Chad Goerzen

A Survey of Motion Planning Algorithms from the Perspective of Autonomous UAV Guidance

A fundamental aspect of autonomous vehicle guidance is planning trajectories. Historically, two fields have contributed to trajectory or motion planning methods: robotics and dynamics and control. The former typically have a stronger focus on computational issues and real-time robot control, while the latter emphasize the dynamic behavior and more specific aspects of trajectory performance. Guidance for Unmanned Aerial Vehicles (UAVs), including fixed- and rotary-wing aircraft, involves significant differences from most traditionally defined mobile and manipulator robots. Qualities characteristic to UAVs include non-trivial dynamics, three-dimensional environments, disturbed operating conditions, and high levels of uncertainty in state knowledge. Otherwise, UAV guidance shares qualities with typical robotic motion planning problems, including partial knowledge of the environment and tasks that can range from basic goal interception, which can be precisely specified, to more general tasks like surveillance and reconnaissance, which are harder to specify. These basic planning problems involve continual interaction with the environment. The purpose of this paper is to provide an overview of existing motion planning algorithms while adding perspectives and practical examples from UAV guidance approaches.

Autonomous Black Hawk in Flight: Obstacle Field Navigation and Landing-site Selection on the RASCAL JUH-60A

This paper describes the development and flight test of autonomous obstacle field navigation and safe landing area selection on the U.S. Army Aeroflightdynamics Directorate RASCAL JUH-60A research helicopter. Using laser detection and ranging LADAR as the primary terrain sensor, the autonomous flight system is able to avoid obstacles, including wires, and select safe landing sites. An autonomous integrated landing zone approach profile was developed and validated that integrates cruise flight, low-level terrain flight, and approach to a safe landing spot determined on the fly. Results are presented for a range of sites and conditions. Approximately 750i¾?km of autonomous flight was performed, 230i¾?km of which was at low altitude in mountainous terrain using the obstacle field navigation system. This is the first time a full-scale helicopter has been flown fully autonomously a significant distance in low-level flight over complex terrain, basing its planning solely on sensor data gathered from an onboard sensor. These flights demonstrate tight integration between terrain avoidance, control, and autonomous landing.

Application of a Nonlinear Recursive Visual-Depth Observer Using UH60 Flight Data

This paper considers the problem of nearness (inverse of the distance to terrain) estimation from optical flow observed by a downward-looking camera attached to a helicopter undergoing known motion. A recursive nonlinear-observer framework is developed by combining online body-state estimates with pixel-based representation of optical flow for dense depth-map estimation. Lyapunov-stability analysis is considered under the assumption of bounded nearness, and global exponential convergence is demonstrated over observable regions of the image plane, which is in contrast with extended Kalman-filter-based estimation schemes that preclude guarantee of convergence. The observer estimates converge rapidly to ground-truth estimates, and the performance of the observer is shown to be similar to the extended Kalman filter, while being more computationally efficient and more robust to model error. The rapid convergence and accuracy of nearness estimation allied with the computational efficiency of the recursive obser...

Guidance performance benchmarking for autonomous rotorcraft

Berenice Mettler∗ Zhaodan Kong Chad Goerzen Matthew Whalley Interactive Guidance and Control Lab San Jose State University Aeroflightdynamics Directorate (AMRDEC) Department of Aerospace Engineering and Mechanics Research Foundation U.S. Army Research, Development University of Minnesota, Twin Cities, MN Ames Research Center Engineering Command Moffett Field, CA Ames Research Center Moffett Field, CA