# Autonomous Navigation Framework for Intelligent Robots Based on a Semantic Environment Modeling
## 1 Introduction  
Environment modeling, recognition, and planning are fundamental components for intelligent systems, such as mobile robots, that enable them to understand and perceive the complex environment in the same way a human does and perform the task reliably. When mobile robots are deployed in the real-world, numerous challenges arise: acquiring spatial models of the continuously changing environment that leads to the problem of robotic mapping, a radically changed scene appearance that is one of the significant factors towards the visual recognition failure [1], and the dynamic nature ofthe operating environment that is a critical challenge for robot planning [2]. Many approaches have been proposed to handle these issues [3]. However, among them, a growing world-wide trend is introducing semantic information into robotic systems using ontology [4], which endows the mobile robot with human-like mapping [5], recognition [6] and planning [7] capabilities. Inspired by these recent advances, our framework exploits the semantic knowledge by defining a Triplet Ontological Semantic Model (TOSM). Recent developments in robotics have heightened the need for semantic information processing techniques to design object and place recognition systems based on the findings of cognitive science. Semantic recognition systems based on Convolutional Neural Network (CNN) models have made groundbreaking advances to tackle scene understanding tasks with an emphasis on object and place recognition [8]. The complex representation of visual information and their processing by biologically inspired design of CNN model has shown unprecedented advancements in visual recognition. The layered structure in the CNN model is more reliable to represent the lower area of the human visual cortex [9]. Motivated by these developments, we combine the strengths of a deep learning-based CNN model with an on-demand database for semantic object-based place recognition and robot localization. When planning a task, an agent requires a description of the environment and of itself, containing the possible states, actions, and events. Classical planners, such as Planning Domain Definition Language (PDDL) planners [10], make use of a set of predicates and actions to generate a valid action sequence. Those planners have been vastly used by the research community and integrated into powerful frameworks [11]. However, they tend not to perform well on unpredictable and dynamic environments, usually described by Partially Observed Markov Decision Processes (POMDP). Aiming to solve POMDPs on a variety of domains, reinforcement learning approaches try to use a large amount of data to approximate a policy capable of obtaining an optimal action based solely on the current state. Those approaches have shown promising results, but they still underperform when dealing with long-term scenarios. Our framework tries to overcome those shortcomings by combining classical PDDL planners, sampling-based planners, and reinforcement learning algorithms into a hierarchical scheme. Autonomous navigation frameworks based on knowledge base have become essential for complex applications of robotic systems in real-world environments. In this direction, the Ontology-Based Unified Robot Knowledge (OUR-K) framework [12] emphasized at robot intelligence to perform navigation, recognition, planning, and manipulation tasks using knowledge description (for contexts, features, objects, spaces, and actions) and knowledge association for establishing relationships between descriptions by supporting inference. The OUR-K framework considered small static indoor environments such as an office room; however, our framework concentrates on robot navigation in large dynamic environments using the TOSM-based architecture. The RoboEarth framework [13] designed a knowledge-based system to generate, share, and reuse data using a cloud database. In contrast, our framework uses the on-demand database to ensure real-time capabilities.