Mivar model and algorithm of information processing for avoiding dynamic obstacles with a changing safety zone

We propose a mivar model and a mivar algorithm for dynamic trajectory planning in large indoor spaces with dense crowds of moving people. The main idea is to calculate the parameters of an elliptical changing safety zone for dynamic obstacles by integrating semantic object detection and geometric mapping. The mivar model relies on semantic information: object class, speed, and scene factors. The mivar expert system calculates key parameters of the safety zone and prediction position, providing differentiated safety margins for various semantic targets, such as children, the elderly, and adults. The calculated safety zone will be considered in path planning as the size of the obstacle itself. The scientific innovation lies in the use of dynamic ellipses, changing their size depending on semantic information, to determine the safety zone of dynamic obstacles. The mivar algorithm was verified on a platform implemented in Python using Pygame, with integration of the data exchange interface with the Wi!Mi. This allowed for visualization of the planning process, confirmation of effectiveness, and quantification of safety gains. Prospects for research include deep integration with machine learning methods to enhance the robustness and flexibility of rule updates.

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