Surrogate based trajectory planning method for an unmanned electric shovel

Wang, Xiaobang; Song, Xueguan; Sun, Wei

doi:10.1016/j.mechmachtheory.2020.104230

Cited by 16 publications

(6 citation statements)

References 22 publications

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“…Up to now, many mathematical models have been proposed to describe the S-curve. In this study, the generalized logical function 33 is adopted to define the S-curve and the corresponding S-curve expressions of the control speeds of the base to arm 4 can be written aswhere, b k ’s are the shape parameters of the expressions. For the comparison between the PTP method and the S-curve method, it is necessary to ensure that all the initial conditions, the final positions, and the essential constraints should be the same.…”

Section: Numerical Simulation and Resultsmentioning

confidence: 99%

Performance-maximum optimization of the intelligent lifting activities for a polar ship crane through trajectory planning

Zou

Lai

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part C:

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Knuckle boom crane (KBC) mounted on a polar ship (PS) is a maritime engineering equipment widely used in offshore activities. The working performance of a PS-mounted KBC (PSKBC) may degrade quickly if manipulated manually under extreme working conditions in polar region. To address the challenges, a performance-maximum optimization method through trajectory planning is developed to further promote the intelligently unmanned lifting processes of the PSKBC. Firstly, the dynamic model of lifting activities of the PSKBC is established using Lagrangian equation, based on which the point-to-point trajectory planning (PTP) method is developed for lifting processes. Subsequently, the PTP‐based optimization model is established to realize the minimum energy consumption and time cost of the lifting activities of the PSKBC, and the obtained optimal lifting performance is compared with the traditional S-curve method. To verify the superiority of the proposed performance-maximum optimization method, numerical simulations of the lifting activities of the PSKBC with respect to different crane loads and final lifting positions are performed. Results indicate that the PTP‐based performance-maximum optimization method for the PSKBC can perfectly achieve better performances with applicable lifting trajectories regarding various working conditions and demands.

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Section: Numerical Simulation and Resultsmentioning

confidence: 99%

Performance-maximum optimization of the intelligent lifting activities for a polar ship crane through trajectory planning

Zou

Lai

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part C:

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“…Based on a relevant study performed in Ref. [21], a sixth-degree polynomial can better balance the solving accuracy and speed for the PTP method. Therefore, in this study, a sixth-degree polynomial is selected to describe the water levels in each ballast tank, namely…”

Section: Design Variablesmentioning

confidence: 99%

Point-to-Point-Based Optimization Method of Ballast Water Allocation for Revolving Floating Cranes with Experimental Verification

Wang,

Yu,

et al. 2024

JMSE

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The Revolving Floating Crane (RFC) is a specialized engineering vessel crucial for offshore lifting operations, such as offshore platform construction and deep-water salvaging. It boasts impressive lifting capacity, good adaptability to various environmental conditions, and high operational efficiency. Conventionally, the safety and stability of RFC operations heavily depend on manual ballast water allocation, which is directly influenced by factors such as personnel status and sea conditions. These manual operations often result in reduced lifting efficiency, higher energy consumption, and compromised operational safety. In response, this paper introduces a ballast water-allocation approach based on the Point-to-Point (PTP) theory for the intelligent operation process of the RFC. The fundamental principles of the PTP theory are analyzed, and a method tailored to optimize ballast water allocation for RFC is proposed. Considering the unique characteristics of the ballast system and the specific requirements of lifting operations, an optimization model for PTP-based ballast water allocation is established. Numerical experiments are conducted to verify the efficacy and reliability of the proposed method. Comparing it to the conventional approaches, the results demonstrate a notable 17.75% reduction in energy consumption and an impressive 73.49% decrease in decision-making time, showcasing the superiority of the proposed approach. Finally, the engineering feasibility of the PTP-based optimization method for ballast water allocation is validated through actual lifting experiments, underscoring its potential to enhance RFC operations.

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“…Based on the theory of soil damage, Awuah-Offei et al [ 5 ] constructed a digging resistance model of an electric shovel, analyzed the kinematics and dynamics of the digging process, and optimized the crowd and hoist speed with the unit energy consumption of digging materials as the objective function. Wang X et al [ 6 , 7 ] proposed a point-to-point (PTP) trajectory planning method with the goal of minimizing the energy consumption per unit volume of mining, and designed an intelligent mining system accordingly. Wei B et al [ 8 ] proposed a new three-degrees-of-freedom parallel excavating mechanism with higher flexibility for the MRS, and optimized the size and excavation trajectory of the new mechanism with the unit excavation energy consumption as the objective function.…”

Section: Introductionmentioning

confidence: 99%

Excavating Trajectory Planning of a Mining Rope Shovel Based on Material Surface Perception

Feng

Lin

et al. 2023

Sensors

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The mining rope shovel (MRS) is one of the core pieces of equipment for open-pit mining, and is currently moving towards intelligent and unmanned transformation, replacing traditional manual operations with intelligent mining. Aiming at the demand for online planning of an intelligent shovel excavation trajectory, an MRS excavating trajectory planning method based on material surface perception is proposed here. First, point cloud data of the material stacking surface are obtained through laser radar to perceive the excavation environment and these point cloud data are horizontally calibrated and filtered to reconstruct the surface morphology of the material surface to provide a material surface model for calculation of the mining volume in the subsequent trajectory planning. Second, kinematics and dynamics analysis of the MRS excavation device are carried out using the Product of Exponentials (PoE) and Lagrange equation, providing a theoretical basis for calculating the excavation energy consumption in trajectory planning. Then, the trajectory model of the bucket tooth tip is established by the method of sixth-order polynomial interpolation. The unit mass excavation energy consumption and unit mass excavation time are taken as the objective function, and the motor performance and the geometric size of the MRS are taken as constraints. The grey wolf optimizer is used for iterative optimization to realize efficient and energy-saving excavation trajectory planning of the MRS. Finally, trajectory planning is carried out for material surfaces with four different shapes (typical, convex, concave, and convex–concave). The results of experimental validation show that the actual hoist and crowd forces are essentially consistent with the planned hoist and crowd forces in terms of the peak value and trend variations, verifying the accuracy of the calculation model and confirming that the full bucket rate and various parameters meet the constraints. Therefore, the trajectory planning method based on material surface perception are feasible for application to different excavation conditions.

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Surrogate based trajectory planning method for an unmanned electric shovel

Cited by 16 publications

References 22 publications

Performance-maximum optimization of the intelligent lifting activities for a polar ship crane through trajectory planning

Performance-maximum optimization of the intelligent lifting activities for a polar ship crane through trajectory planning

Point-to-Point-Based Optimization Method of Ballast Water Allocation for Revolving Floating Cranes with Experimental Verification

Excavating Trajectory Planning of a Mining Rope Shovel Based on Material Surface Perception

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