Energy-Efficient Local Path Planning of a Self-Guided Vehicle by Considering the Load Position

Mohammadpour, Mohammad; Kélouwani, Sousso; Gaudreau, Marc‐André; Allani, Bilel; Zeghmi, Lotfi; Amamou, Ali; Graba, Massinissa

doi:10.1109/access.2022.3216601

Cited by 8 publications

(4 citation statements)

References 31 publications

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“…The center of mass is fundamental in physics and engineering, significantly affecting robotic systems' energy consumption. Mohammadpour et al (2022) proposed a path-planning algorithm considering the center of mass. Their research emphasizes the impact of centroid displacement on self-guided vehicles' (SGVs') energy consumption, showing that payload placement alters the SGV's dynamic inertial parameters and shifts the center of mass, affecting energy consumption during rotational motion.…”

Section: Impact Of the Center Of Massmentioning

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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Section: Impact Of the Center Of Massmentioning

confidence: 99%

A review on energy efficiency in autonomous mobile robots

Wu,

Yeong,

et al. 2023

RIA

View full text Add to dashboard Cite

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“…Fadlo et al (2021) proposed an energy consumption model for differential‐wheeled mobile robots using fuzzy logic control. Mohammadpour et al (2022) studied the robot's load position and proposed an energy optimization method based on local path planning. Liu et al (2021) proposed an improved path‐planning algorithm based on the A* algorithm and modeled the robot's energy consumption using an energy model.…”

Section: Introductionmentioning

confidence: 99%

Modeling analysis and experiment for energy system of ocean energy driven unmanned marine vehicle: The Yulang II case study

Jia,

Liao,

et al. 2024

Journal of Field Robotics

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It is critical to master a marine robot's energy system characteristics adequately to ensure that the robot can perform long‐endurance tasks in complex and ever‐changing marine environments. This paper presents, for the first time, an energy system modeling and analysis method for a marine robot driven by wind, solar, and wave composite energy, using Yulang II as an example. First, an onshore experiment method was designed, and then the conversion experiment of wind, sunlight, and electrical energy was carried out based on that experiment method. At the same time, based on computational fluid dynamics technology, a resistance pole curve considering wave energy capture is constructed. In addition, the attitude, relative motion, and battery constraints of the robot in the ocean are analyzed to construct the energy system model of Yulang II. Finally, the method and model proposed in this paper were compared and verified by simulation and field tests. The results indicated that the corrected forecast deviation was 5.71% in the typical marine environment of the experiment, which supported the study of ocean energy‐driven unmanned marine vehicles strongly.

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“…However, several works have been proposed to incorporate fundamental safety criteria among the most commonly used local motion planners for SGV application namely: Dynamic Window Approach (DWA) [24], Artificial Potential Field (APF) [25], and Timed Elastic Bands (TEB) [26]. Dynamic model-based DWA optimization is considered in [27]. The authors have demonstrated that the position of the center of mass affects the motion stability of the mobile platform.…”

Section: Introductionmentioning

confidence: 99%

A Proposed Adaptive Navigation Architecture to Improve Safety of Self-Guided Vehicles in Dynamic Manufacturing Environments.

GRABA,

Kelouwani,

Zeghmi

et al. 2024

Preprint

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Industrial Self-Guided Vehicles (SGVs), a type of Autonomous Mobile Robots (AMRs) used for material handling tasks, have recently attracted increased attention. As logistics scenarios grow more complex, effectively using state-of-the-art navigation algorithms becomes more challenging but essential for enhancing industrial operational efficiency. In recent years, several path and motion planning algorithms have been developed for broad autonomous navigation applications. While classical navigation schemes might seem suitable for automating SGV movement, a thorough evaluation reveals potential performance bottlenecks, especially when multiple SGVs navigate in dynamically evolving manufacturing environments. Deploying a fleet of SGVs in a shared environment complicates navigation safety for different occupants. Therefore, this paper proposes an adaptive architecture suitable for heterogeneous types of SGVs, capable of characterizing and modeling navigation safety. An extensive evaluation of the proposed approach for the SGV-System, aimed at modeling navigation conflicts through the configuration space, is conducted. The assessment highlights safety and energy efficiency benchmarks across various scenarios, including avoiding unpredictable obstacles such as pedestrians and forklifts. Additionally, simulation tests with various local motion planners were conducted to test the reliability of the proposed strategy. The results suggest that adding an adaptive component to the navigation framework, alongside suitable motion planners, enhances the motion performance of the entire fleet of autonomous mobile platforms.

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Energy-Efficient Local Path Planning of a Self-Guided Vehicle by Considering the Load Position

Cited by 8 publications

References 31 publications

A review on energy efficiency in autonomous mobile robots

A review on energy efficiency in autonomous mobile robots

Modeling analysis and experiment for energy system of ocean energy driven unmanned marine vehicle: The Yulang II case study

A Proposed Adaptive Navigation Architecture to Improve Safety of Self-Guided Vehicles in Dynamic Manufacturing Environments.

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