influencing user safety assessments, and the impact of motion semantic clarity, user technical acceptance, and risk tolerance level on safety perception. Based on the analyzed characteristics, we summarize vital insights to provide valuable guidelines for designing safe, interactive motions in HSRI. The current results may pave the way for developing future soft machines that can safely interact with humans and their surroundings.

-Designing a Dislocation Function to Improve the Safety of Bipedal Robots-

-Designing a Dislocation Function to Improve the Safety of Bipedal Robots-

Vision-and Tactile-Based Continuous Multimodal Intention and Attention Recognition for Safer Physical Human–Robot Interaction

Employing skin-like tactile sensors on robots enhances both the safety and usability of collaborative robots by adding the capability to detect human contact. Unfortunately, simple binary tactile sensors alone cannot determine the context of the human contact—whether it is a deliberate interaction or an unintended collision that requires safety manoeuvres. Many published methods classify discrete interactions using more advanced tactile sensors or by analysing joint torques. Instead, we propose to augment the intention recognition capabilities of simple binary tactile sensors by adding a robot-mounted camera for human posture analysis. Different interaction characteristics, including touch location, human pose, and gaze direction, are used to train a supervised machine learning algorithm to classify whether a touch is intentional or not with an F1-score of 86%. We demonstrate that multimodal intention recognition is significantly more accurate than monomodal analyses with the collaborative robot Baxter. Furthermore, our method can also continuously monitor interactions that fluidly change between intentional or unintentional by gauging the user’s attention through gaze. If a user stops paying attention mid-task, the proposed intention and attention recognition algorithm can activate safety features to prevent unsafe interactions. We also employ a feature reduction technique that reduces the number of inputs to five to achieve a more generalized low-dimensional classifier. This simplification both reduces the amount of training data required and improves real-world classification accuracy. It also renders the method potentially agnostic to the robot and touch sensor architectures while achieving a high degree of task adaptability. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Whenever a user interacts physically with a robot, such as in collaborative manufacturing, the robot may respond to unintended touch inputs from the user. This may be through body collisions or that the user is suddenly distracted and is no longer paying attention to what they are doing. We propose an easy-to-implement method to augment safety of physical human-robot collaboration by determining whether the touch from a user is intentional or not through the use of robot-mounted basic touch sensors and computer vision. The algorithm examines the location of the user’s hands relative to the touched sensors in addition to observing where the user is looking. Machine learning is then used to classify in real-time, with an F1-score of 86%, whether a touch is intentional or not such that the robot can react accordingly. The method is particularly applicable in collaborative manufacturing contexts, but can also be applied anywhere where a user physically interacts with a robot. We demonstrate the utility of the method in enhancing safety during human-robot collaboration through a simulated collaborative manufacturing scenario with the robot Baxter, but the method can easily be adapted to other systems.

Suitability assessment method for safe robot tooling design in Human-Robot Collaborative applications

State of the art applications in human-robot collaborative work cells, are, sometimes, highly compromised by strict directives to avoid potential hazards for operators. Robot motion (e.g., speed, momentum, power etc.) is strictly defined according to ISO/TS15066 to guarantee that potential pressures and forces due to collisions do not exceed predefined limits for different parts of the human body. In this direction the authors suggest a robotic tool design methodology that could optimize Human – Robot Collaborative (HRC) applications by preventing vast reduction of robot velocity during the design phase, while eliminating the need for redesign during the validation stages of the cell. Also, actuation mechanisms and their components are also presented to uphold human safety standards. To demonstrate the effectiveness of the proposed method, a real-life industrial use case has been materialized, involving a high-payload collaborative robot performing assembly operations. The results underscore the effectiveness of these design practices in enhancing the efficiency and safety of human-robot collaboration.

Assessment of the risk of a dangerous event of a human collision with a remote-controlled robot

This study assesses the risk of a collision between a human and the “Robot Arm” during remote control, using a 6DoIt Mobile Robot Arm with six degrees of freedom. The bow-tie approach, combined with EN ISO 12100 standards, was employed to evaluate the risk of such a dangerous event, considering physical, organizational, psychosocial, and informational factors. The proposed risk assessment method is based on the bow-tie model, emphasizing the importance of determining the possibility of avoiding hazards as per EN ISO 12100. A three-level protective system – physical, psychological, and informational – is suggested to mitigate the risk, interconnected to enhance safety by reducing the severity of potential consequences. The approach has been refined to better determine the severity of outcomes, focusing on the ability to avoid danger. Additionally, recommendations for enhancing robotics safety management were developed, aiming to improve the effectiveness of measures to reduce collision risks during the operation of the “Robot Arm”.

Use of U-sensor for motion safety of a collaborative manipulative robot

One approach to increasing the operational efficiency of robotics is to utilize the capabilities of modern collabora-tive applications of robots. Within the framework of the paper, issues related to the possibility of utilizing the availa-ble sensor base, namely ultrasonic sensors for the sensing of robots for further collaborative use are discussed. A model of a sensing robot is presented and elements of a control algorithm for a collaborative robot are proposed.

Learning Failure Prevention Skills for Safe Robot Manipulation

Learning Failure Prevention Skills for Safe Robot Manipulation

A Novel Path Planning Approach for Mobile Robot in Radioactive Environment Based on Improved Deep Q Network Algorithm

The path planning problem of nuclear environment robots refers to finding a collision-free path under the constraints of path length and an accumulated radiation dose. To solve this problem, the Improved Dueling Deep Double Q Network algorithm (ID3QN) based on asymmetric neural network structure was proposed. To address the issues of overestimation and low sample utilization in the traditional Deep Q Network (DQN) algorithm, we optimized the neural network structure and used the double network to estimate action values. We also improved the action selection mechanism, adopted a priority experience replay mechanism, and redesigned the reward function. To evaluate the efficiency of the proposed algorithm, we designed simple and complex radioactive grid environments for comparison. We compared the ID3QN algorithm with traditional algorithms and some deep reinforcement learning algorithms. The simulation results indicate that in the simple radioactive grid environment, the ID3QN algorithm outperforms traditional algorithms such as A*, GA, and ACO in terms of path length and accumulated radiation dosage. Compared to other deep reinforcement learning algorithms, including DQN and some improved DQN algorithms, the ID3QN algorithm reduced the path length by 15.6%, decreased the accumulated radiation dose by 23.5%, and converged approximately 2300 episodes faster. In the complex radioactive grid environment, the ID3QN algorithm also outperformed the A*, GA, ACO, and other deep reinforcement learning algorithms in terms of path length and an accumulated radiation dose. Furthermore, the ID3QN algorithm can plan an obstacle-free optimal path with a low radiation dose even in complex environments. These results demonstrate that the ID3QN algorithm is an effective approach for solving robot path planning problems in nuclear environments, thereby enhancing the safety and reliability of robots in such environments.

A review on model-based and model-free approaches to control soft actuators and their potentials in colonoscopy.

Colorectal cancer (CRC) is the third most common cancer worldwide and responsible for approximately 1 million deaths annually. Early screening is essential to increase the chances of survival, and it can also reduce the cost of treatments for healthcare centres. Colonoscopy is the gold standard for CRC screening and treatment, but it has several drawbacks, including difficulty in manoeuvring the device, patient discomfort, and high cost. Soft endorobots, small and compliant devices thatcan reduce the force exerted on the colonic wall, offer a potential solution to these issues. However, controlling these soft robots is challenging due to their deformable materials and the limitations of mathematical models. In this Review, we discuss model-free and model-based approaches for controlling soft robots that can potentially be applied to endorobots for colonoscopy. We highlight the importance of selecting appropriate control methods based on various parameters, such as sensor and actuator solutions. This review aims to contribute to the development of smart control strategies for soft endorobots that can enhance the effectiveness and safety of robotics in colonoscopy. These strategies can be defined based on the available information about the robot and surrounding environment, control demands, mechanical design impact and characterization data based on calibration.