Experimental validation of mission energy prediction model for unmanned ground vehicles

Self Cite

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.

Section: Experimental and Simulation Studiesmentioning

confidence: 95%

Section: Approach 2: Bayesian Estimation and Prediction In The Presenmentioning

confidence: 99%

Section: Approach 2: Bayesian Estimation and Prediction In The Presenmentioning

confidence: 99%

“…The resulting model would have still been linear, so the proposed Bayesian prediction could be applied. Experimental validation (Sadrpour et al, ) showed that the linearization about

θ = 0

is adequate for prediction of power under typical operating conditions, i.e.,

| θ | \leq 12^{\circ}

. Also, operating in roads with more extreme uphill or downhill grades resulted in robot slippage and tipping.…”

Section: Approach 2: Bayesian Estimation and Prediction In The Presenmentioning

confidence: 99%

Section: Experimental and Simulation Studiesmentioning

confidence: 99%

See 3 more Smart Citations

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

Sadrpour¹,

Jin²,

Ulsoy

2013

Self Cite

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.

A Hybrid Parameters Estimation Approach for Power Consumption Modeling of Ground Mobile Robots With Unknown Payload

Haji Ali Mohamadi,

Khorasani,

Verstraten

et al. 2024

Typically, batteries are the main power source for mobile robots, and the practical constraints in carrying them pose challenges in achieving efficiency, autonomy, and safety objectives in robotic missions. Moreover, dealing with battery degradation requires a precise understanding of energy consumers' behavior in the system. Therefore, establishing an energy consumption model has an important role in addressing these challenges. This study focuses on the modeling and estimation of power consumption in indoor ground mobile robots with unknown payloads. We investigate key components and parameters that influence the actuation energy consumption of ground mobile robots equipped with DC motors, particularly in the context of pure rolling without slipping. To estimate the actuation power consumption of ground mobile robots, we present a hybrid parameters estimation approach. This method includes an offline step for estimating the mass‐decoupled coefficients of a power model, followed by two online steps for estimating the robot's unknown payload and its actuation power consumption. Simulation and experimental results with a differential drive robot (Turtlebot3) validate the effectiveness of the proposed approach. Additionally, the performance of the proposed power consumption estimation method is compared with two other data‐driven models based on multivariate linear regression and multilayer perceptron neural networks. The results demonstrate that the proposed method provides improved power consumption estimation accuracy on the selected indicators compared to the others. We also emphasize the importance of balancing the accuracy of power consumption estimation with its associated costs in the context of the proposed approach. This extra cost includes the energy needed for additional data measurement and processing. Considering this trade‐off is crucial for the practical implementation and resource optimization in robotic systems.