Intelligent vision-based navigation for mobile robots in unknown indoor environments using neural networks
TL;DR
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.
Abstract
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.