In this work, we consider the optimal path of a fixed-wing unmanned aerial vehicle (UAV) tracking a mobile surface target. One of the limitations of fixed-wing UAVs in tracking mobile targets is the lack of hovering capability when the target moves much slower than the minimum UAV speed, requiring the UAV maintain an orbit about the target. In this paper, we propose a method to find the optimal policy for fixed-wing UAVs to minimize the location uncertainty of a mobile target. Using a grid-based Markov Decision Process (MDP), we use an off-line policy iteration algorithm to find an optimal UAV path in a coarse discretized state space, followed by an on-line policy iteration algorithm that applies a finer grid MDP to the region of interest to find the final optimal UAV trajectory. We validate the proposed algorithm using computer simulations. Comparing the simulation results with other methods, we show that the proposed method has up to 13% decrease in error uncertainty than ones resulted using conventional methods.

Our work presents solutions to two related vexing problems in feature-based localization of ground targets in Unmanned Aerial Vehicle (UAV) images: (i) A good initial guess at the pose estimate that would speed up the convergence to the final pose estimate for each image frame in a video sequence; and (ii)Time-bounded estimation of the position of the ground target. We address both these problems within the framework of the Iterative Closest Point (ICP) algorithm that now has a rich tradition of usage in computer vision and robotics applications. We solve the first of the two problems by frame-to-frame propagation of the computed pose estimates for the purpose of the initializations needed by ICP. The second problem is solved by terminating the iterative estimation process at the expiration of the available time for each image frame. We show that when frame-to-frame homography is factored into the iterative calculations, the accuracy of the position calculated at the time of bailing out of the iterations is nearly always sufficient for the goals of UAV vision.

This paper describes our on-going efforts toward developing heterogeneous, cooperative systems technologies. In particular, we present the role of unmanned mobile ground systems (robots) in a heterogeneous sensor network, consisting of two unmanned aircraft, a mobile ground robot, and a set of four stationary ground sensors, performing an intelligence, surveillance, and reconnaissance (ISR) mission. The unmanned mobile ground robot is equipped with an infrared (IR) sensor, the aircraft and the stationary ground sensors use optical cameras and radio frequency (RF) detectors, respectively. The primary responsibility of the mobile ground robot is to verify the identity of a target based on its IR signature. In addition, the mobile ground robot also assists with the sensor network's overall target localization estimation efforts by sharing its IR sensor-based target location measurements with members of the sensor network. Our analysis and field experiments demonstrated scalability and effectiveness of our approach.

By active stereo we mean a stereo vision system that allows for independent panning and tilting for each of the two cameras. One advantage of active stereo in relation to regular stereo is the former's wider effective field of view; if an object is too close to the camera baseline, the depth to the object can still be estimated accurately by panning the cameras appropriately. Another advantage of active stereo is that it can yield a larger number of depth measurements simultaneously for each position of the platform on which the camera system is mounted. Panning and tilting over a large angular range, while being the main reason for the advantages of active stereo, also make it more challenging to calibrate such systems. For a calibration procedure to be effective for active stereo, the estimated parameters must be valid over the entire range of the pan and tilt angles. This paper presents a new approach to the calibration of such vision systems. Our method is based on the rationale that an active stereo calibration procedure must explicitly estimate the locations and the orientations of the pan and tilt rotating axes for the cameras through a closed-form solution. When these estimates for the axes are combined with the homogeneous transform relationships that link the various coordinate frames, we end with a calibration that is valid over a large variation in the pan and tilt angles.

This paper presents an efficient person tracking algorithm for a vision-based mobile robot using two independently moving cameras each of which is mounted on its own pan/tilt unit. Without calibrating these cameras, the goal of our proposed method is to estimate the distance to a target appearing in the image sequences captured by the cameras. The main contributions of our approach include: 1) establishing the correspondence between the control inputs to the pan/tilt units and the pixel displacement in the image plane without using the intrinsic parameters of the cameras; and 2) derivation of the distance information from the correspondence between the centers of masses of the segmented color-blobs from the left and the right images without stereo camera calibration. Our proposed approach has been successfully tested on a mobile robot for the task of person following in real environments.