Abstract: A large area surveillance method is based on weighted double deep Q-learning. A robot which of Q-value table including a QA-value table and QB-value table is provided, an unidentified object enters a large space to trigger the robot, and the robot perceives a current state s and determines whether the current state s is a target state, if yes, the robot reaches a next state and monitors the unidentified object, and if not, the robot reaches a next state, obtains a reward value according to the next state, selectively updates a QA-value or QB-value with equal probability, and then updates a Q-value until convergence to obtain an optimal surveillance strategy. The problems of a limited surveillance area and camera capacity are resolved, and the synchronization of multiple cameras doesn't need to be considered, and thus the cost is reduced. A large area surveillance robot is also disclosed.

Abstract: A large area surveillance method is based on weighted double deep Q-learning. A robot which of Q-value table including a QA-value table and QB-value table is provided, an unidentified object enters a large space to trigger the robot, and the robot perceives a current state s and determines whether the current state s is a target state, if yes, the robot reaches a next state and monitors the unidentified object, and if not, the robot reaches a next state, obtains a reward value according to the next state, selectively updates a QA-value or QB-value with equal probability, and then updates a Q-value until convergence to obtain an optimal surveillance strategy. The problems of a limited surveillance area and camera capacity are resolved, and the synchronization of multiple cameras doesn't need to be considered, and thus the cost is reduced. A large area surveillance robot is also disclosed.