DEVELOPMENT OF AN ALGORITHM FOR SEARCHING FOR THE OPTIMAL ROUTE IN INDOOR PREMISES
M. Lysenko · Mechanics And Mathematical Method · 2025
The development of an inclusive environment is a crucial aspect of modern society, particularly in the field of indoor navigation systems. Many existing shortest path algorithms, such as the classic Dijkstra’s algorithm, do not take into account the specific needs of users with disabilities. This can lead to the creation of routes that are unsuitable or inconvenient for wheelchair users, individuals with visual or hearing impairments, and those with temporary mobility difficulties. This issue is especially critical in medical facilities, where the speed and accessibility of movement can directly impact the quality of service and patient safety. This paper presents a modification of Dijkstra’s algorithm that allows for the dynamic adaptation of graph weights according to the specific needs of users and the current environmental conditions. The proposed approach integrates a production knowledge model, enabling the algorithm to make decisions based on formalized rules that consider accessibility parameters such as the availability of elevators, ramps, doorway widths, lighting, tactile markings, and other factors. The adaptive navigation system, built on the basis of the modified Dijkstra’s algorithm, offers several key advantages: the ability to personalize routes according to the user’s profile, automatic exclusion of inaccessible or unsuitable paths, and dynamic route adjustment in response to environmental changes (e.g., construction work or elevator malfunctions). The proposed algorithm can be used to optimize navigation not only in medical facilities but also in shopping centers, educational institutions, transportation hubs, and other buildings with high pedestrian traffic. The results of the study indicate that implementing an adaptive approach to route searching significantly enhances accessibility and convenience for all categories of users. Future system development may include the integration of artificial intelligence technologies for real-time analysis of environmental changes, as well as extending the algorithm for use in open spaces, particularly in urban navigation systems.