Ground Robot Terrain Mapping and Energy Prediction in Environments with 3-D Topography

Quann, Michael; Ojeda, Lauro; Smith, William; Rizzo, Denise; Castanier, Matthew P.; Barton, Kira

doi:10.23919/acc.2018.8430767

Cited by 6 publications

(3 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Sofman et al (2006) incrementally learned the relation between dense laserbased features characterizing ground unit traversability and overhead features that can be used to assess traversability from aerial images, whereas Bekhti and Kobayashi (2016) learned to predict vibration-based traversability from terrain texture. Quann et al (2020) proposed an energy traversal cost regressor considering both terrain position and appearance. In addition, Mayuku et al (2021) proposed a self-supervised labeling approach for a near-to-far scenario, where vibrationbased traversal cost is inferred from image data, and the selfsupervised data gathering is based on identified terrain classes.…”

Section: Mobile Robot Traversabilitymentioning

confidence: 99%

Autonomous robotic exploration with simultaneous environment and traversability models learning

2022

View full text Add to dashboard Cite

In this study, we address generalized autonomous mobile robot exploration of unknown environments where a robotic agent learns a traversability model and builds a spatial model of the environment. The agent can benefit from the model learned online in distinguishing what terrains are easy to traverse and which should be avoided. The proposed solution enables the learning of multiple traversability models, each associated with a particular locomotion gait, a walking pattern of a multi-legged walking robot. We propose to address the simultaneous learning of the environment and traversability models by a decoupled approach. Thus, navigation waypoints are generated using the current spatial and traversability models to gain the information necessary to improve the particular model during the robot’s motion in the environment. From the set of possible waypoints, the decision on where to navigate next is made based on the solution of the generalized traveling salesman problem that allows taking into account a planning horizon longer than a single myopic decision. The proposed approach has been verified in simulated scenarios and experimental deployments with a real hexapod walking robot with two locomotion gaits, suitable for different terrains. Based on the achieved results, the proposed method exploits the online learned traversability models and further supports the selection of the most appropriate locomotion gait for the particular terrain types.

show abstract

Section: Mobile Robot Traversabilitymentioning

confidence: 99%

Autonomous robotic exploration with simultaneous environment and traversability models learning

2022

View full text Add to dashboard Cite

show abstract

“…The work presented in this paper improves upon the methodology presented by the authors in Quann et al (2018) through a more general formulation that has been refined for experimental implementation. Additionally, the experimental results and validation presented here are entirely new.…”

Section: Contributionsmentioning

confidence: 99%

Off‐road ground robot path energy cost prediction through probabilistic spatial mapping

Quann

Ojeda

Smith

et al. 2019

Journal of Field Robotics

Self Cite

View full text Add to dashboard Cite

Energy is an important factor in planning for ground robots, particularly in off‐road environments where the terrain significantly impacts energy usage. 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‐validation 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.

show abstract

“…The method was extended to cases where π is not available [47], and when multiple integrals are computed simultaneously [73; 26]. It has been applied to fields ranging from econometrics [48] to computer graphics [5] and robotics [56]. While GPs are virtually the only choice that has been explored in the BPNI literature, in practice they suffer from a number of challenges.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Probabilistic Numerical Integration with Tree-Based Models

Zhu¹,

Liu²,

Kang³

et al. 2020

Preprint

View full text Add to dashboard Cite

Bayesian quadrature (BQ) is a method for solving numerical integration problems in a Bayesian manner, which allows user to quantify their uncertainty about the solution. The standard approach to BQ is based on Gaussian process (GP) approximation of the integrand. As a result, BQ approach is inherently limited to cases where GP approximations can be done in an efficient manner, thus often prohibiting high-dimensional or non-smooth target functions. This paper proposes to tackle this issue with a new Bayesian numerical integration algorithm based on Bayesian Additive Regression Trees (BART) priors, which we call BART-Int. BART priors are easy to tune and well-suited for discontinuous functions. We demonstrate that they also lend themselves naturally to a sequential design setting and that explicit convergence rates can be obtained in a variety of settings. The advantages and disadvantages of this new methodology are highlighted on a set of benchmark tests including the Genz functions, and on a Bayesian survey design problem.

show abstract

Ground Robot Terrain Mapping and Energy Prediction in Environments with 3-D Topography

Cited by 6 publications

References 12 publications

Autonomous robotic exploration with simultaneous environment and traversability models learning

Autonomous robotic exploration with simultaneous environment and traversability models learning

Off‐road ground robot path energy cost prediction through probabilistic spatial mapping

Bayesian Probabilistic Numerical Integration with Tree-Based Models

Contact Info

Product

Resources

About