LiDAR-Driven Navigation in Dynamic Indoor Environments: Optimizing Performance and Reliability

Autonomous systems face difficulties in movements in dynamically changing environments mainly due to constantly moving obstacles. Some of the existing limitations of existing systems include the inability to accommodate optimum levels of real-time changes and efficiency at moderate costs. To tackle these issues, this paper presents a new LiDAR-driven navigation system that enables real-world self-driving mapping and control. The system focuses on increasing path planning to avoid the impediments, which are essential for dynamic environments. The study evaluates the proposed system through four research questions that focus on assessing its accuracy and efficiency in varied crowd densities, its performance and reliability across various room settings, and its effectiveness in recovering from collisions. Results indicate that the system maintains high navigation accuracy and efficiency in less crowded environments but faces challenges as obstacle density increases. The system shows general reliability, though its collision recovery effectiveness diminishes in more complex or crowded settings. These findings suggest that the system is well-suited for environments like warehouses, where navigating dynamic obstacles is critical. Overall, the proposed system demonstrates substantial potential to enhance autonomous navigation in dynamic indoor environments