We propose an informative path planning (IPP) algorithm for active
classification using an unmanned aerial vehicle (UAV), focusing on weed
detection in precision agriculture. We model the presence of weeds on farmland
using an occupancy grid and generate plans according to information-theoretic
objectives, enabling the UAV to gather data efficiently. We use a combination
of global viewpoint selection and evolutionary optimization to refine the UAV's
trajectory in continuous space while satisfying dynamic constraints. We
validate our approach in simulation by comparing against standard "lawnmower"
coverage, and study the effects of varying objectives and optimization
strategies. We plan to evaluate our algorithm on a real platform in the
immediate future.Comment: 7 pages, 4 figures, submission to International Symposium on
Experimental Robotics 201
