Real-Time Energy-Efficient Path Planning for Unmanned Ground Vehicles Using Mission Prior Knowledge

Surveillance missions that involve unmanned ground vehicles (UGVs) include situations where a UGV has to choose between alternative paths to complete its mission. Currently, UGV missions are often limited by the available on-board energy. Thus, we propose a dynamic most energy-efficient path planning algorithm that integrates mission prior knowledge with real-time sensory information to identify the mission’s most energy-efficient path. Our proposed approach predicts and updates the distribution of energy requirement of alternative paths using recursive Bayesian estimation through two stages: (1) exploration — road segments are explored to reduce their energy prediction uncertainty; (2) exploitation — the most energy-efficient path is selected using the collected information in the exploration stage and is traversed. Our simulation results show that the proposed approach outperforms offline methods, as well as a method that only relies on exploitation to identify the most energy-efficient path.