Hierarchical Off-Road Path Planning and Its Validation Using a Scaled Autonomous Car'

In the last few years. while a lot of research effort has been s p t on autonomous vehicle navigation, primarily focused on on-road vehicles, off-road path plann ing still presents new challenges. Path planning for an autonomous ground vehicle over a large horizon in an un str ctured environment when high-resolution a-priori information is available, is still very much an ope n problem due to the computations involved. Localization and control of an autonomous vehicle and how the cont rol algorithms interact with the path planner is a complex task. The first part of this research details the d ev lopment of a path decision support tool for off-road application implementing a novel hierarchical pa th planning framework and verification in a simulation environment. To mimic real world issues, like communic ation delay, sensor noise, modeling error, etc., it was important that we validate the framework in a real envi ronment. In the second part of the research, development of a scaled autonomous car as part of a real experim ental environment is discussed which provides a compromise between cost as well as implementation complex ities compared to a full-scale car. The third part of the research, explains the development of a vehiclein-loop (VIL) environment with demo examples to illustrate the utility of such a platform. Our proposed path planning algorithm mitigates the challen ge of high computational cost to find the optimal path over a large scale high-resolution map. A globa l path planner runs in a centralized server and uses Dynamic Programming (DP) with coarse information to cr eate an optimal cost grid. A local path planner utilizes Model Predictive Control (MPC), running on-board , using the cost map along with high-resolution information (available via various sensors as well as V2V co mmunication) to generate the local optimal path. Such an approach ensures the MPC follows a global optimal pat h while being locally optimal. A central server efficiently creates and updates route critical infor mation available via vehicle-to-infrastructure(V2X) communication while using the same to update the prescribed global cost grid. For localization of the scaled car, a three-axis inertial me asurement unit (IMU), wheel encoders, a global positioning system (GPS) unit and a mono-camera are m ounted. Drift in IMU is one of the major issues ii which we addressed in this research besides developing a low -level controller which helped in implementing the MPC in a constrained computational environment. Using a camera and tire edge detection algorithm we have developed an online steering angle measurement pack age as well as a steering angle estimation algorithm to be utilized in case of low computational resour ces. We wanted to study the impact of connectivity on a fleet of vehi cl s running in off-road terrain. It is costly as well as time consuming to run all real vehicles. A lso some scenarios are difficult to recreate in real but need a simulation environment. So we have develop ed a vehicle-in-loop (VIL) platform using a VIL simulator, a central server and the real scaled car to com bine the advantages of both real and simulation environment. As a demo example to illustrate the utility of V IL platform, we have simulated an animal crossing scenario and analyze how our obstacle avoidance al gorithms performs under different conditions. In the future it will help us to analyze the impact of connectivi ty on platoons moving in off-road terrain. For the vehicle-in-loop environment, we have used JavaScript Obje ct Notation (JSON) data format for information exchange using User Datagram Protocol (UDP) for implementi ng Vehicle-to-Vehicle (V2V) and MySQL server for Vehicle-to-Infrastructure (V2I) communicatio n.