Autonomy Artificial Intelligence Robotics (AAIR)

Path Planning Support of Intelligent Battery Tray to Autonomous Combat Vehicles

by Xi Nan; Jing Dong-O’Brien; Liang Yan; Peng Li; Alex Hundich; David Skalny


Path planning is critical for mission implementation in various robot platforms and autonomous combat vehicles. With the efforts of electrification, battery energy storage as power sources is an ideal solution for robots and autonomous combat vehicles to improve capability and survivability. However, the battery’s limited energy and limited instantaneous power capability could become limiting factors for a mission. The energy and power constraints are also affected by the environment, battery state of health (SOH), and state of charge (SOC) significantly; in the worst case, a well-tested mission profile could fail in the real world if all aspects of the battery are not considered. This paper presents a framework to model the battery’s capability to support a whole mission and specific tasks under various environments. This real-time battery model can be built into an intelligent battery management system to support system-level mission planning, real-time task selection/ teleoperation, post-mission evaluation, and maintenance assistance. Furthermore, case studies are presented to show that the simple rule-of-thumb approach would not provide an optimal solution and that a comprehensive battery model is necessary. Transparent to vehicle’s system control, this model framework provides a simplified parameter set for existing path planning approaches to achieve optimum battery usage, which leads to the improved range, duration, and reliability for a mission.