Car collision avoidance with velocity obstacle approach

The obstacle avoidance maneuver is required for an autonomous vehicle. It is essential to define the system's performance by evaluating the minimum reaction times of the vehicle and analyzing the probability of success of the avoiding operation. This paper presents a collision avoidance algorithm based on the velocity bstacle approach that guarantees collision-free maneuvers. The vehicle is controlled by an optimal feedback control named FLOP, designed to produce the best performance in terms of safety and minimum kinetic collision energy. Dimensionless accident evaluation parameters are proposed to compare different crash scenarios

Enhancing the Quality of Manual Spot Welding through Augmented Reality Assisted Guidance

AbstractManual spot welding loses the comparison with automated spot welding, not because of a higher execution time, but due to an inferior quality of welded points, mostly a low repeatability. It is not a human fault. Human welder is compelled to operate without having at disposal the knowledge of significant process features that are known by the robot: exact position of the welding spot, electric parameters to be adopted for every specific point, quality of the welded spot and, based on it, possible need for repetition of a defective weld.The research shows that, using an augmented reality device, it is possible to display this very data to the human operator, in order to enhance the manual process execution. The adopted device is a tablet mounted on the welding gun. It displays the working area seen by the built-in camera. The image is augmented by the superposition of computer generated images of the welding spots and their properties. The state of the spot (welded or not) and its execution quality (good or defective weld) is transmitted by some graphic features, like point color, size and way of blinking. The paper describes the algorithms used in the development of the program for this application, focusing on the problem of real time localization of the welding gun position. The augmented reality application was actually installed on a experimental station by a welding gun manufacturer and the results of the tests are presented and discussed

combining factory simulation with value stream mapping a critical discussion

Abstract Value Stream Mapping (VSM) is employed for the analysis of manufacturing processes. The VSM analysis leads to improve the process through the reduction of non-value added steps. The optimization is often verified by computer simulation (CS) before actual implementation in the factory. The two approaches imply a different underlying conceptual model of production: a deterministic flow of material against a stochastic queuing network. The authors discuss the critical issues, but show, with the help of an automotive case study, that they could produce positive outcomes if the goals are carefully chosen and if some rules of use are respected

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

Qualification of a Collaborative Human-robot Welding Cell

AbstractThis work is focused on evaluating performance of a collaborative robot welding cell developed in our previous research. Such a cell is based on an interactive cooperation between a human supervisor and a welding robot. This approach to organizing a workstation allows to employ robots even in the case of prototypes or small productions. Research on collaborative robots usually focuses on safety issues and on the programming techniques. Present work deals with a complementary problem crucial to industrial applications: the qualification of the welding cell performance in terms of accuracy, repeatability and dependability.In this application, the human worker is responsible for handling of the parts to be assembled and for teaching the robot. The robot is in charge of actual welding. Teaching is executed by demonstration: the teacher shows the welding trajectories with a pointer observed by a motion capture system. The program is generated automatically and executed by the robot. Robots and humans share the same workspace in different times therefore human risk exposure is minimal.Industrial applications of this or similar technology require that the process reliability and capability be assessed. We describe and analyze error accumulation along the entire data flow from the measurement tool, through the reference system transformations, to the actual representation and execution of the robot program

application of numerical simulation for the estimation of die life after repeated hot forging work cycles

Abstract Die life estimation in hot forging processes is a compelling challenge, due to the number of factors, mainly wear and plastic deformation induced by thermal effects (tempering). The extent of the heating-cooling cycle and the steady state die temperature are known only after hundredth of work cycles. In the paper a realistic work sequence of repeated forging is simulated by the Finite Elements Method on a symmetrical workpiece geometry, for ease of calculation. Tool wear and tempering-induced deformation are estimated along the complete die life cycle, with the help of Neural Network Regression