This work developed and evaluated a human-machine interface for the control of a roadable air vehicle (RAV), capable of surface driving, vertical takeoff, sustained flight, and landing. Military applications seek to combine the benefits of ground and air vehicles to maximize flexibility of movement but require that the operator have minimal pilot training. This makes the operator vulnerable to automation complexity issues; however, the operator will expect to be able to interact extensively and control the vehicle during flight. A mixed-initiative control approach mitigates these vulnerabilities by integrating the operator into many complex control domains in the way that they often expect---flexibly in charge, aware, but not required to issue every command. Intrinsic safety aspects were evaluated by comparing performance, decision making, precision, and workload for three RAV control paradigms: human-only, fully automated, and mixed-initiative control. The results suggest that the mixed-initiative paradigm leverages the benefits of human and automated control while also avoiding the drawbacks associated with each.

Shipdeck landing is one of the most challenging tasks for a rotorcraft. Current autonomous rotorcraft use shipdeck mounted transponders to measure the relative pose of the vehicle to the landing pad. This tracking system is not only expensive but renders an unequipped ship unlandable. We address the challenge of tracking a shipdeck without additional infrastructure on the deck. We present two methods based on video and lidar that are able to track the shipdeck starting at a considerable distance from the ship. This redundant sensor design enables us to have two independent tracking systems. We show the results of the tracking algorithms in three different environments - field testing results on actual helicopter flights, in simulation with a moving shipdeck for lidar based tracking and in laboratory using an occluded, and, moving scaled model of a landing deck for camera based tracking. The complimentary modalities allow shipdeck tracking under varying conditions.

Currently deployed unmanned rotorcraft rely on carefully preplanned missions and operate from prepared sites and thus avoid the need to perceive and react to the environment. Here we consider the problems of finding suitable but previously unmapped landing sites given general coordinates of the goal and planning collision free trajectories in real time to land at the “optimal” site. This requires accurate mapping, fast landing zone evaluation algorithms, and motion planning. We report here on the sensing, perception and motion planning integrated onto a full-scale helicopter that flies completely autonomously. We show results from 8 experiments for landing site selection and 5 runs at obstacles. These experiments have demonstrated the first autonomous full-scale helicopter that successfully selects its own landing sites and avoids obstacles.

We consider the perceptual challenges inherent in the robotic manipulation of previously unseen socks, with the end goal of manipulation by a household robot for laundry. The task poses challenging problems in modeling the appearance, shape and configuration of these textile items that tend to exhibit high variability in texture, design, and style while being highly articulated objects.

Safe autonomous flight is essential for widespread acceptance of aircraft that must fly close to the ground. We have developed a method of collision avoidance that can be used in three dimensions in much the same way as autonomous ground vehicles that navigate over unexplored terrain. Safe navigation is accomplished by a combination of online environmental sensing, path planning and collision avoidance. Here we report results with an autonomous helicopter that operates at low elevations in uncharted environments some of which are densely populated with obstacles such as buildings, trees and wires. We have recently completed over 1000 successful runs in which the helicopter traveled between coarsely specified waypoints separated by hundreds of meters, at speeds up to 10 meters/sec at elevations of 5-10 meters above ground level. The helicopter safely avoids large objects like buildings and trees but also wires as thin as 6 mm. We believe this represents the first time an air vehicle has traveled this fast so close to obstacles. Here we focus on the collision avoidance method that learns to avoid obstacles by observing the performance of a human operator.

This paper describes a low-cost experimental platform for investigating control and dynamics of a vehicle performing high speed sliding turns in an off-road environment. The hardware design and field performance of the vehicle are discussed. State and control input data were recorded during a series of human-controlled off-road driving maneuvers. Analysis performed on the data demonstrates the ability to detect slippage and measure sideslip angle. Preliminary classification of human control inputs using pattern recognition techniques shows the ability to match steering inputs with vehicle trajectories that can be used to develop motion primitives for vehicle control. These tools and techniques will be used for the development of high speed autonomous off-road driving.