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This paper proposed a randomized hybrid system approach for planning the paths and measurements of a network of robotic sensors deployed for searching and classifying objects in a partially-observed environment containing multiple obstacles and multiple targets. The sensor planning problem considered in this paper consists of coordinating and planning the motions of each robot, equipped with two on-board sensing capabilities. One sensing capability is assumed to have low classification accuracy and a large field of view (FOV), and the other is assumed to have high classification accuracy and a smaller FOV. A sampling function for rapidly-exploring trees is presented that takes into account both sensor measurements of obstacle locations and robot configuration and velocity to generate new milestones for the tree online. The tree expansion also takes into account the expected information value of the targets, represented by conditional mutual information, in order to favor expansions toward targets with higher measurement benefit. The proposed method is implemented and demonstrated on a network of robotic sensors simulated using the 3D physics-based robotics software packages Player/Stage/Gazebo.