Optimization of Agricultural Machinery Task Scheduling Algorithm Based on Multiobjective Optimization

Liang, Zheng

doi:10.1155/2022/5800332

Cited by 7 publications

(4 citation statements)

References 9 publications

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“…The auto drive system is composed of an information perception module, positioning and attitude measurement module, path planning module, and tracking control module. While path planning and tracking control are effective supports for the implementation of autonomous driving in the field of engineering applications [5,6], as a common type of agricultural robot, tracked agricultural machinery can autonomously perceive external environmental information. It can also make decisions and plans for its own posture and path, independently controlling its own behavior during operations [7,8].…”

Section: Introductionmentioning

confidence: 99%

Path Planning and Tracking Control of Tracked Agricultural Machinery Based on Improved A* and Fuzzy Control

Liu,

Wang,

Wang

et al. 2024

Electronics

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In order to improve the efficiency of agricultural machinery operations and reduce production costs, this article proposes a path planning algorithm based on the improved A* algorithm (IA*) and a tracking controller based on fuzzy sliding mode variable structure control (F-SMC) to meet the operation requirements of tracked agricultural machinery. Firstly, we introduce a heuristic function with variable weights, a penalty, and a fifth-order Bezier curve to make the generated path smoother. On this basis, the ant colony algorithm is introduced to further optimize the obtained path. Subsequently, based on fuzzy control theory and sliding mode variable structure control theory, we established a kinematic model for tracked agricultural machinery as the control object, designed a fuzzy sliding mode approaching law, and preprocessed it to reduce the time required for sliding mode control to reach the chosen stage. The simulation experiment of path planning shows that compared with A*, the average reduction rate of the path length for IA* is 5.51%, and the average reduction rate of the number of turning points is 39.01%. The path tracking simulation experiment shows that when the driving speed is set to 0.2 m/s, the adjustment time of the F-SMC controller is reduced by 0.99 s and 1.42 s compared to the FUZZY controller and PID controller, respectively. The variance analysis of the adjustment angle shows that the minimum variance of the F-SMC controller is 0.086, and the error converges to 0, proving that the vehicle trajectory is smoother and ultimately achieves path tracking. The field test results indicate that the path generated by the IA* algorithm can be tracked by the F-SMC controller in the actual environment. Compared to the A* algorithm and FUZZY controller, the path tracking time reduction rate of IA* and F-SMC is 29.34%, and the fuel consumption rate is reduced by 2.75%. This study is aimed at providing a feasible approach for improving the efficiency of tracked agricultural machinery operations, reducing emissions and operating costs.

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Section: Introductionmentioning

confidence: 99%

Path Planning and Tracking Control of Tracked Agricultural Machinery Based on Improved A* and Fuzzy Control

Liu,

Wang,

Wang

et al. 2024

Electronics

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“…There are two different development directions of modern agricultural machinery [1,2] . One is to develop in the direction of super large scale and complexity.…”

Section: Introductionmentioning

confidence: 99%

Multi-machine collaboration realization conditions and precise and efficient production mode of intelligent agricultural machinery

Bo,

Xiaoxue,

Yana

et al. 2024

International Journal of Agricultural and Biological Engineering

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Multi-machine collaboration of agricultural machinery is one of the international frontier and hot research in the field of agricultural equipment. However, the current domestic multi-machine collaborative operation of agricultural machinery is limited to the research of task goal planning and collaborative path optimization in a single production link. In order to achieve the purpose of zero inventory of agricultural materials and precise and efficient production operations, a new technology of agricultural machinery multi-machine collaboration with multi-dimension and full chain was proposed, which takes into account the whole process of agricultural production, as well as agricultural machinery system and external supply chain, storage and transportation chain collaboration. The problems of data collaboration, process collaboration and organization collaboration were analyzed. And the realization conditions of new multi-machine cooperative technology were analyzed. Meanwhile, the zero inventory mode and precise operation mode of agricultural materials under the background of multimachine cooperation of intelligent agricultural machinery were studied. Then, a precise and efficient agricultural production mode based on data-process-organization collaboration was constructed. The results showed that the multi-machine cooperative technology mode of multi-dimensional and full-chain agricultural machinery could greatly improve the efficiency of agricultural machinery, operation quality, land utilization rate and reduce production cost.

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“…The existing research on the multi-machine cooperative problem of agricultural machinery mainly addresses two aspects: path planning [4,5] and task allocation [6,7]. Cao et al used time windows to detect different types of conflicts after planning the path to address the problem of path conflicts in multi-machine cooperative path planning of Agronomy 2024, 14, 710 2 of 20 agricultural machinery [8].…”

Section: Introductionmentioning

confidence: 99%

Task Allocation of Multi-Machine Collaborative Operation for Agricultural Machinery Based on the Improved Fireworks Algorithm

Zhu,

Wang,

Pan

et al. 2024

Agronomy

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Currently, the multi-machine collaboration of agricultural machinery is one of the international frontiers and a topic of research interest in the field of agricultural equipment. However, the multi-machine cooperative operation of agricultural machinery is mostly limited to the research on task goal planning and cooperative path optimization of a single operation. To address the mentioned shortcomings, this study addresses the problem of multi-machine cooperative operation of fertilizer applicators in fields with different fertility and fertilizer cooperative distribution of fertilizer trucks. The research uses the task allocation method of a multi-machine cooperative operation of applying fertilizer-transporting fertilizer. First, the problems of fertilizer applicator operation and fertilizer truck fertilizer distribution are defined, and the operating time and the distribution distance are used as optimization objectives to construct functions to establish task allocation mathematical models. Second, a Chaos–Cauchy Fireworks Algorithm (CCFWA), which includes a discretized decoding method, a population initialization with a chaotic map, and a Cauchy mutation operation, is developed. Finally, the proposed algorithm is verified by tests in an actual scenario of fertilizer being applied in the test area of Jimo District, Qingdao City, Shandong Province. The results show that compared to the Fireworks Algorithm, Genetic Algorithm, and Particle Swarm Optimization, the proposed CCFWA can address the problem of falling into a local optimum while guaranteeing the convergence speed. Also, the variance of the CCFWA is reduced by more than 48% compared with the other three algorithms. The proposed method can realize multi-machine cooperative operation and precise distribution of seeds and fertilizers for multiple seeding-fertilizer applicators and fertilizer trucks.

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Optimization of Agricultural Machinery Task Scheduling Algorithm Based on Multiobjective Optimization

Cited by 7 publications

References 9 publications

Path Planning and Tracking Control of Tracked Agricultural Machinery Based on Improved A* and Fuzzy Control

Path Planning and Tracking Control of Tracked Agricultural Machinery Based on Improved A* and Fuzzy Control

Multi-machine collaboration realization conditions and precise and efficient production mode of intelligent agricultural machinery

Task Allocation of Multi-Machine Collaborative Operation for Agricultural Machinery Based on the Improved Fireworks Algorithm

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