Robotics Automation and Adaptive Motion Planning: A Hybrid Approach using AutoNav, LIDAR-based SLAM, and DenseNet with Leaky ReLU

摘要

This study presents a hybrid robotics automation system that optimizes real-time navigation, obstacle avoidance, and environmental mapping under difficult situations by combining AutoNav, LIDAR-based SLAM, and DenseNet with Leaky ReLU for robust motion planning. The main goals are to use AutoNav to enable autonomous navigation with optimal path planning, LIDAR-based SLAM to accomplish precise mapping and localization, and DenseNet and Leaky ReLU to improve motion planning and obstacle avoidance. Through the integration of various technologies, the study suggests an effective and flexible system for robotic operations in dynamic situations in real time. In order to provide the best trajectory planning and accurate obstacle recognition, the system constantly modifies motion planning based on real-time sensor data. Performance results show that the system outperforms individual parts, with a 95% gradient efficiency, a localization error of only 0.045 meters, and a pathfinding accuracy of 97.5%, the system's performance results illustrate its superiority over individual parts and highlight its real-time adaptability and optimization accuracy. For the future of robotics, this hybrid system offers breakthroughs in real-time decision-making and nimble autonomous robotic navigation.