Planning energy-efficient paths is an important capability in many robotics applications. Obtaining an energy-cost map for a given environment enables planning such paths between any given pair of locations within the environment. However, efficiently building an energy map is challenging, especially for large environments. Some of the prior work uses physics-based laws (friction and gravity force) to model energy costs across environments. These methods work well for uniform surfaces, but they do not generalize well to uneven terrains. In this letter, we present a method to address this mapping problem in a data-driven fashion for the cases where an aerial image of the environment can be obtained. To efficiently build an energy-cost map, we train a neural network that learns to predict the complete energy maps by combining aerial images and sparse ground robot energy-consumption measurements. Field experiments are performed to validate our results. We show that our method can efficiently build an energy-cost map accurately even across different types of ground robots.
Bibliographical noteFunding Information:
Manuscript received February 24, 2020; accepted June 11, 2020. Date of publication July 2, 2020; date of current version July 10, 2020. This letter was recommended for publication by Associate Editor H. Ryu and Editor Y. Choi upon evaluation of the reviewers’ comments. This work was supported in part by NSF under Grant #1525045, in part by NSF under Grant #1617718, in part by NSF under Grant #1849107, and in part by MN State LCCMR program. (Corresponding author: Minghan Wei.) The authors are with the Department of Computer Science and Engineering, University of Minnesota, Minneapolis, MN 55455 USA (e-mail: firstname.lastname@example.org; email@example.com). Digital Object Identifier 10.1109/LRA.2020.3006797
© 2016 IEEE.
- Intelligent robots
- path planning