Date of Award
6-2015
Access Type
Thesis - Open Access
Degree Name
Master of Science in Aerospace Engineering
Department
Graduate Studies
Committee Chair
Dr. Richard Prazenica
First Committee Member
Dr. Hever Moncayo
Second Committee Member
Dr. Sathya Gangadharan
Abstract
In recent years, the capabilities and potential value of unmanned autonomous systems (UAS) to perform an extensive variety of missions have significantly increased. It is well comprehended that there are various challenges associated with the realization of autonomous operations in complex urban environments. These difficulties include the requirement for precision guidance and control in conceivably GPS-denied conditions as well as the need to sense and avoid stationary and moving obstructions within the scene. The small size of some of these vehicles restricts the size, weight and power consumption of the sensor payload and onboard computational processing that can accommodated by UAS.
This thesis analyzes the development and implementation of terrain mapping, path planning and control algorithms on an unmanned ground vehicle. Data from GPS, IMU and LIDAR sensors are fused in order to compute and update a dense 3D point cloud that is used by an implicit terrain algorithm to provide detailed mathematical representations of complex 3D structures generally found in urban environments. A receding horizon path planning algorithm is employed to adaptively produce a kinematically-feasible path for the unmanned ground vehicle. This path planning algorithm incorporates obstacle avoidance constraints and provides a set of waypoints to be followed by the unmanned ground vehicle. A waypoint controller is designed and implemented to enable the vehicle to follow the waypoints from the path planner. Open-loop experiments are provided with an unmanned ground vehicle in order to demonstrate terrain generation with real sensor data. Closed-loop results are then presented for a simulated ground vehicle in order to demonstrate the performance of the receding horizon path planning and control algorithms using the terrain map generated from the open-loop experiments.
Scholarly Commons Citation
Singh, Gurasis, "Sensor Fusion and Obstacle Avoidance for an Unmanned Ground Vehicle" (2015). Doctoral Dissertations and Master's Theses. 248.
https://commons.erau.edu/edt/248
