Safe Robot Research Articles

Integration of robotic in the reprocessing and transfer of endoscopes

Background and study aims Optimal hygiene is crucial for patients undergoing flexible endoscopy. Reprocessing is currently influenced by manual procedures performed by endoscopy staff. To overcome this limitation, we designed and evaluated the integration of robotic application for an automated endoscope processing pathway. Methods We used an endoscope reprocessing pass through machine with drying cabinet and a Franka Emika Panda robot. The robot was programmed to interact with its environment in a compliant way, guaranteeing desired contact force thresholds and therefore ensuring safety of both robot and medical equipment. Results In an initial phase we tested the robots’ ability to handle a modified tray holding an endoscope as well as certain challenges (correct positioning, connection of tubing, undesired collisions). We added another Panda robot arm resulting in a device featuring two independent manipulators and tested the accuracy of each individual step. We evaluated 50 consecutive processing and transfer procedures, simulating the average daily throughput of an endoscopic unit. The endoscopes were removed in adapted tray using a specially designed lifting device and placed in an endoscope storage and venting cabinet. The mean time for the handling of the scope was 104.2 ± 1.2 seconds and an accuracy of 100 % (0 failures in 50 attempts) was achieved.Conclusions To the best of our knowledge, this is the first description and evaluation of an automated compliant robotic assistance in the processing of endoscopes. Further development could help to overcome shortcomings of the man handled endoscope processing and could lead to reproducible, standardized and certified endoscope processing.

Discriminative feature learning using a multiscale convolutional capsule network from attitude data for fault diagnosis of industrial robots

Effective fault diagnosis is important to ensure the reliability, safety, and efficiency of industrial robots. This article proposes a simple yet effective data acquisition strategy based on transmission mechanism analysis, using only one attitude sensor mounted on an end effector or an output component to monitor the attitude of all transmission components. Unlike widely used vibration-monitoring signals, attitude signals can provide fault features reflecting spatial relationships. Using one attitude sensor facilitates the data collection, but weakens fault features and introduces strong background noise in attitude signals. To learn discriminative features from the attitude data collected by the attitude sensor, a multiscale convolutional capsule network (MCCN) is proposed. In MCCN, integrating low-level and high-level features in a convolutional neural network (CNN) as multiscale features is conductive to noise reduction and robust feature extraction, and a capsule network (CapsNet) is used to recognize the spatial relationships in attitude data. The extracted multiscale features in CNN and the spatial-relational features in CapsNet are fused for effective fault diagnosis of industrial robots. The performance of MCCN is evaluated by attaching a softmax-based classifier and integrating it into different transfer learning frameworks to diagnose faults in industrial robots under single and variable working conditions, respectively. Fault diagnosis experiments were conducted on a 6-axis series industrial robot and a parallel robot-driven 3D printer. The superiority of the proposed MCCN was demonstrated by comparing its performance with the other feature learning methods.

A review of mechanical model, structure, and prospect for long-distance pipeline pig and robot

AbstractThe safety and reliability of robots are very important for the inspection in a long-distance pipeline used for the oil and gas transportation. In this paper, the long-distance pipeline pig and robots (LDPPRs) are classified into two categories, which are velocity-uncontrollable type and velocity-controllable type. Among them, the velocity control of velocity-controllable LDPPR has three ways, which are friction resistance, driving force, and self-running. Meanwhile, the mechanical models of the motion of the LDPPRs are classified into the dynamic model of the velocity-uncontrollable LDPPRs, velocity-controllable LDPPRs, and vibration, and the contact mechanics model between the rubber barrel and the pipe wall. In addition, the anti-stuck technologies, the inspection technologies, and the location technologies are investigated and analyzed. Thus, the purpose of this review is to provide a concise reference for the development and research directions, as well as the design of the LDPPRs.

깊이 이미지를 이용한 딥러닝 기반 충돌 확률 예측 및 장애물 회피 알고리즘

This paper proposes a vision-based navigation algorithm in cluttered environments by using a convolutional neural network (CNN)-based deep learning and depth images. Existing approaches for robot navigation are developed based on obstacle detection or an accurate map to generate a path, which requires complex computation. To solve this issue, we propose a deep-learning-based navigation algorithm that does not require a preobtained map or a recognition of obstacles. In addition, by exploiting a collision probability prediction using a depth image, our method automatically adjusts the velocity command while passing the complex area, so we can improve the safety of a mobile robot. To validate the performance of our proposed method, we conduct a simulation with a mobile robot in a Gazebo environment. The simulation results show that our method outperforms the conventional avoidance algorithm.

P-455 Laparoscopic robot-assisted autologous transplantation of cryopreserved ovarian tissue: report of an operative technique and reproductive outcomes in a case series

Abstract Study question Is laparoscopic robot-assisted autologous transplantation of ovarian tissue feasible and is the reproductive outcome adequate in comparison with published data? Summary answer Laparoscopic robot-assisted autologous transplantation of ovarian tissue is feasible and safe. The reproductive outcome in our series is comparable with published data. What is known already Autologous cryopreserved ovarian tissue transplantation (ACOTT) is recognized as a valid fertility preservation technique among young patients facing iatrogenic premature ovarian failure (IOP). Orthotopic transplantation consists in the reimplantation of ovarian tissue back to the medulla of the ovary or into a specially created peritoneal pouch. It has been described by laparoscopy or mini laparotomy. It is the most efficient technique to restore endocrine function and fertility. Conception and live birth rates have been reported to be respectively 38% and 26%. The robot-assisted laparoscopic approach was recently described in a 7-case series. Study design, size, duration We prospectively registered all ACOTT procedures performed in our department between November 2004 and January 2022. Participants/materials, setting, methods We analyzed the data of 29 ACOTT procedures performed among 22 patients. The indication for ovarian cryopreservation, the time to restore and duration of endocrine function (regular menstrual cycles), time to first pregnancy and number of pregnancies/live births have been prospectively collected. The operative technique (laparoscopic/robot), duration of surgical procedure, hospital stay, intra and post-operative complications have been retrospectively extracted from the patients’ files. Main results and the role of chance We included 22 patients: 8 with a malignant hematologic disease, 7 with a breast cancer, 3 with a benign disease, 2 with a pelvic malignancy and 2 with a sarcoma. Their median age at ovarian tissue cryopreservation was 28.5 (11 – 35) years. Their median age at first ACOTT was 36.5 (23 – 42) years. The median time between cryopreservation and ACOTT was 6.75 (2 – 21) years. Seven out of 22 patients had a second ACOTT. Twenty-nine procedures of orthotopic reimplantation were performed. A concomitant heterotopic (sub cutaneous) transplantation was performed among six out of the 29 procedures, at the beginning of our experience. All 29 orthotopic reimplantation were performed laparoscopically, 19 out of them with robot assistance. The median operating time was 102 (43 – 149) minutes and was shorter for laparoscopy (62 minutes) than for robot assisted laparoscopy (106 minutes). We encountered no complication, and the median hospital stay was 1 (1 – 2) day. The median time to restore endocrine function was 4 (1 – 6) months. Eighteen transplanted patients wished to be pregnant among whom 12 (67%) had at least one pregnancy and 8 (44%) had at least one live birth. Limitations, reasons for caution This is a small sample within a single institution. The patients were referred from many centers for ovarian tissue cryopreservation and secondary ACOTT. They were followed until they became pregnant and then referred to their gynecologist. Finally, three patients who did not wish to conceive were lost of follow-up. Wider implications of the findings This case series confirms the feasibility and safety of robot assisted ACOTT. The procedure is longer when performed with the robot. The fertility outcome (pregnancy/livebirth rates) is comparable with the published data. The added value of the robot approach needs to be investigated in larger series with long term follow-up. Trial registration number P2004/122

Some aspects regarding the selection of industrial robots in manufacturing or assembly processes

The current period involves a short product life, associated with frequent changes in the product range. In order to cope with the new world economic conditions, a high degree of automation of production and assembly processes is required. As a result, industrial robots must also meet a new level of flexibility and versatility. It is now clear that it is very important to evaluate the performance as well as the safety characteristics, but especially the compatibility with the existing production system, as well as the compatibility with the possible evolution of the production system or even the substantial modification of the tasks of the industrial robot in a new production system, totally modified from the previous one. This requires the formulation of evaluation methods with relevant design metrics and quantitative methods based on simulations, which can support the mechanical designer of the robot, but especially the production system manager, to correlate the load-based performance characteristics, compatibility and safety of the industrial robot, taking into account the wide variety of possible solutions.

Improved Analytic Expansions in Hybrid A-Star Path Planning for Non-Holonomic Robots

In this study, we concisely investigate two phases in the hybrid A-star algorithm for non-holonomic robots: the forward search phase and analytic expansion phase. The forward search phase considers the kinematics of the robot model in order to plan continuous motion of the robot in discrete grid maps. Reeds-Shepp (RS) curve in the analytic expansion phase augments the accuracy and the speed of the algorithm. However, RS curves are often produced close to obstacles, especially at corners. Consequently, the robot may collide with obstacles through the process of movement at these corners because of the measurement errors or errors of motor controllers. Therefore, we propose an improved RS method to eventually improve the hybrid A-star algorithm’s performance in terms of safety for robots to move in indoor environments. The advantage of the proposed method is that the non-holonomic robot has multiple options of curvature or turning radius to move safer on pathways. To select a safer route among multiple routes to a goal configuration, we introduce a cost function to evaluate the cost of risk of robot collision, and the cost of movement of the robot along the route. In addition, generated paths by the forward search phase always consist of unnecessary turning points. To overcome this issue, we present a fine-tuning of motion primitive in the forward search phase to make the route smoother without using complex path smoothing techniques. In the end, the effectiveness of the improved method is verified via its performance in simulations using benchmark maps where cost of risk of collision and number of turning points are reduced by up to around 20%.

Exploring care robots' cybersecurity threats from care robotics specialists’ point of view

Care robots can perform tasks related to physical or mental care; assisting in daily tasks or rehabilitation, independently or semi-automatically. Care robots are exploitable in home-care, nursing homes, or other care facilities. Care robots have the potential to solve several challenges related to aging people. However, care robots suffer have similar cybersecurity problems as other information and communication technology (ICT) devices. In addition, the cybersecurity threats of care robots have been studied less than those of industrial robots. This study’s purpose is to map cybersecurity threats related to care robots from the perspective of care robotics specialists. The study consists of thematic interviews of six purposive-selected specialists in care robotics. A semi-structured thematic interview guide based on the literature view of previous studies, facilitates the conversations at the interviews. All interviews were transcribed verbatim, analyzed by deductive content analysis, and the remaining material was analyzed by inductive content analysis. According to the interviewed specialists, care robots’ cybersecurity threats are associated with the same risks and threats as the use of other ICT devices or robots. Most potential threats are considered to be remote access of care robots, spying, and eavesdropping. Network connectivity is seen as the main interface to the realization of cybersecurity threats in care robotics. New features such as artificial intelligence and machine learning are considered to create more opportunities for new threats. Experts also highlight the underlying human factors behind cybercrime. According to the results, more studies exploring the motives for cybercrime against care robots and the potential benefits derived from it are needed to determine the likelihood of the realization of threats to care robots are needed. Cybersecurity is a race against cybercrime and finding a balance between significant and acceptable risks. In the future, a service ecosystem should be developed which guarantees the safety of care robots throughout their life-cycle: during the design and development phase, deployment and user guidance, maintenance, and reuse of the robot. Additionally, it is important to take into account how new robust operating models can withstand failures and how critical services can be secured in the event of a cybersecurity threat.

Sliding-Mode Nonlinear Predictive Control of Brain-Controlled Mobile Robots.

In this article, we develop a robust sliding-mode nonlinear predictive controller for brain-controlled robots with enhanced performance, safety, and robustness. First, the kinematics and dynamics of a mobile robot are built. After that, the proposed controller is developed by cascading a predictive controller and a smooth sliding-mode controller. The predictive controller integrates the human intention tracking with safety guarantee objectives into an optimization problem to minimize the invasion to human intention while maintaining robot safety. The smooth sliding-mode controller is designed to achieve robust desired velocity tracking. The results of human-in-the-loop simulation and robotic experiments both show the efficacy and robust performance of the proposed controller. This work provides an enabling design to enhance the future research and development of brain-controlled robots.

Improvement and Survey of Fog Computing Using Encryption

This research develops a fog computing-based encrypted manipulate machine in a practical industrial placing. The evolved system conceals controller profits and signals over communique links the usage of multiplicative holomorphic encryption to prevent eavesdropping assaults. Experimental validation confirms the feasibility of function servo manipulate for the motor driven degree with the developed machine in phrases of overall performance degradation, parameter version, and processing time. The evolved machine inherits its stability no matter whether plant parameters range or no longer even after the controller profits and indicators are encrypted. Furthermore, despite the fact that processing time becomes longer by way of increasing a key length of encryption, degradation of control performance is advanced simultaneously. Keywords: Networked robots, robot safety, motion control, encryption control, fog computing.

Nonlinear Predictive Motion Control for Autonomous Mobile Robots Considering Active Fault-Tolerant Control and Regenerative Braking.

To further advance the performance and safety of autonomous mobile robots (AMRs), an integrated chassis control framework is proposed. In the longitudinal motion control module, a velocity-tracking controller was designed with the integrated feedforward and feedback control algorithm. Besides, the nonlinear model predictive control (NMPC) method was applied to the four-wheel steering (4WS) path-tracking controller design. To deal with the failure of key actuators, an active fault-tolerant control (AFTC) algorithm was designed by reallocating the driving or braking torques of the remaining normal actuators, and the weighted least squares (WLS) method was used for torque reallocation. The simulation results show that AMRs can advance driving stability and braking safety in the braking failure condition with the utilization of AFTC and recapture the braking energy during decelerations.

Path planning of patrol robot based on improved discrete electrostatic discharge algorithm

Safety is the premise of the stable and sustainable development of the chemical industry, safety accidents will not only cause casualties and economic losses, but also cause panic among workers and nearby residents. Robot safety inspection based on the fire risk level in a chemical industrial park can effectively reduce process accident losses and can even prevent accidents. The optimal inspection path is an important support for patrol efficiency, therefore, in this study, the fire risk level of each location to be inspected, which is obtained by the electrostatic discharge algorithm (ESDA)–nonparallel support vector machine evaluation model, is combined with the optimisation of the inspection path; that is, the fire risk level is used to guide the inspection path planning. The inspection path planning problem is a typical travelling salesman problem (TSP). The discrete ESDA (DESDA), based on the ESDA, is proposed. In view of the shortcomings of the long convergence time and ease of falling into the local optimum of the DESDA, further improvements are proposed in the form of the IDESDA, in which the greedy algorithm is used for the initial population, the 2-opt algorithm is applied to generate new solutions, and the elite set is joined to provide the best segment for jumping out of the local optimum. In the experiments, 11 public calculation examples were used to verify the algorithm performance. The IDESDA exhibited higher accuracy and better stability when solving the TSP. Its application to chemical industrial parks can effectively solve the path optimisation problem of patrol robots.

Research focus for construction robotics and human-robot teams towards resilience in construction: scientometric review

PurposeRapid urbanisation and recent shock events have reiterated the need for resilient infrastructure, as seen in the pandemic. Yet, knowledge gaps in construction robotics and human–robot teams (HRTs) research limit maximising these emerging technologies’ potentials. This paper aims to review the state of the art of research in this area to identify future research directions in HRTs able to aid the resilience and responsiveness of the architecture, engineering and construction (AEC) sector.Design/methodology/approachA total of 71 peer-reviewed journal articles centred on robotics and HRTs were reviewed through a quantitative approach using scientometric techniques using Gephi and VOSviewer. Research focus deductions were made through bibliometric analysis and co-occurrence analysis of reviewed publications.FindingsThis study revealed sparse and small research output in this area, indicating immense research potential. Existing clusters signifying the need for further studies are on automation in construction, human–robot teaming, safety in robotics and robotic designs. Key publication outlets and construction robotics contribution towards the built environment’s resilience are discussed.Practical implicationsThe identified gaps in the thematic areas illustrate priorities for future research focus. It raises awareness on human factors in collaborative robots and potential design needs for construction resilience.Originality/valueRapid urbanisation and recent shock events have reiterated the need for resilient infrastructure, as seen in the pandemic. Yet, knowledge gaps in construction robotics and HRTs research limit maximising these emerging technologies’ potentials. This paper aims to review the state of the art of research in this area to identify future research directions in HRTs able to aid the resilience and responsiveness of the AEC sector.

3D Printing of Capacitor Using Ionic Liquid Gel

In recent years, there has been a growing demand for safe soft robots that work in a work environment similar to that of humans. The use of 3D modeling technology in the manufacturing of soft robot chassis, soft electronic parts, and wiring processes is expected to lead to more efficient and smaller soft robot manufacturing. The objective of this study is to fabricate a gel capacitor using ionic liquid as an electrolyte, which has characteristics such as non-volatility, flame retardance, and conductivity. We fabricate ionic liquid gels that possess flexibility and conductivity after curing using a photoformable 3D gel printer, and discuss their performance as capacitors.

Designing Expandable-Structure Robots for Human-Robot Interaction.

In this paper, we survey the emerging design space of expandable structures in robotics, with a focus on how such structures may improve human-robot interactions. We detail various implementation considerations for researchers seeking to integrate such structures in their own work and describe how expandable structures may lead to novel forms of interaction for a variety of different robots and applications, including structures that enable robots to alter their form to augment or gain entirely new capabilities, such as enhancing manipulation or navigation, structures that improve robot safety, structures that enable new forms of communication, and structures for robot swarms that enable the swarm to change shape both individually and collectively. To illustrate how these considerations may be operationalized, we also present three case studies from our own research in expandable structure robots, sharing our design process and our findings regarding how such structures enable robots to produce novel behaviors that may capture human attention, convey information, mimic emotion, and provide new types of dynamic affordances.

An intelligent manufacturing cell based on human–robot collaboration of frequent task learning for flexible manufacturing

The trend of short-run production and personalized customization is more and more popular in the manufacturing industry. And the robots in these production lines must conduct task adjustment efficiently when learning new tasks. Thus, this paper developed the intelligent manufacturing cell based on the human–robot collaboration (HRC-IMC) which can enhance the learning ability of cobots by introducing the intelligence of human. The HRC-IMC was composed with four modules: the imitating learning module, the human–robot safety planning module, the task planning module and the visual inferring module. All of the four modules were designed to provide a set of systematic and effective methods. That was conductive to the efficiency improvement of the task adjustment for cobots’ new task learning. The experimental results indicated that the efficiency of task adjustment can be increased by 42.8 % when the HRC-IMC was employed than that of Moveit. All in all, this study is of great significance for improving the efficiency of new task adjustment of cobots by imitating the manipulation experience of human via combining four algorithm modules.

Robotic Exploration for Learning Human Motion Patterns

Understanding how people are likely to move is key to efficient and safe robot navigation in human environments. However, mobile robots can only observe a fraction of the environment at a time, while the activity patterns of people may also change at different times. This article introduces a new methodology for mobile robot exploration to maximize the knowledge of human activity patterns by deciding where and when to collect observations. We introduce an exploration policy driven by the entropy levels in a spatio-temporal map of pedestrian flows, and compare multiple spatio-temporal exploration strategies including both informed and uninformed approaches. The evaluation is performed by simulating mobile robot exploration using real sensory data from three long-term pedestrian datasets. The results show that for certain scenarios the models built with proposed exploration system can better predict the flow patterns than uninformed strategies, allowing the robot to move in a more socially compliant way, and that the exploration ratio is a key factor when it comes to the model prediction accuracy.

SHARP: Shielding-Aware Robust Planning for Safe and Efficient Human-Robot Interaction

Jointly achieving safety and efficiency in human-robot interaction settings is a challenging problem, as the robot’s planning objectives may be at odds with the human’s own intent and expectations. Recent approaches ensure safe robot operation in uncertain environments through a supervisory control scheme, sometimes called “shielding,” which overrides the robot’s nominal plan with a safety fallback strategy when a safety-critical event is imminent. These reactive “last-resort” strategies (typically in the form of aggressive emergency maneuvers) focus on preserving safety without efficiency considerations; when the nominal planner is unaware of possible safety overrides, shielding can be activated more frequently than necessary, leading to degraded performance. In this letter, we propose a new shielding-based planning approach that allows the robot to plan efficiently by explicitly accounting for possible future shielding events. Leveraging recent work on Bayesian human motion prediction, the resulting robot policy proactively balances nominal performance with the risk of high-cost emergency maneuvers triggered by low-probability human behaviors. We formalize Shielding-Aware Robust Planning (SHARP) as a stochastic optimal control problem and propose a computationally efficient framework for finding tractable approximate solutions at runtime. Our method outperforms the shielding-agnostic motion planning baseline (equipped with the same human intent inference scheme) on simulated driving examples with human trajectories taken from the recently released Waymo Open Motion Dataset.

Control Evaluation of Antagonistic Series Elastic Actuation for a Robotic Endoscope Joint

Haptic feedback is typically missing during telemanipulation of surgical robots in minimally invasive surgeries, i.e., surgeons cannot feel the interaction forces between the instruments and tissues. Instead, surgeons have to solely rely on visual feedback, which increases complexity of guiding the instruments and poses the safety threat of unperceivable contacts outside the field of view. We propose a novel series elastic actuation design for articulated robotic endoscopes to overcome these limitations and evaluate an according device with one joint. Similar to the actuation of human fingers, the joint is driven by antagonistic tendons. Springs are integrated in the transmission between the tendons and the motors outside of the endoscope shaft. We estimated the joint angle and thereby the endoscope shape, measured spring deflection, estimated tendon forces from that deflection, and implemented force control for the endoscope joint. Zero torque control and impedance control were evaluated under application of both a continuous force and an impact force to the endoscope tip. The springs reduced impact forces at the tip of the endoscope through their inherent compliance. At the same time, feeding back the estimated force resulted in a stable tendon force control and a tunable endoscope joint control: Zero torque control effectively reduced the external forces, while the endoscope joint showed the expected stiffness in impedance control. These results show that antagonistic series elastic actuation is a promising concept for endoscope joint actuation and that it can lead towards safer robot–tissue interactions in surgical robotics.

Specifying Inputs for the Computational Structure of a Surgical System via Optical Method and DLT Algorithm Based on In Vitro Experiments on Cardiovascular Tissue in Minimally Invasive and Robotic Surgery.

With the application of four optical CMOS sensors, it was possible to capture the trajectory of an endoscopic tool during an in vitro surgical experiment on a cardiovascular preparation. This was due to the possibility of obtaining a path when a reflective marker was attached. In the work, APAS (Ariel Performance Analysis System) software and DLT (direct linear transformation) algorithm were used. This made it possible to acquire kinematic inputs to the computational model of dynamics, which enabled, regardless of the type of surgical robot structure, derivation of the analogous motion of an endoscopic effector due to the mathematical transformation of the trajectory to joints coordinates. Experiments were carried out with the participation of a practiced cardiac surgeon employing classic endoscopic instruments and robot surgical systems. The results indicated by the experiment showed that the inverse task of kinematics of position for the surgical robot with RCM (remote center of motion) structure was solved. The achieved results from the experiment were used as inputs for deriving a numerical dynamics model of surgical robot during transient states that was obtained by applying the finite element method and by driving dynamics moments acquired through the block diagrams method using a steering system with DC (direct current) motor and PID (proportional–integral–derivative) controller. The results section illustrates the course of kinematic values of endoscopic tools which were employed to apply numerical models as inputs, the course of the driving torque of the model of the surgical robot that enabled the selection of the drive system and the strength values, stresses and displacements according to von Mises hypothesis in its structure during the analysis of transient states that made it possible to establish the strength safety of the surgical robot. For the conducted experiments, the accuracy was ±2 [mm]. In the paper, the employment of optical CMOS sensors in surgical robotics and endoscopy is discussed. The paper concludes that the usage of optical sensors for determining inputs for numerical models of dynamics of surgical robots provides the basis for setting the course of physical quantities that appear in their real object structure, in manners close to reality.