This paper presents an innovative method for estimating wind velocity using an optical flow sensor mounted on a miniature air vehicle (MAV). Using the flow of features measured by the optical flow sensor in the longitudinal and lateral directions, the MAV's crab-angle is estimated. By combining the crab-angle with measurements of ground track from GPS and the MAV's airspeed, the wind velocity is computed. Our algorithm allow this computation to be performed in real time on board an MAV. Unlike previous techniques, this approach does not require the use of magnetometers. This algorithm has been implemented and its effectiveness demonstrated through experimental test results.

Unmanned Aerial Vehicles in the "mini" to "micro" size ranges (<6 foot wingspan) are becoming increasingly popular platforms for the collection of video data about an area of interest. Currently, mini-UAV platforms transmit video back to the end user using analog RF transmitters. Transmission of digital video, although it is more desirable on a mini-UAV platform, requires video compression to be performed. Unfortunately, determining the motion information required for digital video compression is a computationally expensive procedure. Hardware capable of performing this task is typically too heavy, too large, and too power intensive to be carried on a mini-UAV.We present an efficient algorithm for solving the motion estimation problem which requires far less computation, making it feasible for implementation on-board a mini-UAV. We reduce the amount of computation necessary by using (1) knowledge of camera locations (from available mini-UAV sensor data) and (2) the projective geometry of the camera. We present encouraging initial results, showing that this method reduces computations by almost two orders of magnitude compared to standard methods, and yields comparable or improved compression results.

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This paper presents an efficient conflict resolution method for multiple aerial vehicles based on speed planning. The problem is assigning a speed profile to each aerial vehicle in real time such that the separation between them is greater than a minimum safety value and the total deviation from the initial planned trajectories is minimized. Also, the arrival time of each aerial vehicle at the end waypoint of the trajectory is taken into account to solve the conflicts. The proposed method involves the use of appropriate airspace discretization. The method consists of two steps: a search tree step, which finds if it exists a solution; and an optimization step by solving a QP-problem, which minimizes a cost function. The paper also presents simulations for several scenarios and experiments that have been carried out in the multivehicle aerial testbed of the Center for Advanced Aerospace Technologies (CATEC).

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