首页|Studies from University of Modena and Reggio Emilia Add New Findings in the Area of Robotics (A Method for the Assessment and Compensation of Positioning Errors In Industrial Robots)
Studies from University of Modena and Reggio Emilia Add New Findings in the Area of Robotics (A Method for the Assessment and Compensation of Positioning Errors In Industrial Robots)
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NETL
NSTL
Elsevier
Investigators publish new report on Robotics. According to news reporting from Reggio Emilia, Italy, by NewsRx journalists, research stated, “Industrial Robots (IR) are currently employed in several production areas as they enable flexible automation and high productivity on a wide range of operations. The IR low positioning performance, however, has limited their use in high precision applications, namely where positioning errors assume importance for the process and directly affect the quality of the final products.” Financial support for this research came from European Community. The news correspondents obtained a quote from the research from the University of Modena and Reggio Emilia, “Common approaches to increase the IR accuracy rely on empirical relations which are valid for a single IR model. Also, existing works show no uniformity regarding the experimental procedures followed during the IR performance assessment and identification phases. With the aim to overcome these 1 restrictions and further extend the IR usability, this paper presents a general method for the evaluation of IR pose and path accuracy, primarily focusing on instrumentation and testing procedures. After a detailed description of the experimental campaign carried out on a KUKA KR210 R2700 Prime robot under different operating conditions (speed, payload and temperature state), a novel online compensation approach is presented and validated. The position corrections are processed with an industrial PC by means of a purposely developed application which receives as input the position feedback from a laser tracker.”
Reggio EmiliaItalyEuropeEmerging TechnologiesMachine LearningNano-robotRobotRoboticsUniversity of Modena and Reggio Emilia
NETL
NSTL
Elsevier
