Assessment of Energy-Efficient and Model-Based Control

Report No. ARL-TR-8042
Authors: Craig Lennon, Marshal Childers, Mario Harper, Camilo Ordonez, Nikhil Gupta, James Pace, Ryan Kopinsky, Aneesh Sharma, Emmanuel Collins, and Jonathan Clark
Date/Pages: June 2017; 30 pages
Abstract: The US Army Research Laboratory's (ARL's) Robotics Collaborative Technology Alliance is a program intended to change robots from tools that Soldiers use into teammates with which Soldiers can work. One desired ability of such a teammate is the ability to operate in an energy-efficient manner on a variety of surfaces. To develop such a teammate, alliance researchers developed planning algorithms that incorporate knowledge of the vehicle's steering and control system. These algorithms adapt their navigation to different types of terrain, learning appropriate parameter values by conducting a brief set of trial maneuvers, and are intended to enable the robot to operate in a manner that is more energy efficient. In June of 2016, ARL researchers conducted an assessment of this technology by comparing this planning algorithm to a traditional minimum-distance planning algorithm. This assessment found an overall improvement in energy efficiency, which was clearly visible when the systems operated on grass, but unclear when the systems operated on asphalt. Overall, the results suggest that the energy-efficient planner does have the potential to plan more energy-efficient paths.
Distribution: Approved for public release
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Last Update / Reviewed: June 1, 2017