# NRNH-AR: A Small Robotic Agent Using Tri-Fold Learning for Navigation and Obstacle Avoidance  
[PERSON] 1  
[PERSON] 1  
[PERSON] 1  
[PERSON] 1  
[PERSON] 1  
during the DRL phase. NRNH-AR is a novel DRL-based algorithm specifically designed for deployment on a small physical robot with limited computational resources. This approach offers advantages in terms of adaptability, computational efficiency, and the ability to learn with limited data, making it well-suited for real-world applications where robots must operate in dynamic and unpredictable environments. Our primary objective is to develop and validate a novel DRL-based algorithm in a small autonomous physical robot designed for real-world navigation in narrow or complex environments. Because the robot is small, it must operate with limited resources, such as a small memory size and low computation capacity. The NRNH-AR algorithm optimizes these resources using tri-fold learning phases. Small-sized robots have been developed for various applications, such as exploration and rescue in disaster regions, where human rescuers cannot enter due to narrow space and dangerous situations [5]. In the agricultural field, due to a lack of workers, several navigation robots were developed for purposes such as crop health monitoring and soil moisture assessment [6]. Due to narrow paths in the greenhouses, the size of the robots must be small enough to function properly. In [7], a small cleaning robot, which detects and removes sharp metal objects from the road, was developed, and the authors of [8] developed a small robot to detect mine holes. The size of the robot was adapted to the diameters of the holes. The robots used in [9] are categorized as small robots, the purpose of which is to avoid obstacles efficiently. The sizes of these small robots range from approximately 4 cm \(\times\) 2 cm \(\times\) 1 cm to 40 cm \(\times\) 30 cm \(\times\) 10 cm, depending on the application and the environment where the robot operates. Almost all robots use the simultaneous localization and mapping (SLAM) algorithm to understand their environment. The implementation of reinforcement learning algorithms in robotics has been the subject of extensive research [10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22]. These studies have explored various applications, including the active classification of moving targets using control policies learned in realistic environments [13; 14; 15; 16; 17]. For instance, Ref. [18] presents a DRL approach with a fuzzy attention mechanism and heterogeneous graph neural networks (FAM HGNN) to address obstacle avoidance in robotics environments. Similarly, Ref. [19] leverages LiDAR sensors for obstacle evasion and environmental understanding during navigation. In the field of autonomous robot navigation, works such as Refs. [8; 20; 21] have developed DRL algorithms to enhance decision making, enable active exploration, adapt to changing conditions, and optimize efficiency and safety, even in crowded environments. Another study [22] focuses on predicting pedestrians using visual sensors and neural networks, providing an innovative solution for robot navigation in dense crowds. Recent advances in obstacle avoidance have significantly enhanced mobile robot navigation. [PERSON] et al. (2024) presented a comprehensive analysis of global and local obstacle avoidance algorithms, highlighting trade-offs in convergence and computational efficiency [23]. Upon analyzing the six previous works [9; 18; 19; 20; 21; 22], it is evident that five of them primarily focused on software or algorithm development, demonstrating an efficiency of over 80%. However, it is important to note that none of these works were tested in real operating environments, which limits the ability to assess their true effectiveness and applicability. Conversely, the work presented in [20] underwent physical evaluation, providing more tangible performance data, albeit with an efficiency of 60%.