A Scalable Hybrid Power and Energy Architecture for Unmanned Ground Vehicles

Advances in robot performance have been limited by a lack of significant new advances in the mature field of battery technology. The limitations of batteries and other traditional energy sources have been recognized in other industries, and while much research has gone into alternatives or augmentation of energy sources through a hybridization scheme, very little of this research has been applied to robotic power systems. There are many potential advantages that could be obtained by shifting the focus of robotic power systems from the use of a single energy storage device to the inclusion of multiple storage/generation devices optimized for a robotic platform. The challenge lies in the development of the hardware and control algorithms for a scalable power delivery architecture which satisfies both the power and energy requirements of most unmanned ground vehicles. This paper proposes a ‘Hybrid Power and Energy for Robots’ (HyPER) system architecture which is easily scalable and facilitates the use of a wide variety of energy storage and conversion devices. The focus of the paper is on the system power allocation algorithm and its stability. The proposed architecture is simulated in software, and then implemented in a laboratory environment and placed under loads which simulate the conditions experienced by a robotic platform during standard operation. The experimental results for the example system under load are presented, demonstrating that the architecture functions properly when faced with real world robotic power demands. The benefits of this architecture are not limited to providing flexible sources of electrical power to the robot; the system architecture also facilitates power source optimization and management which can extend the operating time and the range of the robot, and extend the life and reliability of the energy storage devices. Beyond these main functions, other potential areas for the control algorithm development include health monitoring algorithms, energy optimization algorithms, mission energy requirement estimation, mission planning, and mission programmable operation modes.