Power Characterization of a Skid-Steered Mobile Field Robot with an Application to Headland Turn Optimization

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.

Section: Methodsmentioning

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Section: Related Workmentioning

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Section: Path Energy Prediction With Sparse Datamentioning

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Section: Introductionmentioning

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Section: Introductionmentioning

confidence: 99%

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

Quann

Ojeda

Smith

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On‐line estimation of power model parameters for skid‐steer robots with applications in mission energy use prediction

Pentzer

Brennan

Reichard

2022

This paper presents algorithms to predict the energy used by a skid‐steer robot to complete a given path, including the refinement of that prediction during operation. The vast majority of commercially available ground mobile robots utilize skid‐steer technology due to the robustness and maneuverability of the design. However, the complex track/terrain interactions developed during skid‐steer maneuvers make traction‐related power losses difficult to model. Accurate estimates of skid‐steer power and energy usage enable more intelligent mission planning decisions to be made. In this study, a skid‐steer power model that is linear in three unknown parameters was used and tested extensively on two skid‐steer vehicles, one tracked and the other wheeled. Repeated testing shows good repeatability of estimated parameters, and results indicate that the tracked vehicle exhibits a much larger sensitivity to the surface types tested than the wheeled vehicle. The skid‐steer power model is then combined with a kinematic model of skid‐steer movement and a trajectory control algorithm to predict the energy required to complete a given traversal path. This is useful for estimating energy demands for retro‐traversal, an application demonstrated in this study. The energy predictions are further refined through the mapping of power model parameters onto terrain types using overhead imagery of the area of operation. The maps are used to predict what terrain types will be encountered along the planned path to improve the estimate of the energy required.

Characterization of motion power loss of off‐road wheeled robot in a slippery terrain

Shafaei

Mousazadeh

2022

For the first time in realm of power study of off‐road wheeled robots, this study deals with motion power loss due to slippage of robot wheels traversed on slippery terrain. For this purpose, effects of slippery terrain type (solid balls with diameter of 0.0127, 0.0254, and 0.0508 m), tire air pressure (20.68, 34.47, and 55.16 kPa), and robot forward speed (0.17, 0.33, and 0.5 m/s) on the power loss were characterized. Derived results proved that the increasing effect of slippery terrain type on the power loss was dominant (1.08 and 3.21 times) than that of robot forward speed and tire air pressure, respectively. Meanwhile, the increasing effect of robot forward speed on the power loss was prevailed (2.98 times) than that of tire air pressure. Hence, to minimize the power loss of the robot traversed on each type of slippery terrain, adjustment of robot forward speed should be considered as first priority. A comparison between motion power loss (43.60–249.40 W) and provided motion power for the robot (136–436.37 W) implies that 12.93–75.44% of provided motion power was wasted by slippage of the robot wheels on slippery terrains. Overall, the analytical results obtained in this study lead to open a new prospection for comprehending of the power loss trends of off‐road wheeled robots traversed on slippery terrains. As slippery terrain composed of solid balls, the results can be especially utilized for final phase of unloading robotic operations of catalyst handling procedure in process towers and reactors of oil, gas, petrochemical, and chemical industries.