Optimization of a Navigation System for Autonomous Charging of Intelligent Vehicles Based on the Bidirectional A* Algorithm and YOLOv11n Model

Abstract

Aiming to enable intelligent vehicles to achieve autonomous charging under low-battery conditions, this paper presents a navigation system for autonomous charging that integrates an improved bidirectional A* algorithm for path planning and an optimized YOLOv11n model for visual recognition. The system utilizes the improved bidirectional A* algorithm to generate collision-free paths from the starting point to the charging area, dynamically adjusting the heuristic function by combining node-target distance and search iterations to optimize bidirectional search weights, pruning expanded nodes via a greedy strategy and smoothing paths into cubic Bézier curves for practical vehicle motion. For precise localization of charging areas and piles, the YOLOv11n model is enhanced with a CAFMFusion mechanism to bridge semantic gaps between shallow and deep features, enabling effective local-global feature fusion and improving detection accuracy. Experimental evaluations in long corridors and complex indoor environments showed that the improved bidirectional A* algorithm outperforms the traditional improved A* algorithm in all metrics, particularly in that it reduces computation time significantly while maintaining robustness in symmetric/non-symmetric and dynamic/non-dynamic scenarios. The optimized YOLOv11n model achieves state-of-the-art precision (P) and mAP@0.5 compared to YOLOv5, YOLOv8n, and the baseline model, with a minor 0.9% recall (R) deficit compared to YOLOv5 but more balanced overall performance and superior capability for small-object detection. By fusing the two improved modules, the proposed system successfully realizes autonomous charging navigation, providing an efficient solution for energy management in intelligent vehicles in real-world environments.

Keywords: ROS system; YOLOv11n; bidirectional A*; corridor environment; path planning.

Figure 1

Framework for an autonomous navigation and charging system.

Figure 2

Charging pile and smart-vehicle signal docking.

Figure 3

Effect diagram representing the redundant node-pruning strategy.

Figure 4

Graphs showing comparison of optimization performance between two algorithms.

Figure 5

Schematic diagram of the target-detection network architecture based on the CAFM Fusion module.

Figure 6

CAFM Fusion model.

Figure 7

CAFM module.

Figure 8

Enhancement of image effects.

Figure 9

Accuracy of target-object detection.

Figure 10

Heatmaps.

Figure 11

Target-recognition accuracy.

Figure 12

Real-world environment images.

Figure 13

Visualization of path planning.