The purpose of the article was to build a low-cost system for identifying shapes in order to program industrial robots (on the base of the six-axis “ABB IRB 140” robot) for a welding process in 2D. The whole system consisted of several elements developed in individual stages. The first step was to identify the existing robot control systems, which analysed images from an attached low-cost digital camera. Then, a computer program, which handles communication with the digital camera capturing and processing, was written. In addition, the program’s task was to detect geometric shapes (contours) drawn by humans and to approximate them. This study also presents research on a binarization and contour recognition method for this application. Based on this, the robot is able to weld the same contours on a 2D plane.

Purpose This paper aims to propose the method of automatic robotic assembly of two or more parts placed without fixing instrumentation and positioning on the pallet. Design/methodology/approach Assembly tasks performed by industrial robots are usually based on a constant program, extensive tooling, fixing objects in a given place and a relatively limited sensory system. In this study, a different approach is presented. The industrial robot program is adjusted to the location of parts for assembly in the work space. This leads to a transition from a clearly defined assembly sequence realized by the industrial robot to the one in which the order of execution of the assembly operations can be determined by the mutual position of parts to be assembled. Findings The method presented in this study combines many already known algorithms. The contribution of the authors is to test and select the appropriate combination of methods capable of supporting robotic assembly process based on data from optical 3D scanners. The sequence of operations from scanning to place the parts in the installation position by an industrial robot is developed. A set of parameters for selected methods is presented. The result is a universal procedure that determines the position of the preset models in partial measurements performed at a fixed relative position of the sensor, the measurement object. Originality/value The developed procedure for determining the position of the parts is essential to develop a flexible robotic assembly system. It will be able to perform the task of assembly on the basis of appropriate search algorithms taking into account the selected and implemented sequence of assembly position and orientation of parts, particularly the base unit freely placed on an assembly pallete. It is also the basis of a system for testing different algorithms to optimize the flexible robotic assembly.

Abstract. The article analyzes the available ergonomic constructions used for the support of the musculoskeletal system during static, prolonged work performed in forced positions. Possible evaluation methods are presented as well as ergonomic considerations of work performed in inclined positions, where there is no possibility of influencing the working plane. As a result of the presented work, a set of criteria has been proposed and the requirements for methods which can be used to evaluate the technical constructions supporting the worker during tasks performed in forced and static positions.

The purpose of the paper is to explore the problem of modeling technological assembly process, particularly generating assembly sequence for parts and machinery sets. A new computer program Msassembly is introduced. The program was invented by the authors on the basis of an algorithm for determining assembly sequence for parts and machinery sets. The algorithm is based on hypergraphs and directed graphs, as well as on assessment of transitions between assembly states. The principles of operation of Msassembly are presented on the example of modelling the assembly sequence of a ball joint. At the end of the paper, research findings are submitted.

The proposed model of the neural network describes the task of planning the assembly sequence on the basis of predicting the optimal assembly time of mechanical parts. In the proposed neural approach, the k-means clustering algorithm is used. In order to find the most effective network, 10,000 network models were made using various training methods, including the steepest descent method, the conjugate gradients method, and Broyden–Fletcher–Goldfarb–Shanno algorithm. Changes to network parameters also included the following activation functions: linear, logistic, tanh, exponential, and sine. The simulation results suggest that the neural predictor would be used as a predictor for the assembly sequence planning system. This paper discusses a new modeling scheme known as artificial neural networks, taking into account selected criteria for the evaluation of assembly sequences based on data that can be automatically downloaded from CAx systems.