Improving the Accuracy of Industrial Robots by offline Compensation of Joints Errors

The use of industrial robots in many fields of industry like prototyping, pre-machining and end milling is limited because of their poor accuracy. Robot joints are mainly responsible for this poor accuracy. The flexibility of robots joints and the kinematic errors in the transmission systems produce a significant error of position in the level of the end-effector. This paper presents these two types of joint errors. Identification methods are presented with experimental validation on a 6 axes industrial robot, STAUBLI RX 170 BH. An offline correction method used to improve the accuracy of this robot is validated experimentally

Feedrate planning for machining with industrial six-axis robots

The authors want to thank Stäubli for providing the necessary information of the controller, Dynalog for its contribution to the experimental validations and X. Helle for its material contributions.Nowadays, the adaptation of industrial robots to carry out high-speed machining operations is strongly required by the manufacturing industry. This new technology machining process demands the improvement of the overall performances of robots to achieve an accuracy level close to that realized by machine-tools. This paper presents a method of trajectory planning adapted for continuous machining by robot. The methodology used is based on a parametric interpolation of the geometry in the operational space. FIR filters properties are exploited to generate the tool feedrate with limited jerk. This planning method is validated experimentally on an industrial robot

Dynamic Compensation Framework to Improve the Autonomy of Industrial Robots

It is challenging to realize the autonomy of industrial robots under external and internal uncertainties. A majority of industrial robots are supposed to be programmed by teaching-playback method, which is not able to handle with uncertain working conditions. Although many studies have been conducted to improve the autonomy of industrial robots by utilizing external sensors with model-based approaches as well as adaptive approaches, it is still difficult to obtain good performance. In this chapter, we present a dynamic compensation framework based on a coarse-to-fine strategy to improve the autonomy of industrial robots while at the same time keeping good accuracy under many uncertainties. The proposed framework for industrial robot is designed along with a general intelligence architecture that is aiming to address the big issues such as smart manufacturing, industrial 4.0

Improving the Dynamic Accuracy of Elastic Industrial Robot Joint by Algebraic Identification Approach

International audienceIn this paper, an improvement of the dynamic accuracy of a flexible robot joint is addressed. Based on the observation of the measured axis deformation, a simplified elastic joint model is deduced. In the first step, the non-linear model component's is analyzed and identified in the cases of the gravity bias and the friction term. In the second step, a non asymptotically algebraic fast identification of the oscillatory behavior of the robot axis is introduced. Finally, the performances of the identification approach are exploited in order to improve the dynamic accuracy of a flexible robot axis. This is done experimentally by the combination of the adaptation of the jerk time profile to reduce the end-point vibration and the model-based precompensation of the end-point tracking error

Robotic learning of force-based industrial manipulation tasks

Even with the rapid technological advancements, robots are still not the most comfortable machines to work with. Firstly, due to the separation of the robot and human workspace which imposes an additional financial burden. Secondly, due to the significant re-programming cost in case of changing products, especially in Small and Medium-sized Enterprises (SMEs). Therefore, there is a significant need to reduce the programming efforts required to enable robots to perform various tasks while sharing the same space with a human operator. Hence, the robot must be equipped with a cognitive and perceptual capabilities that facilitate human-robot interaction. Humans use their various senses to perform tasks such as vision, smell and taste. One sensethat plays a significant role in human activity is ’touch’ or ’force’. For example, holding a cup of tea, or making fine adjustments while inserting a key requires haptic information to achieve the task successfully. In all these examples, force and torque data are crucial for the successful completion of the activity. Also, this information implicitly conveys data about contact force, object stiffness, and many others. Hence, a deep understanding of the execution of such events can bridge the gap between humans and robots. This thesis is being directed to equip an industrial robot with the ability to deal with force perceptions and then learn force-based tasks using Learning from Demonstration (LfD).To learn force-based tasks using LfD, it is essential to extract task-relevant features from the force information. Then, knowledge must be extracted and encoded form the task-relevant features. Hence, the captured skills can be reproduced in a new scenario. In this thesis, these elements of LfD were achieved using different approaches based on the demonstrated task. In this thesis, four robotics problems were addressed using LfD framework. The first challenge was to filter out robots’ internal forces (irrelevant signals) using data-driven approach. The second robotics challenge was the recognition of the Contact State (CS) during assembly tasks. To tackle this challenge, a symbolic based approach was proposed, in which a force/torque signals; during demonstrated assembly, the task was encoded as a sequence of symbols. The third challenge was to learn a human-robot co-manipulation task based on LfD. In this case, an ensemble machine learning approach was proposed to capture such a skill. The last challenge in this thesis, was to learn an assembly task by demonstration with the presents of parts geometrical variation. Hence, a new learning approach based on Artificial Potential Field (APF) to learn a Peg-in-Hole (PiH) assembly task which includes no-contact and contact phases. To sum up, this thesis focuses on the use of data-driven approaches to learning force based task in an industrial context. Hence, different machine learning approaches were implemented, developed and evaluated in different scenarios. Then, the performance of these approaches was compared with mathematical modelling based approaches.</div