Mission Energy Prediction for Unmanned Ground Vehicles Using Real‐time Measurements and Prior Knowledge

Abstract

A typical unmanned ground vehicle (UGV) mission can be composed of various tasks and several alternative paths. Small UGVs are typically teleoperated and rely on electric rechargeable batteries for their operations. Since each battery has limited energy storage capacity, it is essential to predict the expected mission energy requirement during the mission execution and update this prediction adaptively via real‐time performance measurements (e.g., vehicle power consumption and velocity). We propose and compare two methods in this paper. One is based on recursive least‐squares estimation built upon a UGV longitudinal dynamics model. The other is based on Bayesian estimation when prior knowledge (e.g., road average grade and operator driving style) is available. The proposed Bayesian prediction can effectively combine prior knowledge with real‐time performance measurements for adaptively updating the prediction of the mission energy requirement. Our experimental and simulation studies show that the Bayesian approach can yield more accurate predictions even with moderately imprecise prior knowledge. © 2013 Wiley Periodicals, Inc.

A Step Toward Mobile Robots Autonomy: Energy Estimation Models

“…Analytically, it depends on many parameters like linear (v) and angular (r) velocities, linear (a) and angular accelerations, robot weight, slope and the type of surface. Model parameters remain difficult to evaluate experimentally and the null acceleration hypothesis is generally observed in the literature [5]. This hypothesis corresponds with the work context where mission is composed by a sequence of straight lines traveled with constant velocities.…”

“…DC motor motion power model for Pioneer 3DX robot has been largely studied in [4], [5], [6] and [7]. Analytically, it depends on many parameters like linear (v) and angular (r) velocities, linear (a) and angular accelerations, robot weight, slope and the type of surface.…”

“…2 (a)), the polynomial approximation of parameters in (4) yields to (5). This experimental data is the average of power measurements (1 second of period sampling) of 20 m straight line traveling on a flat horizontal floor using SF M control scheme.…”

“…The authors identify consumption models for DC motor in order to compare the efficiency of different motion plans to cover opened area. Energy prediction models integrating rolling resistance for unmanned ground vehicles is addressed by Sadrpour et al in [5] to define optimal velocity profile for coverage planning. Optimal velocity for energy efficient navigation has been also addressed in [6] for car-like robots.…”

Mission energy prediction for unmanned ground vehicles

“…We forecast the velocity using the exponentially smoothing (EWMA) [20]. Although, the variance of velocity prediction (error) is considered in computing the energy prediction variance, we assume that the combined parameter, i.e., C, is deterministic with a value equal to the most recent estimate of C for the energy prediction.…”