Proposal of a Monitoring System for Collaborative Robots to Predict Outages and to Assess Reliability Factors Exploiting Machine Learning

Industry standards pertaining to Human-Robot Collaboration (HRC) impose strict safety requirements to protect human operators from danger. When a robot is equipped with dangerous tools, moves at a high speed or carries heavy loads, the current safety legislation requires the continuous on-line monitoring of the robot’s speed and a suitable separation distance from human workers. The present paper proposes to make a virtue out of necessity by extending the scope of on-line monitoring to predicting failures and safe stops. This has been done by implementing a platform, based on open access tools and technologies, to monitor the parameters of a robot during the execution of collaborative tasks. An automatic machine learning (ML) tool on the edge of the network can help to perform the on-line predictions of possible outages of collaborative robots, especially as a consequence of human-robot interactions. By exploiting the on-line monitoring system, it is possible to increase the reliability of collaborative work, by eliminating any unplanned downtimes during execution of the tasks, by maximising trust in safe interactions and by increasing the robot’s lifetime. The proposed framework demonstrates a data management technique in industrial robots considered as a physical cyber-system. Using an assembly case study, the parameters of a robot have been collected and fed to an automatic ML model in order to identify the most significant reliability factors and to predict the necessity of safe stops of the robot. Moreover, the data acquired from the case study have been used to monitor the manipulator’ joints; to predict cobot autonomy and to provide predictive maintenance notifications and alerts to the end-users and vendors

Robust Adversarial Reinforcement Learning for Optimal Assembly Sequence Definition in a Cobot Workcell

The fourth industrial (I4.0) revolution encourages automatic online monitoring of all products to achieve zero-defect and high-quality production. In this scenario, collaborative robots, in which humans and robots share the same workspace, are a suitable solution that integrates the precision of a robot with the ability and flexibility of a human. To improve human-robot collaboration, human changeable choices or even non-significant mistakes should be allowed or corrected during work. This paper proposes a robust online optimization of the Dassembly sequence through Robust Adversaria lReinforcement Learning (RARL), where an artificial agent is deliberately trying to boycott the assembly completion. To demonstrate the applicability of robust human-robot collaborative assembly using adversarial RL, an environment composed of Markov Decision Process (MDP) like grid world is developed and a multi-agent RL approach is integrated. The results of the framework are promising: the robot observation on human activities has been successfully achieved thanks to a penalty-reward system adopted and the alternation of human to robot actions for the wrong terminal state is the one pursued by the human, but due to robot blockage wrong actions, the right terminal state is followed by human, which is the same as the robot target

Robust assembly sequence generation in a Human-Robot Collaborative workcell by reinforcement learning

Human-Robot Collaborative (HRC) workcells could enhance the inclusive employment of human workers regardless their force or skills. Collaborative robots not only substitute humans in dangerous and heavy tasks, but also make the related processes within the reach of all workers, overcoming lack of skills and physical limitations. To enable the full exploitation of collaborative robots traditional robot programming must be overcome. Reduction of robot programming time and worker cognitive effort during the job become compelling requirements to be satisfied. Reinforcement learning (RL) plays a core role to allow robot to adapt to a changing and unstructured environment and to human undependable execution of repetitive tasks. The paper focuses on the utilization of RL to allow a robust industrial assembly process in a HRC workcell. The result of the study is a method for the online generation of robot assembly task sequence that adapts to the unpredictable and inconstant behavior of the human co-workers. The method is presented with the help of a benchmark case study

Smartphone and Bluetooth Smart Sensor Usage in IoT Applications

Bluetooth Low Energy is an interesting short-range radio technology that could be used for connecting tiny devices into the Internet of Things (IoT) through gateways or cellular networks. For example, they are widely used in various contexts, from building and home automation to wearables. This paper proposes a method to improve the use of smartphones with a smart wireless sensor network acquisition system through Bluetooth Low Energy (BLE). A new BLE Smart Sensor, which acquires environmental data, was designed and calibration methods were performed. A detailed deviation is calculated between reference sensor and sensor node. The data obtained from laboratory experiments were used to evaluate battery life of the node. An Android application for devices such as Smartphones and Tablets can be used to collect data from a smart sensor, which becomes more accurate

Intelligent energy management for mobile manipulators using machine learning

Integrated robotic systems combining manipulators with mobile robots provide outstanding improvement opportunities for semi-automatic assembly processes leveraged by Industry 4.0. Factory operations are released from the rigid layout constraints imposed by conventional fixed robots. Thus, they introduce new challenges in managing the recharge cycles as the energy consumption of mobile manipulators is not simply related to the travelled distance but to the overall tasks executed. Its estimation requires a systemic approach. In the proposed solution, an intelligent monitoring system is implemented on board. Data gathered online, and Key Performance Indicators (KPIs) calculated during the working tasks are exploited by Machine Learning (ML) to optimize energy recharging cycles. Although the development of an intelligent monitoring framework for a mobile manipulator was the original objective of the research, the monitoring system is exploited here for energy management only, leaving space for other future applications

Prediction and estimation model of energy demand of the AMR with cobot for the designed path in automated logistics systems

Abstract The ecosystem of the Industry 4.0 involves many new technologies, such as autonomous mobile robots (AMR) and cobots (collaborative robots), these are characterized with higher flexibility and cost effectiveness which makes them more suitable for automated internal logistics systems. The evaluation of energy consumption of AMRs for a designed path in a real case scenario using analytical tools are challenging. This paper proposes a method of evaluation of the sustainability of new technologies of Industry 4.0 in internal logistics. The proposed framework demonstrates data management technique of the industrial robots. Since, the AMR with manipulator perform different tasks as a single system in logistics there is big demand to develop model of cyber physical system. During task execution measured robots' physical parameters used as input data to perform analytics. Moreover, acquired data from different condition use cases have been used to monitor the battery behaviour of the AMR and preliminary results of the linear regression model is presented

AUTOMATED ANOMALY MONITORING AND DETECTION SYSTEM FOR FCU SYSTEM

Implementing and integrating new technologies such as the Internet of Things (IoT), smart sensors, and information and communication technology (ICT) into building facilities generates a large amount of data that will be utilized to better manage building facilities specifically FCU. Automated fault detection and diagnostics(AFDD) systems assist facility managers in informing operators to perform scheduled maintenance and visualizing facility anomalies on building information models (BIM). This study proposes a AFDD system for FCU system using an IoT sensors and by visualizing faults in a BIM model. The proposed system shows the data management and anomaly detection and monitoring technique on the BIM. The experiment results demonstrated the framework's competence to detect anomalies in the FCU system. Furthermore, data collected from various simulated conditions of the building facilities was utilized to monitor and detect anomalies in the 3D model of the fan coil. The automated detection FCU anomalies on the BIM model and preliminary results of the system are demonstrated