Energy consumption optimization for mobile robots in three-dimension motion using predictive control

Yacoub, Mostafa; Necsulescu, D.; Sąsiadek, Jurek Z.

doi:10.1109/ascc.2013.6606254

Cited by 4 publications

(5 citation statements)

References 11 publications

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“…This work is an extension to a previously developed MPC optimization algorithm [11]. The MPC controller aims to take benefit from the kinetic energy gained during the downhill phase to minimize the consumed electric energy while going uphill.…”

Section: Experimental Evaluation Of Energy Optimization Algorithm Formentioning

confidence: 99%

“…The voltage profile fed to the DC motor here is actually obtained from previous predictive control simulations already done as they were applied on the same robot parameters and same environment conditions [11]. It is assumed here in the experiments that the DC voltage is changed according to the distance covered which is measured by the motors encoders.…”

Section: B Experimental Planningmentioning

confidence: 99%

“…The mobile robot is assumed to move straight forward on inclined road with road angle ( ). The DC motor mechanical load torque ( ) at each wheel is [11]  …”

Section: Mobile Robot Modelmentioning

confidence: 99%

“…More detailed analysis of the model and the simulation results for open-loop, PID and MPC controllers of the mobile robot are presented in[11].The transfer function matrix of the mobile robot is obtained from system state space matrices A, B, C and D as described in(2), the discretized model is obtained assuming Zero-Order Hold (ZOH) as followsT = U VW = ℒ 4# Y( Z − T) 4# [ 4# (T − Z)\, if A is nonsingular a = a and b = b,where, T , \ , a cC_ b are the discrete-time state space matrices.…”

mentioning

confidence: 99%

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Experimental evaluation of energy optimization algorithm for mobile robots in three-dimension motion using predictive control

Yacoub

Necsulescu

Sąsiadek

2013

21st Mediterranean Conference on Control and Automation

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Torque saturation of DC motors of the wheels of mobile robots is one of the main difficulties during climbing hills. A two-DC motor-driven wheels mobile robot is used in the present work to attempt crossing a ditch-like hindrance using predictive control. The proposed predictive control algorithm is compared with the PID control and the open-loop control. Experimental examination of energy optimization algorithm for mobile robots is presented. The experimental results showed a good agreement with the simulation results confirming the capability of the predictive control to avoid torque saturation and indicating a noticeable reduction in the energy consumption. Additionally, a theoretical parametric study of the predictive control is presented. The effects of the road slope and the prediction horizon length on the consumed energy are evaluated. The analytical study showed that the energy consumption is reduced by increasing the prediction horizon until it reaches a limit at which no more energy reduction is obtained. This limit is proportional to the width of the ditch in front of the mobile robot.

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Section: Experimental Evaluation Of Energy Optimization Algorithm Formentioning

confidence: 99%

Section: B Experimental Planningmentioning

confidence: 99%

“…The mobile robot is assumed to move straight forward on inclined road with road angle ( ). The DC motor mechanical load torque ( ) at each wheel is [11]  …”

Section: Mobile Robot Modelmentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

Experimental evaluation of energy optimization algorithm for mobile robots in three-dimension motion using predictive control

Yacoub

Necsulescu

Sąsiadek

2013

21st Mediterranean Conference on Control and Automation

Self Cite

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“…MPC is another control method to address energy optimization challenges. Yacoub et al (2013) tackled torque saturation in robot climbing with an energy optimization algorithm using MPC. MPC using voltage and current control reduced energy consumption by 63% and 53%, respectively, compared to PID control, while providing more robust speed control performance.…”

Section: Energy Optimization For Autonomous Mobile Robotsmentioning

confidence: 99%

A review on energy efficiency in autonomous mobile robots

Wu,

Yeong,

et al. 2023

RIA

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Purpose This paper aims to provide a comprehensive analysis of the state of the art in energy efficiency for autonomous mobile robots (AMRs), focusing on energy sources, consumption models, energy-efficient locomotion, hardware energy consumption, optimization in path planning and scheduling methods, and to suggest future research directions. Design/methodology/approach The systematic literature review (SLR) identified 244 papers for analysis. Research articles published from 2010 onwards were searched in databases including Google Scholar, ScienceDirect and Scopus using keywords and search criteria related to energy and power management in various robotic systems. Findings The review highlights the following key findings: batteries are the primary energy source for AMRs, with advances in battery management systems enhancing efficiency; hybrid models offer superior accuracy and robustness; locomotion contributes over 50% of a mobile robot’s total energy consumption, emphasizing the need for optimized control methods; factors such as the center of mass impact AMR energy consumption; path planning algorithms and scheduling methods are essential for energy optimization, with algorithm choice depending on specific requirements and constraints. Research limitations/implications The review concentrates on wheeled robots, excluding walking ones. Future work should improve consumption models, explore optimization methods, examine artificial intelligence/machine learning roles and assess energy efficiency trade-offs. Originality/value This paper provides a comprehensive analysis of energy efficiency in AMRs, highlighting the key findings from the SLR and suggests future research directions for further advancements in this field.

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Energy Consumption Optimization for Mobile Robots Motion Using Predictive Control

Yacoub

Necsulescu

Sąsiadek

2016

J Intell Robot Syst

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Energy consumption optimization for mobile robots in three-dimension motion using predictive control

Cited by 4 publications

References 11 publications

Experimental evaluation of energy optimization algorithm for mobile robots in three-dimension motion using predictive control

Experimental evaluation of energy optimization algorithm for mobile robots in three-dimension motion using predictive control

A review on energy efficiency in autonomous mobile robots

Energy Consumption Optimization for Mobile Robots Motion Using Predictive Control

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