Waypoint reduction to improve autonomous navigation using deep neural networks and path planners

Safe and efficient navigation is critical for a mobile robot in a highly constrained workspace. Autonomous navigation is to be performed safely and robustly in the environment map with obstacles of various geometric shapes, sizes and colors positioned at random locations. In this paper, we present an approach to perform autonomous navigation by detecting the obstacles in the map and by generating auxiliary collision-free waypoints in obstacle-free space map. In achieving this, the object detection is done using SSDMobileNetV2 model and auxiliary collision-free navigation waypoints are generated using the Deepway neural network model. Further, the RRT path planner is applied to analyze the waypoints generated and to find the a global path between start and goal locations. An optimal local path is achieved using the A* path planner. Extensive simulations of various scenarios are performed and the proposed model is evaluated. The results reveal that the proposed model achieves significant improvements in terms of time, distance and F1-score.