Autonomous Mobile Robot Navigation Using Lower Resolution Grids and PID-Based Pure Pursuit Controller

In the modern era, mobile robots are gaining special attention in various intralogistics operations such as warehousing, manufacturing, electrical high-voltage substations, roads, etc. These vehicles should be capable of effectively recognizing their routes, avoiding singularities and obstacles, and making a decision according to the environment. Hence an advanced control mechanism is required for path planning and navigation to work effectively in that dynamic environment. Keeping insight, into the challenges faced by the mobile robot this study aims to develop path planning using global (A* and Dijkstra) and local planners (Pure Pursuit) in a 2D navigation system, utilizing g-mapping for Simultaneous localization and mapping (SLAM) and Adaptive Monte Carlo Localization (AMCL) for probabilistic localization, to solve this issue. The system is designed to be compatible with the Robot Operating System (ROS) ecosystem. Path planning is carried out on a lower-resolution grid covering the navigable areas and the Pure Pursuit approach is enriched with a Proportional-integral-derivative (PID) controller. The results have shown that proposed schemes give superior performance in challenging obstacle-based warehouse systems, compared to publicly available ROS navigation planners.