Autonomous Landing

Landing on a static target: This research deals with the design and implementation of a real-time, vision-based landing algorithm for an autonomous helicopter. We use vision for precise target detection and recognition, and a combination of vision and GPS for navigation. The helicopter updates its landing target parameters based on vision and uses an onboard behavior-based controller to follow a path to the landing site. The corresponding video shows the detection of a landing pad, navigation towards it, tracking and finally autonomous landing.

Tracking a moving target and landing on it: In this research we focus on the problem of tracking and landing on a moving target using an autonomous helicopter as a platform. We track a moving target using a downward looking camera mounted on a helicopter. Based on the tracking information, our controller is able to generate appropriate control commands for the helicopter in order to land it on target.

Aerial Deployment

GPS-based sensor deployment: This research deals with precision deployment of objects using an autonomous helicopter. The main sensor used for deployment is GPS. The helicopter is given a either a single pre-surveyed location or a set of way-points on which to deploy. It is required to navigate from an initial position to the given location(s) and deploy. The deployment position is actively estimated using Newton's laws of motion. Experimental results from flight tests are presented which show that the deployment is consistent and accurate to approximately 1.5 meters.

Vision-based sensor deployment: The above research is extended to use vision as the primary sensor for deployment. This involves detection of a target, tracking of the target and autonomous deployment. The target is actively tracked and a sensor is deployed.

3D Navigation

Experiments in urban canyon flight: The aim of this research is to allow the helicopter to detect obstacles in the environment, and avoid them. This capability is vital when flying autonomously in environments such as built-up urban areas. Our goal is to have the helicopter fly autonomously down an 'Urban Canyon' without colliding into any buildings. Such a capability would make tasks such as urban search and rescue and surveillance possible. Omnidirectional vision and optic-flow techniques are being employed to achieve this goal.

The helicopter was flown down a street in an urban test area (Ft Benning, Georgia) to capture images from the three cameras mounted onboard. Omnidirectional camera and two sideways looking cameras mounted to the front of the helicopter.

Left Camera	Omnidirectional Camera	Right Camera

Autonomous Flight

	Hovering: This video shows autonomous hovering of the helicopter and control gain tuning. During the first few seconds of this clip, the gains are being tuned, and then when the correct gains are used, the helicopter performs very stable autonomous flight.
	Summary of past projects (upto 2000): These are an amalgamation of past projects which include air-ground cooperation, Differential GPS way-point following, behavior-based control of the helicopter etc.