Energy-efficient motion planning of an autonomous forklift using deep neural networks and kinetic model

Mohammadpour, Mohammad; Kelouwani, Sousso; Gaudreau, Marc-André; Zeghmi, Lotfi; Amamou, Ali; Bahmanabadi, Hossein; Allani, Bilel; Graba, Massinissa

doi:10.1016/j.eswa.2023.121623

Cited by 9 publications

(3 citation statements)

References 47 publications

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“…Autonomous forklifts can avoid obstacles, detect, and manipulate loaded pallets from truck beds or on the ground without operator intervention. In addition, autonomous forklifts can safely execute tasks that require human interaction with hazardous objects or in an unknown environment [ 9 , 11 , 12 ]. However, the usage of autonomous forklifts may represent a burden on developing countries as it has high installation and maintenance costs.…”

Section: Introductionmentioning

confidence: 99%

Design and simulation of eco-friendly smartphone controlled forklift

Abuzied,

Nazih,

Sahbel

2024

Heliyon

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Section: Introductionmentioning

confidence: 99%

Design and simulation of eco-friendly smartphone controlled forklift

Abuzied,

Nazih,

Sahbel

2024

Heliyon

View full text Add to dashboard Cite

“…In terms of robot chassis model construction, it can usually be categorized into kinetic model and kinematic model construction [21]. In path planning, kinematic model construction is widely used in mobile robot path planning because it is efficient and practical [22], unless robots involving special loads or structures need to consider kinetic models [23]. Traditional kinematic model construction assumes that the steering and drive mechanisms of the vehicle are rigid bodies and uses an integer order approach for model construction [24].…”

Section: Introductionmentioning

confidence: 99%

Smooth and Efficient Path Planning for Car-like Mobile Robot Using Improved Ant Colony Optimization in Narrow and Large-Size Scenes

Li,

Jiang,

et al. 2024

Fractal Fract

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Car-like mobile robots (CLMRs) are extensively utilized in various intricate scenarios owing to their exceptional maneuverability, stability, and adaptability, in which path planning is an important technical basis for their autonomous navigation. However, path planning methods are prone to inefficiently generate unsmooth paths in narrow and large-size scenes, especially considering the chassis model complexity of CLMRs with suspension. To this end, instead of traditional path planning based on an integer order model, this paper proposes fractional-order enhanced path planning using an improved Ant Colony Optimization (ACO) for CLMRs with suspension, which can obtain smooth and efficient paths in narrow and large-size scenes. On one hand, to improve the accuracy of the kinematic model construction of CLMRs with suspension, an accurate fractional-order-based kinematic modelling method is proposed, which considers the dynamic adjustment of the angle constraints. On the other hand, an improved ACO-based path planning method using fractional-order models is introduced by adopting a global multifactorial heuristic function with dynamic angle constraints, adaptive pheromone adjustment, and fractional-order state-transfer models, which avoids easily falling into a local optimum and unsmooth problem in a narrow space while increasing the search speed and success rate in large-scale scenes. Finally, the proposed method’s effectiveness is validated in both large-scale and narrow scenes, confirming its capability to handle various challenging scenarios.

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“…One recent research has utilized deep neural networks for vison-based navigation in unknown indoor environments [2]. Another studied energy efficient motion planning of forklifts transportation utilizing Deep neural networks (DNNs) [3]. For underwater navigation application, model predictive control-based velocity controller was verified for trajectory tracking in simulation [4].…”

Section: Introductionmentioning

confidence: 99%

Outdoor Tests of Autonomous Navigation System Based on Two Different Reference Points of PurePursuit Algorithm for 10-ton Articulated Vehicle

Latif,

Lindbäck,

Lideskog

et al. 2024

IEEE Access

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This work presents outdoor test results of autonomous navigation system based on two different reference points of PurePursuit algorithm for a 10-ton articulated research platform. PurePursit is commonly used simplified tracking algorithm based on geometric calculation of desired steering angle by pursuing certain number of points/distance ahead on given path from a fixed reference point of vehicle. Choice of fixed reference point effects the tracking accuracy particularly for articulated/center-steered vehicles. In this experimental work, PurePursuit algorithm with a virtual reference point(PPV) and commonly used front-axle reference point(PPF) is evaluated for heavy duty articulated vehicles in outdoor experiments .Experimental data shows that choice of reference point in PurePursuit algorithm for articulated vehicle has impact on tracking accuracy in terms of crosstrack errors and heading errors. Navigation tests were performed on a flat asphalt surface for paths of sharp complexities i-e a path with continuous curvature (circular path) and a path with sharp turns (zigzag path) with different initial conditions i-e initial position of vehicle. In general, it can be concluded that PurePursuit algorithm with front reference point (PPF) produced fewer crosstrack errors while PurePursuit algorithm with virtual midpoint reference (PPV) produced fewer heading errors.

show abstract

Energy-efficient motion planning of an autonomous forklift using deep neural networks and kinetic model

Cited by 9 publications

References 47 publications

Design and simulation of eco-friendly smartphone controlled forklift

Design and simulation of eco-friendly smartphone controlled forklift

Smooth and Efficient Path Planning for Car-like Mobile Robot Using Improved Ant Colony Optimization in Narrow and Large-Size Scenes

Outdoor Tests of Autonomous Navigation System Based on Two Different Reference Points of PurePursuit Algorithm for 10-ton Articulated Vehicle

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