Jul 2026· European International Journal of Multidisciplinary Research and Management Studies· Vol 6, pp. 69-84· 0 citations
TL;DR
A conceptual DRL navigation framework is proposed comprising environmental perception, state representation, policy optimization, experience replay, reward engineering, semantic reasoning, and adaptive trajectory generation, and the framework demonstrates how semantic knowledge, sensor fusion, and reinforcement learning cooperate to produce robust navigation strategies under uncertainty.
Abstract
Intelligent robot navigation in dynamic environments remains one of the most challenging problems in autonomous robotics because navigation systems must continuously perceive environmental changes, predict moving obstacles, and generate safe trajectories while maintaining operational efficiency. Traditional navigation approaches, including graph-based path planning, rule-based obstacle avoidance, and probabilistic localization, often exhibit limited adaptability when environmental conditions change rapidly. Recent advances in artificial intelligence, particularly Deep Reinforcement Learning (DRL), have enabled autonomous robots to learn navigation policies directly from environmental interactions without relying exclusively on handcrafted rules. DRL integrates perception, decision-making, and continuous learning into a unified framework, making it particularly suitable for complex and uncertain environments such as warehouses, hospitals, manufacturing plants, urban streets, and disaster-response scenarios.
This research-review paper presents a comprehensive analysis of Deep Reinforcement Learning-based intelligent robot navigation with emphasis on dynamic obstacle avoidance, adaptive path planning, perception integration, reward optimization, and policy learning. The paper synthesizes contemporary studies related to artificial intelligence, semantic decision intelligence, cyber-physical systems, cloud intelligence, autonomous optimization, and intelligent infrastructure to establish a multidisciplinary understanding of modern robotic navigation. Particular attention is devoted to semantic AI-enabled decision intelligence, which enhances contextual understanding during navigation and improves policy robustness in continuously evolving environments (Goyal, 2025).
A conceptual DRL navigation framework is proposed comprising environmental perception, state representation, policy optimization, experience replay, reward engineering, semantic reasoning, and adaptive trajectory generation. The framework demonstrates how semantic knowledge, sensor fusion, and reinforcement learning cooperate to produce robust navigation strategies under uncertainty. Furthermore, the paper evaluates challenges involving sparse rewards, safety constraints, computational complexity, sim-to-real transfer, multi-agent coordination, and real-time deployment.
Autonomous robotic systems are transforming industries such as automation, healthcare, transportation, defense, and intelligent services. A major challenge in robotics is motion planning in dynamic and uncertain environments, where robots must navigate safely and efficiently. Traditional algorithms like Dijkstra’s, A*, Probabilistic Road Maps, and Rapidly Exploring Random Trees perform well in static environments but struggle with moving obstacles, sensor uncertainty, and real-time decision-making. Artificial Intelligence (AI) has improved robotic motion planning through adaptive learning, predictive decision-making, reinforcement learning, fuzzy logic, neural networks, and evolutionary optimization. These techniques enable robots to learn from their environment and optimize navigation over time. This paper reviews AI-based adaptive motion planning methods developed before 2019 and examines their role in path optimization, obstacle avoidance, localization, and decision-making. It also proposes a hybrid framework combining sensor fusion, environment mapping, fuzzy inference systems, and reinforcement learning for real-time path optimization. Simulation results demonstrate that AI-based adaptive planning achieves better navigation accuracy, obstacle avoidance, computational efficiency, and environmental adaptability compared to traditional methods.
Michael Anderson· International Journal of Int...· 0 citations
Highlights What are the main findings? Systematically classifies DRL algorithms for indoor mobile robot dynamic obstacle avoidance. Analyzes core challenges and improved strategies across five key technical dimensions. What are the implications of the main findings? Provides a unified reference framework for robot navigation and DRL algorithm selection. Clarifies promising future directions for indoor dynamic obstacle avoidance research. Abstract The ability of mobile robots to avoid obstacles dynamically in indoor environments is a necessary condition for achieving autonomous planning and navigation. When dealing with unstructured and randomly dynamic indoor scenes, traditional obstacle avoidance algorithms have poor adaptability and low flexibility, making it difficult to handle environmental uncertainties. Deep Reinforcement Learning (DRL), with its efficient end-to-end decision-making, autonomous interactive learning capabilities, and proficiency in modeling complex dynamic systems, has emerged as a focal point of research in dynamic obstacle avoidance. This paper first presents the theoretical foundation of DRL, then categorizes the fundamental DRL algorithms for indoor dynamic obstacle avoidance into three main types: Value function-based, Policy-based, and Actor-Critic-based algorithms, while also introducing relevant algorithms. Furthermore, it addresses the core challenges encountered in indoor dynamic obstacle avoidance and summarizes various improvement strategies for the different basic algorithms, detailing their starting points and performance impacts. Finally, the paper outlines the development trends and future research directions in this domain. This review aims to serve as a systematic reference for the design and engineering application of DRL algorithms in dynamic obstacle avoidance for indoor mobile robots.
Jian-Dong Zhao, Hong-hua Zhao, Benwang Li et al.· Italian National Conference...· 0 citations
An intelligent vision-based autonomous robotic framework that integrates deep learning-based object detection with hybrid adaptive navigation for dynamic environments is proposed in this research. The proposed system addresses the challenges of real-time perception and robust navigation in unstructured settings by combining a convolutional neural network (CNN) for object detection with a hybrid control mechanism for motion planning. The CNN, implemented using a state-of-the-art architecture such as YOLO, processes visual input to identify obstacles and target objects, providing critical environmental awareness. Moreover, the hybrid navigation strategy merges reactive obstacle avoidance, achieved through algorithms like the Vector Field Histogram (VFH), with adaptive path planning using Rapidly-exploring Random Trees (RRT) to ensure both immediate collision avoidance and long-term goal convergence. The integration of these components enables the robotic system to dynamically adjust its navigation policy in response to environmental changes, thereby improving robustness and adaptability. The novelty of our approach lies in the seamless fusion of vision-based perception and adaptive control, which enhances the system’s capability to operate in complex, dynamic scenarios. Experimental validation demonstrates the effectiveness of the framework in real-world applications, highlighting its potential for deployment in autonomous vehicles, service robotics, and industrial automation. The proposed method offers a scalable and efficient solution for autonomous systems requiring high levels of situational awareness and adaptive decision-making.
K. A.· International Journal on Rob...· 0 citations
Unstructured robotic navigation is a critical research area due to its applications in disaster response, space exploration, agriculture, and military operations. Unlike structured environments, unstructured settings are unpredictable, dynamic, and lack complete sensory information, making navigation highly complex. Before 2018, research focused on classical and early intelligent methods such as probabilistic robotics, heuristic path planning, and initial machine learning integration. Key navigation tasks—localization, mapping, path planning, and motion control—were addressed using techniques like Bayesian filtering, Kalman filters, particle filters, and occupancy grid mapping to handle uncertainty. Algorithms such as A*, D*, and Rapidly-exploring Random Trees (RRT) were widely used for path planning, often enhanced with heuristics and real-time replanning for dynamic environments. Sensor fusion combining LiDAR, sonar, and vision improved environmental perception, while early AI approaches like neural networks and fuzzy logic enabled adaptive decision-making. Reinforcement learning also showed potential, though it was limited by computational constraints at the time.Despite significant progress, pre-2018 systems faced challenges such as limited computational power, poor generalization, and reliance on handcrafted features. Overall, these foundational methods played a vital role in advancing autonomous navigation, though achieving full autonomy in complex environments remains an ongoing challenge.
Lakshmi Narayanan· International Journal of Int...· 0 citations
A role-of-learning taxonomy is proposed that categorizes existing methods according to how learning participates in the planning pipeline, including direct policy learning, learning-augmented classical planning, hybrid planning, and training enhancement methods.
Zong-Yuan Shen, Shalabh Gupta, Shan-Cheng Zhao et al.· 1 citation
Autonomous robotic navigation has become a key capability for intelligent robots operating in dynamic environments such as warehouses, hospitals, smart cities, agriculture, and autonomous transportation. While supervised learning methods achieve strong navigation performance, they depend on large labeled datasets that are costly and time-consuming to obtain. Self-Supervised Learning (SSL) addresses this limitation by enabling robots to learn robust visual and spatial representations directly from unlabeled sensor data through self-generated learning objectives. This paper reviews recent advances in SSL techniques, including representation learning, contrastive learning, predictive learning, masked image modeling, and multimodal sensor fusion for autonomous navigation. It also examines the integration of data from RGB cameras, LiDAR, IMUs, GPS, depth sensors, and odometry to improve perception, localization, obstacle avoidance, and path planning in unknown environments. Finally, the paper discusses key challenges such as domain adaptation, computational efficiency, safety, and continual learning, highlighting SSL's potential to enable scalable, adaptive, and lifelong autonomous robotic navigation.
D. Michie, Roger Needham· International Journal of Int...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.
