Real-time energy-efficient path planning for unmanned ground vehicles using mission prior knowledge

Abstract

Unmanned Ground Vehicle (UGV) missions include situations where a UGV has to choose between alternative paths, and are often limited by the available on-board energy. Thus, we propose a dynamic energy-efficient path planning algorithm that integrates mission prior knowledge with real-time sensory information to identify the most energy-efficient path for mission completion. Our proposed approach predicts and updates the distribution of the energy requirement for alternative paths using recursive Bayesian estimation through two stages: (a) exploration – road segments can be explored to reduce their energy prediction uncertainty; (b) exploitation – the most reliable path is selected using the collected information in the exploration stage and then traversed. Our simulation results show that the proposed approach outperforms offline methods, as well as a method that relies on exploitation only to identify the most energy-efficient path.