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This paper proposes a Bayesian approach for minimizing the time of finding an object of uncertain location and dynamics using several moving sensing agents with constrained dynamics. The approach exploits twice the Bayesian theory: on one hand, it uses a Bayesian formulation of the objective functions that compare the constrained paths of the agents and on the other hand, a Bayesian optimization algorithm to solve the problem. By combining both elements, our approach handles successfully this complex problem, as illustrated by the results over different scenarios presented and statistically analyzed in the paper. Finally, the paper also discusses other formulations of the problem and compares the properties of our approach with others closely related.

This paper proposes a new method, oriented to crop row detection in images from maize fields with high weed pressure. The vision system is designed to be installed onboard a mobile agricultural vehicle, i.e. submitted to gyros, vibrations and undesired movements. The images are captured under image perspective, being affected by the above undesired effects. The image processing consists of three main processes: image segmentation, double thresholding, based on the Otsu's method, and crop row detection. Image segmentation is based on the application of a vegetation index, the double thresholding achieves the separation between weeds and crops and the crop row detection applies least squares linear regression for line adjustment. Crop and weed separation becomes effective and the crop row detection can be favorably compared against the classical approach based on the Hough transform. Both gain effectiveness and accuracy thanks to the double thresholding that makes the main finding of the paper.

This paper presents an approach to trajectories optimization for Unmanned Aerial Vehicle (UAV) in presence of obstacles, waypoints, and threat zones such as radar detection regions, using Mixed Integer Linear Programming (MILP). The main result is the linear approximation of a nonlinear radar detection risk function with integer constraints and indicator 0-1 variables. Several results are presented to show that the approach can yields trajectories depending on the acceptable risk of detection.

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