Real-Time Computational Scheduling with Path Planning for Autonomous Mobile Robots

With the advancement in technology, modern autonomous vehicles are required to perform more complex tasks and navigate through challenging terrains. Thus, the amount of computation resources to accurately accomplish those tasks have exponentially grown in the last decade. With growing computational intensity and limited computational resources on embedded devices, schedulers are necessary to manage and fully optimize computational loads between the GPU and CPU as well as reducing the power consumption to maximize time in the field. Thus far, it has been proven the effectiveness of schedulers and path planners on computational load on embedded devices through numerous bench testing and simulated environments. However, there have not been any significant data collection in the real-world with all hardware and software combined. This thesis focuses on the implementation of various computational loads (i.e. scheduler, path planner, RGB-D camera, object detection, depth estimation, etc.) on the NVIDIA Jetson AGX Xavier and real-world experimentation on the Clearpath Robotics Jackal. We compare the computation response time and effectiveness of all systems tested in the real-world versus the same software and hardware architecture on the bench.