doctoral dissertation, tech. report CMU-RI-TR-11-03, Robotics Institute, Carnegie Mellon University, May, 2011
Currently deployed unmanned rotorcraft rely on preplanned missions or teleoperation and do not actively incorporate information about obstacles, landing sites, wind, position uncertainty, and other aerial vehicles during online motion planning. Prior work has successfully addressed some tasks such as obstacle avoidance at slow speeds, or landing at known to be good locations. However, to enable autonomous missions in cluttered environments, the vehicle has to react quickly to previously unknown obstacles, respond to changing environmental conditions, and find unknown landing sites.
We consider the problem of enabling autonomous operation at low-altitude with contributions to four problems. First we address the problem of fast obstacle avoidance for a small aerial vehicle and present results from over a 1000 runs at speeds up to 10 m/s. Fast response is achieved through a reactive algorithm whose response is learned based on observing a pilot. Second, we show an algorithm to update the obstacle cost expansion for path planning quickly and demonstrate it on a micro aerial vehicle, and an autonomous helicopter avoiding obstacles.
Next, we examine the mission of finding a place to land near a ground goal. Good landing sites need to be detected and found and the final touch down goal is unknown. To detect the landing sites we convey a model based algorithm for landing sites that incorporates many helicopter relevant constraints such as landing sites, approach, abort, and ground paths in 3D range data. The landing site evaluation algorithm uses a patch-based coarse evaluation for slope and roughness, and a fine evaluation that fits a 3D model of the helicopter and landing gear to calculate a goodness measure. The data are evaluated in real-time to enable the helicopter to decide on a place to land. We show results from urban, vegetated, and desert environments, and demonstrate the first autonomous helicopter that selects its own landing sites.
We present a generalized planning framework that enables reaching a goal point, searching for unknown landing sites, and approaching a landing zone. In the framework, sub-objective functions, constraints, and a state machine define the mission and behavior of an UAV. As the vehicle gathers information by moving through the environment, the objective functions account for this new information. The operator in this framework can directly specify his intent as an objective function that defines the mission rather than giving a sequence of pre-specified goal points. This allows the robot to react to new information received and adjust its path accordingly. The objective is used in a combined coarse planning and trajectory optimization algorithm to determine the best path the robot should take. We show simulated results for several different missions and in particular focus on active landing zone search.
We presented several effective approaches for perception and action for low-altitude flight and demonstrated their effectiveness in field experiments on three autonomous aerial vehicles: a 1m quadrocopter, a 3.6m helicopter, and a full-size helicopter. These techniques permit rotorcraft to operate where they have their greatest advantage: In unstructured, unknown environments at low-altitude.
|UAV, UAS, MAV, Obstacle Avoidance, Collision Avoidance, Landing Zone Selection, Search|
Associated Center(s) / Consortia:
Field Robotics Center
Associated Lab(s) / Group(s): Air Lab
Associated Project(s): Low-Flying Air Vehicles
|Sebastian Scherer, "Low-Altitude Operation of Unmanned Rotorcraft," doctoral dissertation, tech. report CMU-RI-TR-11-03, Robotics Institute, Carnegie Mellon University, May, 2011|
author = "Sebastian Scherer",
title = "Low-Altitude Operation of Unmanned Rotorcraft",
booktitle = "",
school = "Robotics Institute, Carnegie Mellon University",
month = "May",
year = "2011",
address= "Pittsburgh, PA",
|The Robotics Institute is part of the School of Computer Science, Carnegie Mellon University.|
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