Advancements in Robot Navigation Technologies: A Comprehensive Review

Abstract

Robotic navigation has experienced a considerable, rapid growth in the last few years due to the achievements by the state-of-the art technologies which produce more advanced, agile, and efficient robots in various applications. Current approaches (magnetic trail/incorporated wired fixated paths) were laborious to set up and lacked the ability to adaptively translate the route in response to this movement in the world or the task. In contrast, the current robotic platforms rely on the advanced sensors [lidar, ultra-wide-band (UWB) positioning, simultaneous localization and mapping (SLAM), camera, and motion sensors] for event-driven navigation. These systems are trained by complex algorithms (e.g., Extended Kalman Filter (EKF) for state estimation and environment identification and adaptation by deep learning models). In particular, these advances have also been used to decrease costs, since no physical infrastructure is necessary (e.g., to ensure due motion along the tracks or to get positional data from the beacons). As these technologies improve they are making robotic navigation much more powerful and generalizable, with a broad range of applications from logistics to healthcare and beyond.

Introduction

Robots have moved from science fiction into critical real-world roles across industries like manufacturing, healthcare, logistics, and hospitality. This transformation is largely due to advances in robotic navigation technologies, which enable autonomous movement in complex, changing environments. Early systems were rigid and relied on predefined paths using magnetic strips or rails. Modern systems integrate multiple sensors (LiDAR, cameras, IMUs) and AI algorithms to enhance robot intelligence, flexibility, and autonomy.

Core Concepts in Navigation

Navigation allows a robot to locate itself, plan a path, and avoid obstacles in real time. Key enabling technologies include:

• LiDAR: Uses laser pulses for high-resolution environmental mapping, performs well in low-light, but is costly and sensitive to harsh conditions.

• UWB (Ultra-Wideband): Offers accurate real-time indoor positioning, ideal for dynamic environments. It’s low-latency and energy-efficient but faces cost and scalability challenges.

• SLAM (Simultaneous Localization and Mapping): Allows robots to map and navigate unknown environments simultaneously using sensors like LiDAR and cameras. Visual SLAM (vSLAM) is common but suffers from depth and motion issues.

• INS (Inertial Navigation Systems): Uses gyroscopes and accelerometers for dead reckoning, providing navigation without external signals, though prone to drift over time.

• Kalman Filters (KF, EKF, UKF): Essential in state estimation and sensor fusion, especially in noisy or nonlinear systems.

Deep Learning and AI in Navigation

Machine learning, especially deep reinforcement learning, has enhanced robotic navigation by enabling real-time learning and decision-making. These systems improve with data and offer adaptability in complex, unstructured environments, though they require high computational power and face challenges with interpretability and safety.

Sensor Fusion and Methodology

Modern systems fuse data from UWB, LiDAR, IMUs, and odometry using Kalman filters to increase accuracy, reliability, and robustness. This fusion addresses individual sensor limitations:

• UWB provides global positioning but is sensitive to interference.

• Odometry is smooth but drifts over time.

• LiDAR is precise but data-heavy and affected by occlusions.

Extended (EKF) and Unscented Kalman Filters (UKF) correct for sensor noise and nonlinearities. These are scalable for multi-robot systems and resilient in harsh or changing environments.

Applications

A delivery robot system using UWB and odometry fused with Kalman filters was designed for indoor navigation, showing high accuracy and robustness even under signal loss or sensor drift. The system supports:

• Real-time performance

• Obstacle avoidance

• Fault tolerance

• Future expansion with LiDAR and AI-based adaptability

Literature Review

Papers reviewed explore:

• Limitations of traditional path-following robots

• Advantages and drawbacks of LiDAR, UWB, and SLAM

• Challenges like sensor drift, cost, and lack of real-world validation

• Gaps in social navigation, multi-robot coordination, and adaptability

Data Analysis

A dataset compares UWB+Odometry, UWB+LiDAR, and SLAM on:

• Localization accuracy

• Path planning speed

• System robustness

• Real-time latency

Visual analysis (Figure 4) shows UWB+LiDAR offers superior precision and robustness, while SLAM provides strong autonomy in unknown environments.

Conclusion

Implementing advanced technologies such as LiDAR, UWB, Simultaneous Localization and Mapping SLAM, and Inertial Navigation Systems (INS), and powerful algorithms (e.g., Kalman filtering, deep learning) have allowed remarkable progress in robotic navigation. These progress have made robots capable of performing and moving in increasingly complex, dynamic and poorly predictable scenarios at higher degrees of autonomy and accuracy. The combination of these technologies has allowed robots to take up challenges previously restricted by limitations of classical robot design, thereby opening new avenues of applications in various industries. With the prospective future in mind, future research and development are recommended to aim at enhancing the robustness of robotic systems, as well as their robustness to the uncertain and the vast range of environment. Another key direction to address is the development of an efficient human- robot interaction that will be intuitive, easy to use, and cooperative with the human being. Furthermore, the architectures that are required for safe introduction of autonomous robots, in order to build trust and guarantee that the robots involved are used in a way that is in line with the public values, will also be required. Since technological progress is still rising, we expect to also see the further development of smart, flexible and progressive robots for revolutionizing fields such as logistics, healthcare, agriculture and urban infrastructures. At its conclusion, these technologies will transform not only our lives and work, but also open up a new field into which to address some of our day\'s most pressing challenges. The development of robotic navigation holds a bright future ahead with the upgrading of sensors, computing, and algorithms. New developments in emerging technology, including 5G, edge computing, and sensors\' AI-enabled fusion, will enable robots to be real-time action in a complex and dynamic environment. Continuous improvements in deep learning and reinforcement learning will further enhance the intelligence of robots performing tasks in unstructured or human-oriented environment with various attractive applications. With increased versions of lower costs of LiDAR sensor and cameras, complex navigation systems will be able to reach the small industries and consumer markets. This will result in robots being used in fields from agriculture and medicine to disaster relief and autonomous vehicles. Performing efficient lightweight energy-hungry algorithms will also facilitate their use in harsh environments (e.g., space travel, undersea work). Additionally, multi-robot systems and swarm robotics will extend their impact in the domain of logistics and manufacturing, respectively with scalable and cooperative solutions. Generally, future advancement is directed toward development of highly accurate, adaptive, and robust navigation systems that will enable robots to be integrated seamlessly into wide-ranging real-world situations.

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Copyright

Copyright © 2025 Charu Awasthi , Deepal Misra, Harshit Goyal, Omang Joshi, Harsh Gupta, Puneeta Singh. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.