Search papers, labs, and topics across Lattice.
This paper introduces an Ant Colony Optimization (ACO) integrated Model Predictive Control (MPC) framework (ACO-MPC) for energy-efficient path planning in autonomous maritime navigation, addressing the challenge of optimizing energy dispatch in dynamic sea environments. The ACO algorithm generates efficient paths while MPC optimizes energy consumption based on a linear model derived from real-world data, considering renewable energy generation, battery usage, and backup power. Simulation results demonstrate that ACO-MPC achieves collision-free navigation with lower cumulative energy consumption compared to rule-based and standard MPC methods.
Achieve energy-efficient autonomous maritime navigation by combining global path optimization with real-time energy management, outperforming traditional methods in both collision avoidance and energy usage.
This work presents a novel approach to optimizing energy dispatch in autonomous maritime systems by integrating metaheuristic search with model predictive control. Our proposed ACO-MPC framework combines Ant Colony Optimization (ACO with Model Predictive Control (MPC) to dynamically generate energy-efficient paths in a simulated sea surface environment, where renewable generation is influenced by wind speed and polar effects. A linear model, derived from real-world data, is embedded within the MPC formulation to accurately predict energy consumption, thereby enabling real-time optimization of renewable utilization, battery cycling, and backup power usage. Simulation results demonstrate that the ACO-MPC approach significantly outperforms conventional rule-based strategies and standard MPC methods, achieving both collision-free navigation and the lowest cumulative energy during the navigation to target points.
