Sensor stabilization, localization, obstacle detection, and path planning for autonomous rovers: A case study

Autonomous rovers are the next step in exploration of terrestrial planets. Current rovers contain some forms of semi-autonomy, but many functions are still performed by remote human operators. As the distance between Earth and the exploration target increases, communication delays will make teleoperation of rover platforms increasingly difficult. Through the use of autonomous systems, operators may give mission parameters to autonomous exploration rovers and allow onboard systems to carry out the task. In addition, if future exploration requires a repetitive task, such as resource gathering, autonomous rovers represent the best technology for the job. Autonomous rovers face many challenges. Among them are sensor stabilization, localization, obstacle detection, and path planning.;This thesis describes an approach for each of the above mentioned challenges. Sensor stabilization was performed using an inertial measurement unit (IMU) and the reverse angle method of stabilization. A 2D Light Detection and Ranging (LIDAR) sensor provided input data for a landmark-based localization algorithm. The same LIDAR unit was actuated to perform 3D scans used in an obstacle detection method based upon ground plane removal, via random sample consensus (RANSAC), and Euclidean Clustering. A modified A* algorithm was used as an occupancy grid-based path planner. The approaches were verified through implementation on the University of Alabama Modular Autonomous Robotic Terrestrial Explorer (MARTE) platform as part of the 2014 NASA Robotic Mining Competition.