Chenghui Cai

TL;DR: A novel approximate cell-decomposition method in which obstacles, targets, sensor's platform, and FOV are represented as closed and bounded subsets of an Euclidean workspace, and these strategies outperform shortest path, complete coverage, random, and grid search strategies.

TL;DR: A methodology is developed to deploy a mobile sensor network for the purpose of detecting and capturing mobile targets in the plane using a new cell decomposition approach to formulate the probability of detection and the cost of operating the robots based on the geometric properties of the network.

TL;DR: This paper investigates the comparative performance of several information-driven search strategies and decision rules using a canonical target classification problem and shows that quadratic entropy typically leads to the most effective search strategy with respect to correct-classification rates.

TL;DR: The game results show that a computer player implementing the strategies developed in this paper outperforms players implementing Bayesian networks, Q-learning, or constraint satisfaction, as well as human players.

TL;DR: A methodology is developed to deploy a mobile sensor network for the purpose of detecting and capturing mobile targets in the plane using a new cell decomposition approach to formulate the probability of detection and the cost of operating the robots based on the geometric properties of the network.