Abstract
The article discusses the issue of creating an environment’s map with semantic division of areas. This simplifies some aspects of mobile robot control and permits the robot to carry out tasks issued in a human-understandable form. The article presents an innovative algorithm for mapping the environment by a mobile robot. Its novelty stems from the use of a semantic description of the world and Delaunay triangulation method with constraints. This description is used to segment the map of the environment and to reduce redundant information. The developed algorithm is based on the idea of extending the borders of the already discovered areas; they are expanded as the new data are collected from the environment. These data contain information about the location of semantic types in the explored space and is used to update the areas during analysis of the environment’s map. The beforementioned triangulation method is used in that process. The performance of the proposed algorithm is tested in simulation studies. The obtained results show a good computational efficiency of the method, which is crucial in the problem of environment exploration by mobile robots with limited computational resources.
Highlights
Continuous development of automated systems, including mobile robotics, encourages scientists to develop sophisticated solutions [1]
This, in turn, is associated with problems of navigation and path planning, which depend on the environment that either has already been discovered or is just being explored [3]
The first area is related to the acquisition of semantic information from sensory data
Summary
Continuous development of automated systems, including mobile robotics, encourages scientists to develop sophisticated solutions [1]. This will allow robots to serve as a personal aid in everyday tasks. This, in turn, is associated with problems of navigation and path planning, which depend on the environment that either has already been discovered or is just being explored [3]. The first area is related to the acquisition of semantic information from sensory data. Problems encountered in this area often belong to the signal domain, i.e., processing, analysis, and segmentation.
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