Hybrid Power System Design for Autonomous Ground Robots

Abstract

The interest in mobile robots has increased rapidly due to the complicated tasks a mobile robot can accomplish. An efficient robot power supply system can increase the robot range of travel. Different power management techniques have been applied heavily in the field of electric vehicles. Such techniques are helpful in terms of extending the robot driving range; power controller requires placing a DC converter that consists of power switches, inductors, and capacitors. In most cases, robots are still powered by a single battery. This observation inspired this work to develop an enhanced passive multi-source power system, using Generalized Predictive Control (GPC) and Kalman filtering (KF) to find the minimum power required to drive the robot along a predefined path. As a result, the designed power system extends robot driving range from 3 to 16 hours. Since batteries are a major component of any current hybrid energy system design, any good energy management system must incorporate an impending battery failure detection system, so that other energy sources can be switched on to replace a dying battery. This work proposes a battery voltage collapse detection technique based on Fast Fourier Transforms (FFTs) and artificial neural network (ANNs), where the robot driving range is extended more by 3 hours using a backup battery. This work aims to use two batteries, a supercapacitor, and a fuel cell based system to form a long-lasting hybrid energy system for a mobile robot.