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Unfortunately, energy costs in these environments are variable and uncertain, making planning difficult. In this paper, we present a method, based on Gaussian process regression (GPR) and known vehicle modeling information, for predicting future path energy costs. The method uses data, collected by a robot operating in a 3D environment with varying terrains, to build a map from inputs (including position, heading, slope, and satellite imagery) to power consumption. The energy costs of future paths are predicted through the summation of power predictions. Importantly, correlations in those predictions are considered to avoid overconfidence. Experimental cross\u2010validation results demonstrate improved accuracy of path energy cost predictions against a baseline approach, as well as the effect of Gaussian process inputs and kernel choice. Additionally, we show how vehicle modeling can aid in predicting energy costs, particularly when data on the environment is sparse.<\/jats:p>","DOI":"10.1002\/rob.21927","type":"journal-article","created":{"date-parts":[[2019,12,16]],"date-time":"2019-12-16T05:56:21Z","timestamp":1576475781000},"page":"421-439","update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["Off\u2010road ground robot path energy cost prediction through probabilistic spatial mapping"],"prefix":"10.1002","volume":"37","author":[{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0002-3665-0369","authenticated-orcid":false,"given":"Michael","family":"Quann","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering University of Michigan  Ann Arbor Michigan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0001-6605-3293","authenticated-orcid":false,"given":"Lauro","family":"Ojeda","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering University of Michigan  Ann Arbor Michigan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0002-2315-5772","authenticated-orcid":false,"given":"William","family":"Smith","sequence":"additional","affiliation":[{"name":"Research &amp; Technology Integration US Army CCDC Ground Vehicle Systems Center  Warren Michigan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0002-0028-1880","authenticated-orcid":false,"given":"Denise","family":"Rizzo","sequence":"additional","affiliation":[{"name":"Research &amp; Technology Integration US Army CCDC Ground Vehicle Systems Center  Warren Michigan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0002-3646-382X","authenticated-orcid":false,"given":"Matthew","family":"Castanier","sequence":"additional","affiliation":[{"name":"Research &amp; Technology Integration US Army CCDC Ground Vehicle Systems Center  Warren Michigan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0003-1047-8078","authenticated-orcid":false,"given":"Kira","family":"Barton","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering University of Michigan  Ann Arbor Michigan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2019,12,15]]},"reference":[{"key":"e_1_2_7_2_1","first-page":"153","article-title":"Multi\u2010task Gaussian process prediction","author":"Bonilla E. 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