Robot Failures Research Articles

Moving-Distance-Minimized PSO for Mobile Robot Swarm.

Particle swarm optimizer (PSO) and mobile robot swarm are two typical swarm techniques. Many applications emerge separately along both of them while the similarity between them is rarely considered. When a solution space is a certain region in reality, a robot swarm can replace a particle swarm to explore the optimal solution by performing PSO. In this way, a mobile robot swarm should be able to efficiently explore an area just like the particle swarm and uninterruptedly work even under the shortage of robots or in the case of unexpected failure of robots. Furthermore, the moving distances of robots are highly constrained because energy and time can be costly. Inspired by such requirements, this article proposes a moving-distance-minimized PSO (MPSO) for a mobile robot swarm to minimize the total moving distance of its robots while performing optimization. The distances between the current robot positions and the particle ones in the next generation are utilized to derive paths for robots such that the total distance that robots move is minimized, hence minimizing the energy and time for a robot swarm to locate the optima. Experiments on 28 CEC2013 benchmark functions show the advantage of the proposed method over the standard PSO. By adopting the given algorithm, the moving distance can be reduced by more than 66% and the makespan can be reduced by nearly 70% while offering the same optimization effects.

A multi-objective optimization design of industrial robot arms

This paper introduces a multi-objective design mechanism to minimize both the initial and running costs of industrial robot arms. The goal of the design problem is to decide the type of material and physical dimensions of the robot arm to withstand high loads at vulnerable locations using stress analysis. Additionally, it selects the material architecture for the robot links based on a vibration analysis to avoid robot failure at or close to the resonance frequency. Hence, a set of design equations based on stress analysis are developed using analytical approaches while the findings are supported by finite element simulations in ANSYS. These decide the type of material, cross-section area, factor of safety (FoS), and maximum deflection of the robot links in terms of the mass-loads. Moreover, the vibration analysis is conducted to enhance the dynamic characteristics of the robot arm. Therefore, the excitation frequency is modified by changing the mass and the robot segment-material to evade working at the natural frequency. Modal analysis is conducted using ANSYS to identify the fundamental frequencies and their modal shapes. Then, a material selection mechanism based on finite element analysis is considered to allow a safe frequency operation range for the robot arm. A customized robot arm structure that combines Magnesium and Aluminum alloy with highly improved FoS and minimizes the initial and operating costs is proposed. In addition to the body structure of the robot arm, the influence of the reducers and motors on the stiffness and vibration of the robot arm is presented. Finally, the motion of the robot arm is optimized using a Genetic Algorithm subject to a set of boundary conditions imposed by the desired mission. The effect of rotation angle value in the power consumption is presented. The coefficients of a developed angular displacement function are optimized to ensure minimum power consumption during the robot missions.

Blokzincir Destekli Mobil Robotların Sağlık Hizmetinde Uygulanması: Kullanım Örnekleri, Araştırma Zorlukları ve Gelecek Trendler

Using service robots in healthcare is gaining importance in case of emergent situations like pandemics where human labour is considered risky. Multi robot systems of mobile robots have the potential to perform simple but vital tasks in healthcare. However, centralized control with a server computer of these systems carry the risks of single point of failure and ineffective operation of robots, thus decentralized control with blockchain integration offers a better solution. We mention research challenges regarding blockchain powered multi robot systems of mobile robots from use case, blockchain technology and its integration into current computing systems used in medical centers aspects. Then we propose a method for decentralized management and task distribution in a multi robot system by using Hyperledger Fabric as a permissioned blockchain platform and give common use case scenarios. In this system, tasks are assigned to robots depending on the selection of nearest available robots to the task target. Each robot runs the smart contract containing the task assignment method, so that data traffic for the task assignment process is distributed among the network, instead of stacking up on a single line as in a centralized system. Future research issues and directions for future works are also stated as a conclusion.

Exploring the effect of humor in robot failure

Service robots are playing an increasing role in tourism and hospitality. However, this advanced technology is not immune to mistakes. The current research focuses on whether humor could be an effective recovery method in robot failure. Drawing on benign violation theory and regulatory focus theory, two experiments are conducted to demonstrate that, in low-severity failure conditions, humorous responses lead to higher humor appreciation and more positive customer evaluations. However, in high-severity failure conditions, humorous responses lead to higher perceived insincerity and more negative customer evaluations. We also examine the moderating role of anthropomorphism. This paper identifies whether and when humor is appropriate in robot failure and elucidates the introduction and promotion of robot services for managers.

Functional Safety Analysis and Design of Sensors in Robot Joint Drive System

The reliable operation of the sensors of robot joint drive systems (RJDs) is a key factor in ensuring the safety of equipment and personnel. Over the years, additional safety-related systems have been designed to prevent safety incidents caused by robot failures, ignoring the functional safety issues of the robot sensors themselves. In view of this, based on IEC61508, a functional safety analysis and design method for sensors of RJDs is proposed in this paper. Firstly, the hazard analysis and risk assessment clarified the goals that the safety protection function of the RJD’s sensor should achieve. Then, by establishing the motor drive model and transmission model, a model-based sensor fault diagnosis and isolation strategy is proposed. Considering the fault-tolerant operation of system, a fail-operational hardware architecture of the safety-related system is designed. Markov analysis shows that the safety integrity level (SIL) of safety-related systems can reach SIL3. Finally, experiments are designed to validate the proposed fault diagnosis and fault tolerance strategy. The results show that the safety-related system can effectively locate sensor failures, realize fault-tolerant control when a single sensor fails and perform safe torque off (STO) protection when multiple sensors fail.

A Dynamic Leader–Follower Approach for Line Marching of Swarm Robots

Most of the existing exact formation algorithms for swarm systems are fully label-specified, i.e. the desired position for each individual in the formation is predetermined by its label, which would inevitably make the formation algorithms vulnerable to individual failures. To address this issue, in this paper, we propose a dynamic leader–follower approach to solving the line marching problem for a swarm of planar kinematic robots. In contrast to the existing results, the desired positions for the robots in the line are no longer predetermined by the robots’ labels, but the relativity of the robots’ labels as well as their real-time relative positions. By constantly forming the leader–follower chain, exact line marching can be realized by pairwise leader–following tracking. Since the order of the leader–follower chain keeps updating, the proposed algorithm shows strong robustness against robot failures. Comprehensive numerical results are provided to evaluate the performance of the proposed formation algorithm.

Predictive Maintenance System for Wafer Transport Robot Using K-Means Algorithm and Neural Network Model

Maintenance is the technology of continuously monitoring the conditions of equipment and predicting the timing of maintenance for equipment. Particularly in the field of semiconductor manufacturing, where processes are automated, various methods are being tried to minimize the economic losses and maintenance costs caused by equipment failure. A new Predictive Maintenance (PdM) technique, a new method of maintenance, is introduced in this paper to develop an algorithm for predicting the failure of wafer transfer robots in advance. The acceleration sensor data used in the experiment were obtained by installing a sensor onto the wafer transfer robot. To analyze these data, the data preprocessing and FFT process were performed. These data were divided into normal data, first error data, second error data, and third error data (failure data) in stages. By clustering the data using the K-means algorithm, the center point distribution of the clusters was analyzed, and the features of the error data and normal data were extracted. Using these features, an artificial neural network model was designed to predict the point of failure of the robot. Previous research on maintenance systems of the transfer robot used fewer than 50 error data, but 1686 error data were used in this experiment. The reliability of the model is improved by randomly selecting data from a total of 2248 data sets. In addition, it was confirmed that it was possible to classify normal data and error data with an accuracy of 97% and to predict equipment failure by applying neural network modeling.

A practical and synchronized data acquisition network architecture for industrial robot predictive maintenance in manufacturing assembly lines

This manuscript presents a methodology and a practical implementation of a network architecture for industrial robot data acquisition and predictive maintenance . We propose a non-intrusive and scalable robot signal extraction architecture, easily applicable in real manufacturing assembly lines. The novelty of the paper lies in the fact that it is the first proposal of a network architecture which is specially designed to address the predictive maintenance of industrial robots in real production environments. All the infrastructure needed for the implementation of the architecture is comprised of traditional well-known industrial assets. We synchronize the data acquisition with the execution of robot routines using common Programmable Logic Controllers (PLC) to obtain comparable data batches. A network architecture that acquires comparable and structured data over time, is a crucial step to advance towards an effective predictive maintenance of these complex systems, in terms of effectively detecting time dependent degradation. We implement the architecture in a real automotive manufacturing assembly line and show the potential of the solution to detect robot joint failures in real world scenarios. The architecture is therefore specially interesting for industrial practitioners and maintenance personnel. Finally, we test the feasibility of using one-class novelty detection models for robot health status degradation assessment using data of a real robot failure. To the best of our knowledge, this is the first contribution that uses robot torque signals of a real production line failure to train one-class models. • A non-intrusive and scalable robot data acquisition architecture is proposed for real manufacturing assembly lines. • The network acquires synchronized comparable robot operational data over time. • The architecture is able to detect a significant deviation in a faulty ABB IRB 6400r robot data. • One-class novelty detection models detect deviations in real operational conditions.

316 Optimizing Safety in Robotic Lumbar Instrumented Fusions: A Risk Factor Analysis of Robotic Failures

INTRODUCTION: There may be early complications with robot-guided lumbar fusions (RGLF). This study determines risk factors for robot-related complications and suggests optimal OR workflow. METHODS: Single-institution retrospective review of 525 RGLFs. Univariate analysis (independent samples t-tests, chi-squared analysis) assessed demographics, Hounsfield units (HU), procedural characteristics, and OR workflow as risk factors for robot registration failures and pedicle screw malpositioning. Multivariate logistic regression of significant factors was conducted. Subanalysis of Anterior Lumbar Interbody Fusion (ALIF) with RGLF was conducted. Receiver operating characteristic (ROC) curve determined thresholds for independent risk factors. RESULTS: All RGLF: Lower L1-HU and L5-S1 inclusion were significant risk factors for registration failure on univariate analysis. Placing pedicle screws directly after CT acquisition (PS after CT) and before interbody placement showed fewer registration failures. On multivariate analysis, L1-HU (Odds Ratio (OR): 1.012; p = 0.006), L5-S1 inclusion (OR: 2.795; p = 0.033) were independent risk factors. PS after CT improved registration (OR: 0.145; p = 0.004). Age and female gender were risk factors for pedicle screw malpositioning on univariate analysis. PS after CT showed fewer pedicle screw malpositions on univariate and multivariate (OR: 0.146; p = 0.006) analysis. ALIF with RGLF: PS after CT had fewer registration failures. Low L1-HU units was a risk for registration failure on univariate and multivariate (OR: 1.022; p = 0.001) analysis. ROC analysis of L1-HU showed a 145.85 cutoff. Age and hyperlordotic interbody use were risk factors for pedicle screw malposition on univariate analysis. CONCLUSION: Early robot-related complications may be avoided by recognizing risks. PS after CT, before interbody placement may avoid complications including pedicle screw malpositioning. Poor bone quality can lead to registration failures.

Dissecting liabilities in adversarial surgical robot failures: A national (Danish) and EU law perspective

Over the last decade, surgical robots have risen in prominence and usage. They are not merely tools, but have also become advanced instruments with network connectivity. Connectivity is necessary to accept software updates, accept instructions, and transfer sensory data, but it also exposes the robot to cyberattacks, which can damage the patient or the surgeon. These injuries are normally caused by safety failures, as seen in accidents with industrial robots, but cyberattacks are caused by security failures instead. We create a taxonomy for both types of failures in this paper specifically for surgical robots. These robots are increasingly sold and used in the European Union (EU), hence it is natural to consider how surgical robots are viewed and treated by EU law. Specifically, which rights regulators and manufacturers have under it, and which legal remedies and actions a patient or manufacturer would have in a single national legal system in the union, if injuries were to occur from a security failure caused by an adversary that cannot be unambiguously identified (attribution of cyberattacks is often hard). Given that the Medical Device Regulation (MDR) has only recently entered into force, we also offer some general considerations of the regulation. We find that the selected (Danish) national legal system can adequately deal with attacks on surgical robots, because it can on one hand efficiently compensate the patient, and at the same time protect the patient by not shying away from dealing with the problem concretely. This is because of its flexibility; secondly, a remarkable absence of distinction between safety vs security causes of failure and focusing instead on the detrimental effects, thus benefiting the patient; and third, liability can be removed from the manufacturer by withdrawing its status as party, if the patient chooses a separate public law measure to recover damages. Furthermore, we find that current EU law does consider both security and safety aspects of surgical robots, without it mentioning it through literal wording, but it also adds substantial liabilities and responsibilities to the manufacturers of surgical robots, gives the patient special rights and confers immense powers on the regulators, which can end up affecting any future lawsuits.

MPPTM: A Bio-Inspired Approach for Online Path Planning and High-Accuracy Tracking of UAVs.

The path planning and tracking problem of the multi-robot system (MRS) has always been a research hotspot and applied in various fields. In this article, a novel multi-robot path planning and tracking model (MPPTM) is proposed, which can carry out online path planning and tracking problem for multiple mobile robots. It considers many issues during this process, such as collision avoidance, and robot failure. The proposed approach consists of three parts: a neural dynamic path planner, a hyperbolic tangent path optimizer, and an error-driven path tracker. Assisted by Ultra-wideband positioning system, the proposed MPPTM is a low-cost solution for online path planning and high-accurate tracking of MRS in practical environments. In the proposed MPPTM, the proposed path planner has good time performance, and the proposed path optimizer improves tracking accuracy. The effectiveness, feasibility, and better performance of the proposed model are demonstrated by real experiments and comparative simulations.

Evaluation of Robot Degradation on Human-Robot Collaborative Performance in Manufacturing.

Human-robot collaborative systems are highly sought candidates for smart manufacturing applications because of their adaptability and consistency in production tasks. However, manufacturers are still hesitant to adopt these systems because of the lack of metrics regarding the influence of the degradation of collaborative industrial robots on human-robot teaming performance. Hence, this paper defines teaming performance metrics with respect to robot degradation. In addition, the defined metrics are applied to a human-robot collaborative inverse peg-in-hole case study with respect to the degradation of the joint angular encoder and current sensor. Specifically, this case study compares pure insertion versus insertion with spatial scanning to solve the peg-in-hole problem, and manual intervention is implemented in the event of robotic failure. The metrics used in the case study showed that pure insertion more sensitive to robot degradation with manual intervention was required at 0.04° as opposed to 0.12° from insertion with scanning. Therefore, insertion with scanning was shown to be more robust to robot degradation at the cost of a slower insertion time of 9.48 s compared to 3.19 s. Thus, this paper provides knowledge and usable metrics regarding the influence of robot degradation on human-robot collaborative systems in manufacturing applications.

Determining the cause of incorrect work of bumper paint robot reducer

Industrial robots are used for high-precision tasks and in intensive production. One of the most reliable methods for monitoring robots’ work is vibrodiagnostics, which provides recommendations and maintenance procedures, and also serves as a reliable diagnostic method for determining the causes of industrial robot failures. Connection of robot and the supporting structure is very important and it affects on dynamic behavior of the robot. This paper presents the process of diagnosing dynamic condition of the structure with painting robots, after the failure of the robot motion reducer. To analyze the causes of the failure, a numerical-experimental analysis was performed, which included: static calculation, dynamic calculation, determining transfer function in frequency domain and reinforcement factors and measuring accelerations. Supporting structure has very low eigen-frequencies (3,5 to 9.5 Hz), it cannot accept inertial forces and large deformations and resonant behavior occur. Using FEM analysis is shown that the behavior of the structure is unfavorable because 20 eigen-oscillations had a frequency less than 17 Hz, and all are located in the robots’ working space. Determination of the transfer functions showed that the dynamic gain factor in all three directions of the excitation force is extremely large, while the corresponding frequencies are 5 ÷ 20 Hz. As a part of the experimental analysis, accelerations and displacements at the robots’ locations were measured. Accelerations exceed the standard acceptable limit. It was determined that the main problem was the rigidity of the supporting structure and maintenance omissions observed during the visual inspection. Only a partial and relatively quick repair was done on the structure, and it significantly decreased displacements of most critical zones (robots).

Perspective on robotic spine surgery: Who's doing the thinking?

Background: Robotic assisted (RA) spine surgery was developed to reduce the morbidity for misplaced thoracolumbar (TL) pedicle screws (PS) resulting in neurovascular injuries, dural fistulas, and/or visceral/other injuries. RA is gaining the attention of spine surgeons to optimize the placement of TL PSs, and to do this more safely/effectively versus utilizing stereotactic navigation alone, or predominantly free hand (FH) techniques. However, little attention is being focused on whether a significant number of these TL RA instrumented fusions are necessary.Methods: RA spine surgery has been developed to improve the safety, efficacy, and accuracy of minimally invasive TL versus open FH PS placement.Results: Theoretical benefits of RA spine surgery include; enhanced accuracy of screw placement, fewer complications, less radiation exposure, smaller incisions, to minimize blood loss, reduce infection rates, shorten operative times, reduce postoperative recovery periods, and shorten lengths of stay. Cons of RA include; increased cost, increased morbidity with steep learning curves, robotic failures of registration, more soft tissue injuries, lateral skiving of drill guides, displacement of robotic arms impacting accurate PS placement, higher reoperation rates, and potential loss of accuracy with motion versus FH techniques. Notably, insufficient attention has been focused on the necessity for performing many of these TL PS instrumented fusions in the first place.Conclusion: RA spinal surgery is still in its infancy, and comparison of RA versus FH techniques for TL PS placement demonstrates several potential pros, but also multiple cons. Further, more attention must be focused on whether many of these TL PS instrumented procedures are even warranted.

39. Optimizing safety in robotic lumbar instrumented fusions: a risk factor analysis of robotic failures

39. Optimizing safety in robotic lumbar instrumented fusions: a risk factor analysis of robotic failures

Safe and efficient imitation learning by clarification of experienced latent space

Behavioral cloning from observation (BCO) allows the robot to learn the policy without the expert's action information. However, it requires a few interactions with the environment to infer expert's action with risk of robot failures. In addition, BCO assumes that the inferred action is of accurate, causing wrong and inefficient updates of the policy. Both problems can be resolved by outlier detection whether the faced state is experienced or not. This paper addresses such outlier detection mechanisms using variational autoencoder (VAE) to improve safety and efficiency of the standard BCO. For the first safety problem, we suppose that the expert's demonstrations only visited the safe states, and then, VAE is learned by the expert's state data to detect inexperienced and dangerous scenes. For the second efficiency problem, another VAE is trained with the state data safely collected by the imitator's policy to detect the scenes where the inferred actions are not accurate. In handwriting robot experiments, the proposed mechanisms succeeded in improving the standard BCO in terms of both the safety (roughly 64%) and the efficiency (roughly 44%). The high versatility of the proposed mechanisms is verified from learning various alphabets.

A MULTI-AGENT REINFORCEMENT LEARNING FRAMEWORK FOR INTELLIGENT MANUFACTURING WITH AUTONOMOUS MOBILE ROBOTS

AbstractIntelligent manufacturing (IM) embraces Industry 4.0 design principles to advance autonomy and increase manufacturing efficiency. However, many IM systems are created ad hoc, which limits the potential for generalizable design principles and operational guidelines. This work offers a standardizing framework for integrated job scheduling and navigation control in an autonomous mobile robot driven shop floor, an increasingly common IM paradigm. We specifically propose a multi-agent framework involving mobile robots, machines, humans. Like any cyberphysical system, the performance of IM systems is influenced by the construction of the underlying software platforms and the choice of the constituent algorithms. In this work, we demonstrate the use of reinforcement learning on a sub-system of the proposed framework and test its effectiveness in a dynamic scenario. The case study demonstrates collaboration amongst robots to maximize throughput and safety on the shop floor. Moreover, we observe nuanced behavior, including the ability to autonomously compensate for processing delays, and machine and robot failures in real time.

Expect the Unexpected: Leveraging the Human-Robot Ecosystem to Handle Unexpected Robot Failures.

Unexpected robot failures are inevitable. We propose to leverage socio-technical relations within the human-robot ecosystem to support adaptable strategies for handling unexpected failures. The Theory of Graceful Extensibility is used to understand how characteristics of the ecosystem can influence its ability to respond to unexpected events. By expanding our perspective from Human-Robot Interaction to the Human-Robot Ecosystem, adaptable failure-handling strategies are identified, alongside technical, social and organizational arrangements that are needed to support them. We argue that robotics and HRI communities should pursue more holistic approaches to failure-handling, recognizing the need to embrace the unexpected and consider socio-technical relations within the human robot ecosystem when designing failure-handling strategies.

Failure-Resilient Coverage Maximization With Multiple Robots

The task of maximizing coverage using multiple robots has several applications such as surveillance, exploration, and environmental monitoring. A major challenge of deploying such multi-robot systems in a practical scenario is to ensure resilience against robot failures. A recent work (L. Zhou et al., 2019) introduced the Resilient Coverage Maximization (RCM) problem where the goal is to maximize a submodular coverage utility when the robots are subject to adversarial attacks or failures. The RCM problem is known to be NP-hard. In this letter, we propose two approximation algorithms for the RCM problem, namely, the Ordered Greedy (OrG) and the Local Search (LS) algorithm. Both algorithms empirically outperform the state-of-the-art solution in terms of accuracy and running time. To demonstrate the effectiveness of our proposed solution, we empirically compare our proposed algorithms with the existing solution and a brute force optimal algorithm. We also perform a case study on the persistent monitoring problem to show the applicability of our proposed algorithms in a practical setting.

Hierarchical Task Assignment and Path Finding with Limited Communication for Robot Swarms

Complex service robotics scenarios entail unpredictable task appearance both in space and time. This requires robots to continuously relocate and imposes a trade-off between motion costs and efficiency in task execution. In such scenarios, multi-robot systems and even swarms of robots can be exploited to service different areas in parallel. An efficient deployment needs to continuously determine the best allocation according to the actual service needs, while also taking relocation costs into account when such allocation must be modified. For large scale problems, centrally predicting optimal allocations and movement paths for each robot quickly becomes infeasible. Instead, decentralized solutions are needed that allow the robotic system to self-organize and adaptively respond to the task demands. In this paper, we propose a distributed and asynchronous approach to simultaneous task assignment and path planning for robot swarms, which combines a bio-inspired collective decision-making process for the allocation of robots to areas to be serviced, and a search-based path planning approach for the actual routing of robots towards tasks to be executed. Task allocation exploits a hierarchical representation of the workspace, supporting the robot deployment to the areas that mostly require service. We investigate four realistic environments of increasing complexity, where each task requires a robot to reach a location and work for a specific amount of time. The proposed approach improves over two different baseline algorithms in specific settings with statistical significance, while showing consistently good results overall. Moreover, the proposed solution is robust to limited communication and robot failures.