Event Location: NSH 1109

Abstract: Currently deployed unmanned aerial vehicles (UAVs) 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 trajectory planning. Prior work has successfully addressed some problems 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 address 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 behavior is learnt based on observing a pilot example. Second, we show an efficient algorithm to update the obstacle expansion for path planning and show results from 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 demonstrated the first autonomous helicopter that selects its own landing sites.

Last, we present a framework that using the same planning algorithm 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 user in this framework specifies a function that defines the mission rather than giving a sequence of prespecified goal points. The objective is used in a combined planning and trajectory optimization algorithm to determine the best path the robot should take. We show simulated results for several different missions.

We presented several effective approaches for perception and action at low-altitude and demonstrated their effectiveness in field experiments on three autonomous aerial vehicles: a 1m quadrotor, a 3.6m helicopter, and a full-size helicopter. These techniques enable rotorcraft to operate in unstructured, unknown environments at low-altitude.

Committee:Sanjiv Singh, Chair

Takeo Kanade

Alonzo Kelly

Emilio Frazzoli, Massachusetts Institute of Technology