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The objective of this article is to propose data processing from laser range finder that will provide simple, fast, and reliable object recognition including moving objects. The whole method is based on four steps: segmentation, simplification, correspondence between consequent measurements, and object classification. Segmentation uses raw data from laser range finder and it is significant in logical connection of related segments. The most important step is simplification which provides data reduction and acceleration of object classification. The output of simplification is an object represented by significant points. Correspondence between consequent measurements is based on kd-tree nearest neighborhood search. The object is then classified by its spatial changes. These changes are evaluated by position of correspondent significant points. Input of proposed procedure is a probabilistic model of laser range finder. In this article, versatile probabilistic model of Hokuyo URM-30 LX was used. The method was verified by simulations and by tests in real environment. The results show that proposed method is reliable and with small modifications (of parameters), it is usable with any other planar laser range finder.

The article deals with a novel approach to reactive navigation. A proposed reactive navigation is based on the Vector Field Histogram (VFH) method, which is easily modifiable. The biggest advantage of the proposed method is that it allows the robot to avoid static as well as moving obstacles in an unknown environment in a more effective way and without the need of switching any algorithm or the robot’s behaviour. Moreover, the proposed extension allows the avoidance of several moving obstacles in real time. The article contains three proposed improvements of the original VFH* method: representation of moving objects, look-ahead tree improvements and approach to criterion function. The method is verified by several experiments in various scenarios, which include few static and moving obstacles. [ABSTRACT FROM AUTHOR]

Abstract: New modifications of VFH+ and V FH∗ (Vector Field Histogram) methods are presented in the paper. The features of the histogramic algorithms are generally improved and also using a potential of advanced sensors such as laser scanners is enabled. The main focus of the work is an introduction of modifications that provides collision-free navigation in environments with moving obstacles. For this purpose, a new approach in creating a look-ahead tree is proposed. All new modifications are analyzed and experimentally verified. The results obtained using conventional and modified methods have been compared. Experiments have demonstrated the validity and the technical feasibility of the proposed methods. [Copyright &y& Elsevier]

This paper deals with the design and implementation of a universal cyber-physical model capable of simulating any production process in order to optimize its logistics systems. The basic idea is the direct possibility of testing and debugging advanced logistics algorithms using a digital twin outside the production line. Since the digital twin requires a physical connection to a real line for its operation, this connection is substituted by a modular cyber-physical system (CPS), which replicates the same physical inputs and outputs as a real production line. Especially in fully functional production facilities, there is a trend towards optimizing logistics systems in order to increase efficiency and reduce idle time. Virtualization techniques in the form of a digital twin are standardly used for this purpose. The possibility of an initial test of the physical implementation of proposed optimization changes before they are fully implemented into operation is a pragmatic question that still resonates on the production side. Such concerns are justified because the proposed changes in the optimization of production logistics based on simulations from a digital twin tend to be initially costly and affect the existing functional production infrastructure. Therefore, we created a universal CPS based on requirements from our cooperating manufacturing companies. The model fully physically reproduces the real conditions of simulated production and verifies in advance the quality of proposed optimization changes virtually by the digital twin. Optimization costs are also significantly reduced, as it is not necessary to verify the optimization impact directly in production, but only in the physical model. To demonstrate the versatility of deployment, we chose a configuration simulating a robotic assembly workplace and its logistics. [ABSTRACT FROM AUTHOR]