Research on path tracking algorithm of green agricultural machinery for sustainable development

Zhang, Jie; Li, Dan

doi:10.1016/j.seta.2022.102917

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…To achieve the quickest completion time for full-coverage farm operations, it is recommended to minimize the number of turns by planning the path along the longest side 29 . To enhance control and reduce heavy plowing and missed areas, this study adopts a reciprocating mulching operation mode, with turning mode selection based on minimum turning radius and width constraints 30,31 .…”

Section: Full-coverage Path Planning Algorithmmentioning

confidence: 99%

Optimal Scheduling of Agricultural Machines in Hilly Mountainous Areas Based on NSGA-II-SA Hybrid Algorithm with Applications

Liu,

Luo,

Zhao

et al. 2024

Preprint

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Optimizing the scheduling of farm machinery is essential to meet farmers' requirements, minimize scheduling costs, and save time. This study focuses on scheduling farm machinery in multiple cooperatives across various regions, aiming to minimize scheduling costs and reduce scheduling time. Initially, a multi-constraint hybrid clustering algorithm is employed to assign farmland to each farm machinery cooperative by clustering before scheduling. Subsequently, an enhanced version of the Non-dominated Sorting Genetic Algorithm-II (NSGA-II) is proposed, integrating a local search strategy based on congestion-based neighborhood search and the Simulated Annealing (SA) algorithm to develop the NSGA-II-SA algorithm. This hybrid multi-objective evolutionary algorithm effectively optimizes scheduling costs and time. The model's validity and the algorithm's superiority are demonstrated through a Web-based multi-region agricultural machine scheduling system and an example study. Experimental results show that the NSGA-II-SA algorithm significantly reduces scheduling costs and time, as well as the number of dispatched farm machines, outperforming other algorithms with reductions of 9.8%, 3.1%, and 8.7% in total scheduling costs, and 12.5%, 13.4%, and 11.6% in total scheduling time. This research establishes a theoretical framework for multi-region agricultural machine scheduling in hilly and mountainous areas, enhancing agricultural production efficiency.

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Section: Full-coverage Path Planning Algorithmmentioning

confidence: 99%

Optimal Scheduling of Agricultural Machines in Hilly Mountainous Areas Based on NSGA-II-SA Hybrid Algorithm with Applications

Liu,

Luo,

Zhao

et al. 2024

Preprint

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“…The intrinsic unevenness of pathways provided by the A-star algorithm might be a substantial obstacle in the practical implementation of autonomous vehicles and robotic systems in challenging agricultural settings. The sudden changes in both trajectory and velocity may result in inefficiencies, heightened mechanical deterioration, and potential safety hazards, especially when operating with substantial and costly machinery [19][20][21]. Addressing this limitation is of utmost importance as we seek to harness the full potential of the A-star algorithm in agriculture.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Mobile Robot Navigation Based on A-Star Algorithm for Obstacle Avoidance in Smart Agriculture

Chatzisavvas,

Dossis,

Dasygenis

2024

Electronics

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The A-star algorithm (A*) is a traditional and widely used approach for route planning in various domains, including robotics and automobiles in smart agriculture. However, a notable limitation of the A-star algorithm is its tendency to generate paths that lack the desired smoothness. In response to this challenge, particularly in agricultural operations, this research endeavours to enhance the evaluation of individual nodes within the search procedure and improve the overall smoothness of the resultant path. So, to mitigate the inherent choppiness of A-star-generated paths in agriculture, this work adopts a novel approach. It introduces utilizing Bezier curves as a postprocessing step, thus refining the generated paths and imparting their smoothness. This smoothness is instrumental for real-world applications where continuous and safe motion is imperative. The outcomes of simulations conducted as part of this study affirm the efficiency of the proposed methodology. These results underscore the capability of the enhanced technique to construct smooth pathways. Furthermore, they demonstrate that the generated paths enhance the overall planning performance. However, they are also well suited for deployment in rural conditions, where navigating complex terrains with precision is a critical necessity.

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Adoption and intensity of agricultural mechanization and their impact on non-farm employment of rural women

Ma,

Zhou,

Boansi

et al. 2024

World Development

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Research on path tracking algorithm of green agricultural machinery for sustainable development

Cited by 3 publications

References 14 publications

Optimal Scheduling of Agricultural Machines in Hilly Mountainous Areas Based on NSGA-II-SA Hybrid Algorithm with Applications

Optimal Scheduling of Agricultural Machines in Hilly Mountainous Areas Based on NSGA-II-SA Hybrid Algorithm with Applications

Optimizing Mobile Robot Navigation Based on A-Star Algorithm for Obstacle Avoidance in Smart Agriculture

Adoption and intensity of agricultural mechanization and their impact on non-farm employment of rural women

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