ORGANIZATION OF MOBILE ROBOTS NAVIGATION BASED ON COGNITIVE MAPPING

Abstract

The article addresses the relevant task of ensuring the autonomy of mobile robots in complex conditions, where the use of traditional navigation methods based on global coordinate systems and satellite data is impossible or ineffective. To solve this problem, an approach based on cognitive (interpretive) navigation is proposed, where semantic understanding of the environment plays the central role. The key feature of the method is the construction of a cognitive map – a semantically oriented graph whose vertices correspond to landmark objects (or groups of homogeneous landmarks), and whose edges correspond to fixed sets of information-motor actions (elementary conditioned behavioral patterns). Thus, the robot's route while moving along the cognitive map is reduced to a fixed set of information-motor actions. The map construction process is carried out automatically based on a pre-obtained semantically segmented image of the terrain, which allows the mobile robot to acquire a priori information about the relative positions and shapes of the landmarks. To formalize the navigation process and manage the robot's behavior based on the cognitive map, the authors propose a specially developed formal language, LRNB (Language of Robot Navigation Behavior). This language allows the decomposition of complex missions into elementary information-motor actions, the specification of their completion conditions, and the description of interaction scenarios with dynamic and static objects. The work details the principles of building a cognitive map, the syntax of the LRNB language, and the mechanism for forming a route as a sequence of commands. The practical part includes the results of verifying the approach in a simulation environment using a specially developed emulator, as well as preliminary field tests on a laboratory tracked mobile robot, which confirmed the fundamental feasibility of the proposed approach. The obtained results indicate the potential of the method for application in critically important scenarios, such as disaster zones, areas of electronic warfare, and other environments with a high degree of uncertainty. Further work plans are proposed, related to bringing experimental conditions closer to the real-world conditions of potential operation.