A Dual Control Perspective for Exploration and Exploitation in Autonomous Search

This paper presents a balanced strategy for autonomous search problem from a control perspective, namely, dual control for exploration and exploitation (DCEE). To search an unknown source in an unknown environment, the agent is required to learn the operational environment and accomplish the control objective, which essentially forms a learning based control problem. A dual control for exploration and exploitation is developed to realise an optimal trade-off between reducing knowledge uncertainty and accomplishing required goal. Various algorithms in learning and control can be integrated into this new framework to offer flexible and customised solutions according to problem specifications and hardware performance. Relationships between DCEE and other algorithms, especially information-theoretic approaches and bio-inspired control methods, are reflected from the perspective of exploration and exploitation. Simulation studies are provided to demonstrate the advantages of DCEE.