  Visual exploration and smart data collection via autonomous vehicles is an
attractive topic in various disciplines. Disturbances like wind significantly
influence both the power consumption of the flying robots and the performance
of the camera. We propose a reinforcement learning approach which combines the
effects of the power consumption and the object detection modules to develop a
policy for object detection in large areas with limited battery life. The
learning model enables dynamic learning of the negative rewards of each action
based on the drag forces that is resulted by the motion of the flying robot
with respect to the wind field. The algorithm is implemented in a near-real
world simulation environment both for the planar motion and flight in different
altitudes. The trained agent often performed a trade-off between detecting the
objects with high accuracy and increasing the area coverage within its battery
life. The developed exploration policy outperformed the complete coverage
algorithm by minimizing the traveled path while finding the target objects. The
performance of the algorithms under various wind fields was evaluated in planar
and 3D motion. During an exploration task with sparsely distributed goals and
within a UAV's battery life, the proposed architecture could detect more than
twice the amount of goal objects compared to the coverage path planning
algorithm in moderate wind field. In high wind intensities, the energy-aware
algorithm could detect 4 times the amount of goal objects when compared to its
complete coverage counterpart.
