Sensor-based Multi-waypoint Autonomous Robot Navigation with Graph-based Models

Rogers, H., Sellers, T., Lei, T., Carruth, D. W., & Luo, C. (2023). Sensor-based Multi-waypoint Autonomous Robot Navigation with Graph-based Models. Proc. SPIE 12540, Autonomous Systems: Sensors, Processing, and Security for Ground, Air, Sea, and Space Vehicles and Infrastructure 2023. Orlando, FL, USA: SPIE. 12540. DOI:10.1117/12.2663830.

Abstract

Multi-waypoint navigation for autonomous robot is in high demand in real-world robotics applications including search and rescue, disaster response, and environment exploration. In this paper, a sensor-based methodology is proposed for validation of autonomous robot multi-waypoint navigation utilizing graph-based models with adjacent node selection. In addition to time and distance efficiency, the proposed graph-based models incorporate safety in relation to the robot's environment as a driving feature. These models provide path planning for autonomous robot experiments. This methodology is implemented in a real-world environment with simulated dynamic obstacles utilizing a Clearpath Jackal unmanned ground vehicle (UGV) featuring four-wheel drive, GPS, odometry, and a Velodyne VLP-16 LiDAR as the experimental robot configuration. The LiDAR unit utilized has a one hundred meter range with a thirty degree vertical field of view, as the primary navigation sensor. It is utilized to map the environment prior to performing autonomous navigation experiments. This robot configuration is also presented in a simulated environment for more thorough evaluation. The environments to be examined are controlled with obstacles and environment features within the expected view of the LiDAR. The proposed graphbased model is evaluated in terms of planning efficiency from simulated to real-world environments including dynamic obstacle avoidance.