Towards adaptive power consumption estimation for over-actuated unmanned vehicles

Bensekrane, Ismail; Amara, Yacine

doi:10.1109/robio.2017.8324400

Cited by 6 publications

(4 citation statements)

References 14 publications

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“…In this section, Neuro-fuzzy model is developed for the power consumption estimation of a four wheeled overactuated URV. The model considers the degree of actuation redundancy δ R , degree of steerability δ S , and the path curvature C; for definition of these terms one can refer to [4].…”

Section: Power Consumption Estimationmentioning

confidence: 99%

“…Both BMS and EMS cannot estimate the instantaneous power consumption of an URV during the trajectory tracking under kinematic and dynamic conditions of driving. The kinematic parameters are essentially represented by the degree of actuation redundancy, degree of steerability, and the road geometry in terms of path curvature [4].…”

Section: Introduction a Power Consumption By Urvmentioning

confidence: 99%

“…Generally, those models can not represent with accuracy due to presence of non-linearities and not considering the driving modes of navigation and road curvature. In [4], a model is presented taking into account the path curvature C, degree of steerability δ S , and degree of actuation redundancy δ R , to estimate analytically power consumption. But, analytical models cannot model all the system non-linearities and complexities.…”

Section: Introduction a Power Consumption By Urvmentioning

confidence: 99%

See 2 more Smart Citations

Energy Planning for Unmanned Over-Actuated Road Vehicle

Bensekrane

Melingui

Coelen

et al. 2018

2018 IEEE Vehicle Power and Propulsion Conference (VPPC)

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Energy planning for unmanned road vehicles (URV)s is an important step for the management of the autonomous driving. This energy planning depends on the model for power consumption estimation related to URVs. Generally URVs are over-actuated, and this property leads to multiple kinematic configurations for driving. Consequently, it adds more constraints and more complexity for energy planning. In this paper, a Neuro-Fuzzy model is developed for power consumption estimation for different driving modes configurations of URV. Furthermore, a dynamic programming algorithm is applied to find the optimal velocity profile, and the optimal configuration mode in each segment of the road for an over-actuated URV called RobuCAR, used for experimental validation.

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Section: Power Consumption Estimationmentioning

confidence: 99%

Section: Introduction a Power Consumption By Urvmentioning

confidence: 99%

Section: Introduction a Power Consumption By Urvmentioning

confidence: 99%

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Energy Planning for Unmanned Over-Actuated Road Vehicle

Bensekrane

Melingui

Coelen

et al. 2018

2018 IEEE Vehicle Power and Propulsion Conference (VPPC)

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“…However, the energy consumption is not related to the dynamic parameters of the robot as moments of inertia. Instead, Chuy and Bensekane's investigation presents a power consumption modeling, using a 2-dimensional, second order differential equation, that describes a four wheel steering robot forces (O. Chuy Jr. and Ordonez, 2009;I. Bensekrane and Merzouk, 2017).…”

Section: Introductionmentioning

confidence: 99%

Mixed Energy Model for a Differential Guide Mobile Robot

Morales

Mendoza

2018

2018 23rd International Conference on Methods &Amp; Models in Automation &Amp; Robotics (MMAR)

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Energy consumption is an important issue for mobile robots that carry a limited energy sources, like batteries, for a long period of time. An energy model can relate the kinematic movements of the robot with energy values, giving an estimation of the energy needed for the robot to fulfill a specific task. In this study an energy model is proposed, based on the dynamic parameters of the mobile robot, as well as the motors, given an energy value close to real energy consumption. Mixed energy model is tested with a well-known motor energy model, using the velocities related to straight and curvature paths as input. In the results, a higher energy consumption value is identified by the mixed energy model, especially when the acceleration of the mobile robot increases. Energy models are configured with P3-DX robot mobile parameters.

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