Path Planning Analysis for Automated Guided Vehicle

Automated Guided Vehicles enhance industrial automation through streamlined operations that decrease labour use while improving productivity at lower expense with safer work conditions. The success of automated guided vehicles depends on good path planning methods that work well in changing and complex maps. Regular computing approaches fail to react quickly to changes in their setting which causes operational difficulties and safety concerns. This research develops dynamic path planning systems that adapt to environment changes while building better AGV routes and navigation systems using A* and Dijkstra Algorithm. The research examines present methods to compare the performance between Dijkstra and A* algorithm in different indoor environment that simulates industrial environment in creating better solutions. Both path planning methods are compared based on its capability in avoiding obstacles to arrive at it destined goals. Our tests show through Gazebo, and Rviz simulations that AGVs with Dijkstra algorithm travel faster on optimized paths with less traffic delays and time consumption from the starts to the goal positions.