Evolutionary multi-objective optimization for bulldozer and its blade in soil cutting

Barakat, Nada; Sharma, Deepak

doi:10.1080/17509653.2018.1500953

Cited by 10 publications

(4 citation statements)

References 14 publications

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“…The received signal strength at this point is RSSI(i, j). relationship between the transmitted signal strength and the received signal strength is expressed in (8).…”

Section: Experiments On Wireless Sensor Network (Wsns) Deployment Pro...mentioning

confidence: 99%

“…Moreover, real-world problems, such as those involving wireless sensor network deployment [5], water distribution system design [6], electric vehicle charging station problems [7], bulldozer blade in soil cutting [8], structural health monitoring [9], gesture recognition problems [10], etc., have been modeled using multi-objectives that demand efficient MOEAs. Designing an efficient algorithm to find a set of finite representative solutions in the objective space and balance the convergence and diversity is a huge challenge.…”

Section: Introductionmentioning

confidence: 99%

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A Projection-Based Evolutionary Algorithm for Multi-Objective and Many-Objective Optimization

Peng

Chen

et al. 2023

Processes

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Many-objective optimization problems (MaOPs) are challenging optimization problems in scientific research. Research has tended to focus on algorithms rather than algorithm frameworks. In this paper, we introduce a projection-based evolutionary algorithm, MOEA/PII. Applying the idea of dimension reduction and decomposition, it divides the objective space into projection plane and free dimension(s). The balance between convergence and diversity is maintained using a Bi-Elite queue. The MOEA/PII is not only an algorithm, but also an algorithm framework. We can choose a decomposition-based or dominance-based algorithm to be the free dimension algorithm. When it is an algorithm framework, it exhibits a better performance. We compare the performance of the algorithm and the algorithm with the MOEA/PII framework. The performance is evaluated by benchmark test instances DTLZ1-7 and WFG1-9 on 3, 5, 8, 10, and 15 objectives using IGD-metric and HV-metric. In addition, we investigated its superior performance on the wireless sensor networks deployment problem using C-metric. Moreover, determining objective domain for the objects of the wireless sensor networks deployment problem reduces the time and makes the solution set more responsive to user needs.

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“…The received signal strength at this point is RSSI(i, j). relationship between the transmitted signal strength and the received signal strength is expressed in (8).…”

Section: Experiments On Wireless Sensor Network (Wsns) Deployment Pro...mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Projection-Based Evolutionary Algorithm for Multi-Objective and Many-Objective Optimization

Peng

Chen

et al. 2023

Processes

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“…Promising solutions involve autonomous construction and improving the efficiency of non‐skilled operators. Several studies have addressed autonomous construction involving wheel loaders (Shi et al, 2020a, 2020b), bulldozer (Barakat & Sharma, 2019), cranes (Chakraborty & Meena, 2016; Koivumaki & Mattila, 2015), and excavators. In the previous study, the target tasks for excavator automation are free‐form trenching (Jud et al, 2019), rock pile excavation (Fukui et al, 2017), manipulation of large‐scale stones (Mascaro et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of excavation motion sequence for hydraulic excavators based on extraction of excavation style and phase

Sato

Tsunano

Sano

et al. 2022

Journal of Field Robotics

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In recent times, research on highly efficient construction has gained increasing popularity. During construction with hydraulic excavators, the sequence of excavation motions significantly affects the duration and precision of the work; therefore, it should be as efficient as possible. Because automation is still difficult, the ability improvement support system of nonskilled operators is required. However, currently, there exists no effective method for evaluating or planning the excavation motion sequence. This study proposes a method for evaluating excavation motion sequences based on a pattern recognition technique. First, the data array of hydraulic excavator motions is divided into unit excavation motions, characterized using a clustering method. Second, the patterns are divided into excavation phases with similar excavation motion patterns. Finally, the excavation motion sequence is evaluated by calculating the transition frequency between excavation phases. The result shows that the proposed approach can classify the skill level of the operator with accuracy of 0.8. Therefore, the frequency of excavation phase transitions can be used as a useful indicator for evaluating excavation motion sequences.

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“…Kim J.-W. et al optimized the working performance of the hydraulic excavator for multiple objects through the hybrid Taguchi random coordinate search algorithm [13]. Barakat N. and Sharma D. provided an optimal design method for the bulldozer blade based on the evolutionary multi-objective optimization algorithm [14]. Masih-Tehrani M. and Ebrahimi-Nejad S. combined the genetic algorithm and integer linear programming technique for multi-objective optimization of the powertrain of the bulldozer [15].…”

Section: Introductionmentioning

confidence: 99%

Digging Trajectory Optimization for Cable Shovel Robotic Excavation Based on a Multi-Objective Genetic Algorithm

Wang

et al. 2020

Energies

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As one of the most essential earth-moving equipment, cable shovels significantly influence the efficiency and economy in the open-pit mining industry. The optimal digging trajectory planning for each cycle is the base for achieving effective and energy-saving operation, especially for robotic excavation, in which case, the digging trajectory can be precisely tracked. In this paper, to serve the vision of cable shovel automation, a two-phase multi-objective genetic algorithm was established for optimal digging trajectory planning. To be more specific, the optimization took digging time and energy consumption per payload as objects with the constraints of the limitations of the driving system and geometrical conditions. The WK-55-type cable shovel was applied for the validation of the effectiveness of the multi-objective optimization method for digging trajectories. The digging performance of the WK-55 cable shovel was tested in the Anjialing mining site to establish the constraints. Besides, the digging parameters of the material were selected based on the tested data to make the optimization in line with the condition of the real digging operations. The optimization results for different digging conditions indicate that the digging time decreased from an average of 20 s to 10 s after the first phase optimization, and the energy consumption per payload reduced by 13.28% after the second phase optimization, which validated the effectiveness and adaptivity of the optimization algorithm established in this paper.

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Evolutionary multi-objective optimization for bulldozer and its blade in soil cutting

Cited by 10 publications

References 14 publications

A Projection-Based Evolutionary Algorithm for Multi-Objective and Many-Objective Optimization

A Projection-Based Evolutionary Algorithm for Multi-Objective and Many-Objective Optimization

Evaluation of excavation motion sequence for hydraulic excavators based on extraction of excavation style and phase

Digging Trajectory Optimization for Cable Shovel Robotic Excavation Based on a Multi-Objective Genetic Algorithm

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