Energy Management Strategy for Hybrid Autonomous Mobile Robots in Agricultural Applications: A PINN-Based Approach

The application of autonomous mobile robots (AMR) in agriculture has become an important solution for improving agricultural production, reducing operational costs, and minimizing environmental impact. This paper proposes a hybrid power drive system for AMR, which, by combining the advantages of batteries and fuel, enables the robot to perform long-duration, large-scale agricultural tasks. To reduce the energy consumption of the AMR, this paper focuses on designing an effective energy management strategy. Specifically, the energy management problem is modeled as an optimization problem, and the corresponding Hamilton-Jacobi-Bellman (HJB) partial differential equation (PDE) is derived based on the Bellman optimality principle. To efficiently solve this nonlinear PDE, a method based on Physics-Informed Neural Network (PINN) is proposed. By solving the HJB equation, the optimal power distribution strategy is further derived. Finally, the effectiveness of the proposed method is validated through simulations.