Geometrical parametres of the adaptive unit for industrial robots’ gripper calculation

Виконано аналіз конструктивних особливостей вузла адаптації схвата промислового робота. У результаті аналізу виявлено ряд залежностей геометричних параметрів конструктивних елементів компенсаторів кутових похибок від величин похибок позиціонування схватів промислових роботів. Наведено аналітичні вирази для розрахунку вказаних конструктивних параметрів.The need to carry out investigations on the raise of accuracy and decrease of negative after-effect of pose error for the principal and auxiliary technical equipment is caused by the availability of errors in the robotized installation. One of the possible methods for negative influence decrease arising in the robotized technological service of facilities, which is industrial robots gripper adaptation, is considered in the article. Industrial robots’ gripper adaptation expects to apply additional facilities, so-called “Industrial Robots’ Adaptive Unit”. Compliance of the Adaptive Unit for Industrial Robots’ Grippers compensative element is achieved due to using of compensative spring elements, which are included into the compliant mechanisms construction. Using of the linear and angular errors compensator in the Adaptive Unit for Industrial Robots’ Grippers allows to overcome negative force-torque influence which arises at the moment of handling object fixing in the working position device. In the article the analysis of the constructive peculiarities of the Adaptive Unit for Industrial Robots’ Grippers has been carried out. As the analysis result dependencies of the angular error compensator constructive elements geometrical parameters on the industrial robots pose errors have been found. The analytical expressions for calculation of the mentioned construction parameters of the Adaptive Unit for Industrial Robots’ Grippers have been presented. Analytical dependences, which are connected with the geometric parameters of the Adaptive Unit for Industrial Robots’ Grippers constructive elements and take into account the length of handling object and distance of industrial robots’ grippers pole from the left end face of the handling object in its coordinate system, have been found. The obtained results make possible to calculate and choose the Adaptive Unit for Industrial Robots’ Grippers, which is necessary for certain conditions of mechanical assembly manufacturing

On inferring intentions in shared tasks for industrial collaborative robots

Inferring human operators' actions in shared collaborative tasks, plays a crucial role in enhancing the cognitive capabilities of industrial robots. In all these incipient collaborative robotic applications, humans and robots not only should share space but also forces and the execution of a task. In this article, we present a robotic system which is able to identify different human's intentions and to adapt its behavior consequently, only by means of force data. In order to accomplish this aim, three major contributions are presented: (a) force-based operator's intent recognition, (b) force-based dataset of physical human-robot interaction and (c) validation of the whole system in a scenario inspired by a realistic industrial application. This work is an important step towards a more natural and user-friendly manner of physical human-robot interaction in scenarios where humans and robots collaborate in the accomplishment of a task.Peer ReviewedPostprint (published version

Towards adaptive multi-robot systems: self-organization and self-adaptation

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.The development of complex systems ensembles that operate in uncertain environments is a major challenge. The reason for this is that system designers are not able to fully specify the system during specification and development and before it is being deployed. Natural swarm systems enjoy similar characteristics, yet, being self-adaptive and being able to self-organize, these systems show beneficial emergent behaviour. Similar concepts can be extremely helpful for artificial systems, especially when it comes to multi-robot scenarios, which require such solution in order to be applicable to highly uncertain real world application. In this article, we present a comprehensive overview over state-of-the-art solutions in emergent systems, self-organization, self-adaptation, and robotics. We discuss these approaches in the light of a framework for multi-robot systems and identify similarities, differences missing links and open gaps that have to be addressed in order to make this framework possible

A Framework of Hybrid Force/Motion Skills Learning for Robots

Human factors and human-centred design philosophy are highly desired in today’s robotics applications such as human-robot interaction (HRI). Several studies showed that endowing robots of human-like interaction skills can not only make them more likeable but also improve their performance. In particular, skill transfer by imitation learning can increase usability and acceptability of robots by the users without computer programming skills. In fact, besides positional information, muscle stiffness of the human arm, contact force with the environment also play important roles in understanding and generating human-like manipulation behaviours for robots, e.g., in physical HRI and tele-operation. To this end, we present a novel robot learning framework based on Dynamic Movement Primitives (DMPs), taking into consideration both the positional and the contact force profiles for human-robot skills transferring. Distinguished from the conventional method involving only the motion information, the proposed framework combines two sets of DMPs, which are built to model the motion trajectory and the force variation of the robot manipulator, respectively. Thus, a hybrid force/motion control approach is taken to ensure the accurate tracking and reproduction of the desired positional and force motor skills. Meanwhile, in order to simplify the control system, a momentum-based force observer is applied to estimate the contact force instead of employing force sensors. To deploy the learned motion-force robot manipulation skills to a broader variety of tasks, the generalization of these DMP models in actual situations is also considered. Comparative experiments have been conducted using a Baxter Robot to verify the effectiveness of the proposed learning framework on real-world scenarios like cleaning a table