Aug 2026· IAES International Journal of Artificial Intelligence (IJ-AI)· Vol 15, pp. 3836· 0 citations· 31 references
TL;DR
Simulation and physical experiments confirmed collision-free navigation and successful quick response (QR)-code-based goods inspection, demonstrating the feasibility of the proposed framework for small, structured indoor environments.
Abstract
Reliable mobile-robot deployment requires mapping, localization, global planning, and real-time obstacle avoidance to operate consistently under sensor noise and physical constraints. This study presents a vision-based hybrid navigation framework for a differential-drive mobile robot in a warehouse-like environment. The main contribution lies in the system-level integration and experimental validation of established techniques rather than the development of a new standalone navigation algorithm. A ceiling-mounted camera converts top-view images into an occupancy-grid map and world coordinates, while a convolutional neural network (CNN) recognizes goal markers. Odometry–augmented reality University of Cordoba (ArUco) fusion is used to correct accumulated localization drift. Particle swarm optimization (PSO) generates smooth global paths offline, whereas the dynamic window approach (DWA) performs real-time local motion control. Experiments in a 3.3 m × 2.4 m workspace achieved average obstacle-localization errors of 0.959 cm and 0.696 cm along the x- and y-axes, respectively, and a goal-recognition accuracy of 99%. The DWA controller required 12.61±2.42 ms per cycle, while rapidly-exploring random tree (RRT) and PSO required 4–7 s and 477–692 s, respectively, for global path generation. Simulation and physical experiments confirmed collision-free navigation and successful quick response (QR)-code-based goods inspection, demonstrating the feasibility of the proposed framework for small, structured indoor environments.
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
This work forms a prior method based on Deep Deterministic Policy Gradient controller and a adaptive unscented Kalman filter which continuously providing constantly estimating robot states and improving the motion primitives in hazardous operating conditions to validate that end-to-end AI navigation architectures deliver the robust performance needed for next-gen automated industrial monitoring and non-destructive evaluation at scale.
Andrey Ershov, Alexey Lyapunov· International Journal of Int...· 0 citations
Complex and dynamic environment autonomy, the ability to navigate complex and dynamic environments autonomously in real-time and with limited computational power, will persist as a basic challenge to mobile robotic systems. As a solution to this problem, this paper presents an alternative minimum-cost deep learning format using optimization, which combines a Modified Rime Optimization Algorithm (M-RIME) and a lightweight SqueezeNet-based navigation network. The given strategy is supposed to increase the depth of perception, precision of motion control, and computational efficiency at the same time. To begin with, an end-to-end navigation model that uses SqueezeNet is created to concurrently map raw red–green–blue observations to continuous motion commands to facilitate the perception–action interface. Subsequently, the M-RIME algorithm is improved with adaptive exploration–exploitation balancing and an adaptive mutation operator that greatly enhance convergence speed, global exploration, and the stability of solutions. The presented optimizer is used to improve the performance of the SqueezeNet model by automatically optimizing its hyperparameters within an offline optimization system, which improves navigation and retains its real-time functionality. Multi-frame and multi-measurement simulation experiments are performed in complex indoor navigation environments, where success rate, collision rate, navigation time, path length, and frames per second are adopted as quantitative and qualitative metrics to evaluate the suggested framework. The findings show that the optimized SqueezeNet is not only much better than the baseline model and other state-of-the-art optimization frameworks in terms of success rates of navigation, smoother trajectories, lower rates of collisions, and high-quality real-time optimization but also significantly better. In addition, convergence analysis establishes the effectiveness and precision of the suggested M-RIME algorithm in a high-dimensional optimization setting. On the whole, the presented framework will offer a good and scalable choice for autonomous navigation in real-time and can serve as an excellent basis for future developments aimed at temporal modeling, practical applications of autonomous driving, and multi-sensor fusion-driven robotic perception systems.
Jiangjun Ruan, Mei Wang, Lu Peng et al.· AIP Advances· 0 citations
Autonomous mobile robots require reliable coordination among navigation, perception, tracking, and precision approach modules to complete indoor object-search missions. Existing systems often remain fragmented, treating navigation, detection, tracking, and docking as separate tasks rather than as an end-to-end pipeline. This study has two objectives: to validate a fault-tolerant coordination architecture for autonomous search-and-approach behavior and to compare two search strategies under controlled indoor target-position scenarios. The primary contribution is a methodological integration framework based on a Finite State Machine (FSM) that coordinates ROS 2 Navigation2 global navigation, YOLO11n object detection, centroid tracking, and Image-Based Visual Servoing (IBVS), while managing transitions among navigation, visual servoing, recovery, and mission-completion states. A quantitative Gazebo simulation experiment used 40 controlled trials to compare Random Exploration and Waypoint-Based Search. The integrated system achieved a 100% mission success rate without command conflicts, indicating effective FSM-based coordination between global navigation and local visual control. Waypoint-Based Search was more efficient when the target was aligned with predefined nodes, achieving a mean detection time of 50.55 s compared with 164.80 s for Random Exploration. Conversely, Random Exploration performed better when the target was away from predefined paths, reducing mean detection time to 87.00 s compared with 183.64 s. Fault-tolerant behavior was demonstrated in simulation through successful mission completion despite repeated LiDAR-triggered obstacle-recovery events during visual approach. These findings show that search efficiency depends on alignment between exploration design and spatial structure, not universal strategy superiority.
Abstract. In general, the obstacle detection systems mainly rely on depth cameras or AI-based vision approaches; however, these methods are often constrained by limited fields of view and the need for continuous model retraining to adapt to complex and dynamic industrial scenes. To overcome these limitations, this study proposes a LiDAR-based obstacle detection and field monitoring system integrated with a quadruped robot. The proposed system focuses on three main components: real-time field mapping, intelligent path planning with obstacle avoidance, and field change detection. LiDAR point cloud data are pre-processed using pass-through and voxel grid filters, followed by coordinate transformation into the robot reference frame. The Cartographer simultaneous localization and mapping (SLAM) algorithm are employed to generate high-resolution occupancy grid maps for navigation. For autonomous operation, erosion processing and connected component labelling are used to define safe regions, while the A* algorithm enables efficient path planning and adaptive obstacle avoidance in complex environments. To detect unknown obstacles and environmental changes, Gaussian filtering and map differencing are applied, and map similarity is evaluated using histogram analysis and SIFT-based feature matching. Experimental results demonstrated that the system achieves a mapping resolution of 0.05 m and satisfies the Taiwan Association of Information and Communication Standards (TAICS) requirements, including 0.2 m planimetric accuracy and less than 0.1 m positional error. The proposed approach effectively identifies unknown obstacles and visually highlights risk areas, providing a reliable solution for intelligent workplace safety monitoring.
Wen-Yang Chang, Chen-Hsiang Hung, Zheng-Xun Huang et al.· The International Archives o...· 0 citations
Reliable localization is required for autonomous mobile robots when individual sensing streams become noisy, intermittent, or unavailable. This study evaluates a multi-sensor fusion framework that combines LiDAR, monocular vision, GPS, UWB, and IMU data using three strategies: (i) a baseline Extended Kalman Filter (EKF); (ii) a dual-stage sequential EKF that refines LiDAR-Inertial Odometry (LIO) before the final fusion stage; and (iii) a hybrid learning-filtering approach in which modality-specific learned motion and position estimates are incorporated into an EKF. All evaluations were conducted in ROS-Gazebo under nominal operation and controlled sensor-degradation/dropout conditions. Relative to the controller-derived reference trajectory, the standard EKF achieved 0.2235 m RMSE and the dual-stage EKF achieved 0.2029 m RMSE, a descriptive reduction of approximately 9.2% for the reported run. The hybrid learning-EKF achieved 0.212 m RMSE under nominal sensing and 0.384 m RMSE during the tested failure sequence. These results support the evaluated fusion designs under the reported simulation conditions, but they do not establish statistical generalization or universal real-world resilience; independent ground truth, repeated trials, GPS ablation, and physical validation remain necessary.
Muhammad Shahzad Alam Khan, Anas Bin Aqeel, Hassan Elahi et al.· Scientific Reports· 0 citations
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