Design and field testing of a polygonal paddy infield path planner for unmanned tillage operations

Han, Xiongzhe; Kim, Hak-Jin; Jeon, Chan-Woo; Moon, Hee Chang; Kim, Jung Hun; Seo, Il Won

doi:10.1016/j.compag.2021.106567

Cited by 11 publications

(7 citation statements)

References 39 publications

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“…The field boundaries and obstacles are identified to plan optimal paths for the tractor operations along predetermined routes. Path planning methods can be categorized into two types: global (macroscale) and local (microscale) [59], as shown in Figure 7. Each method includes various path-planning algorithms.…”

Section: Optimization Strategiesmentioning

confidence: 99%

Applications of Autonomous Navigation Technologies for Unmanned Agricultural Tractors: A Review

Qu,

Zhang,

Qin

et al. 2024

Machines

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The development of unmanned agricultural tractors (UAT) represents a significant step towards intelligent agricultural equipment. UAT technology is expected to lighten the workload of laborers and enhance the accuracy and efficiency of mechanized operations. Through the investigation of 123 relevant studies in the literature published in recent years, this article reviews three aspects of autonomous navigation technologies for UATs: perception, path planning and tracking, and motion control. The advantages and deficiencies of these technologies in the context of UATs are clarified by analyzing technical principles and the status of current research. We conduct summaries and analyses of existing unmanned navigation solutions for different application scenarios in order to identify current bottleneck issues. Based on the analysis of the applicability of autonomous navigation technologies in UATs, it can be seen that fruitful research progress has been achieved. The review also summarizes the common problems seen in current UAT technologies. The application of research to the sharing and integrating of multi-source data for autonomous navigation has so far been relatively weak. There is an urgent need for high-precision and high-stability sensing equipment. The universality of path planning methods and the efficiency and precision of path tracking need to be improved, and it is also necessary to develop highly reliable electrical control modules to enhance motion control performance. Overall, advanced sensors, high-performance intelligent algorithms, and reliable electrical control hardware are key factors in promoting the development of UAT technology.

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Section: Optimization Strategiesmentioning

confidence: 99%

Applications of Autonomous Navigation Technologies for Unmanned Agricultural Tractors: A Review

Qu,

Zhang,

Qin

et al. 2024

Machines

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show abstract

“…They analyze and compare a variety of point-to-point methods and coverage path planning methods, and conclude that path planning applications in the agricultural field are very few, and the methods need to be optimized [33]. Han et al adopt a path tracking method based on slip estimation to implement a path planner for agricultural robots with auto-steering control, which reduces the restriction of the shape of the farmland [34]. Saba et al apply a model-based reinforcement learning method to build an accurate environment model in an unknown dynamic environment to enable multiple UGVs to learn an environment map, and then implement path planning in the established environment model [35].…”

Section: Related Workmentioning

confidence: 99%

The Intelligent Path Planning System of Agricultural Robot via Reinforcement Learning

Yang

Chen

2022

Sensors

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Agricultural robots are one of the important means to promote agricultural modernization and improve agricultural efficiency. With the development of artificial intelligence technology and the maturity of Internet of Things (IoT) technology, people put forward higher requirements for the intelligence of robots. Agricultural robots must have intelligent control functions in agricultural scenarios and be able to autonomously decide paths to complete agricultural tasks. In response to this requirement, this paper proposes a Residual-like Soft Actor Critic (R-SAC) algorithm for agricultural scenarios to realize safe obstacle avoidance and intelligent path planning of robots. In addition, in order to alleviate the time-consuming problem of exploration process of reinforcement learning, this paper proposes an offline expert experience pre-training method, which improves the training efficiency of reinforcement learning. Moreover, this paper optimizes the reward mechanism of the algorithm by using multi-step TD-error, which solves the probable dilemma during training. Experiments verify that our proposed method has stable performance in both static and dynamic obstacle environments, and is superior to other reinforcement learning algorithms. It is a stable and efficient path planning method and has visible application potential in agricultural robots.

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“…In this method, the optimal route is obtained by analyzing the spraying voyage outside the spraying operation area and combining with the full-coverage route planning in order to avoid the problems of repeated and missed application in aerial pesticide application. Han et al (2021) proposed an infield path planner that can automatically generate pattern-based X-turn path diagrams to provide efficient operation routes for polygonal rice fields. Xu et al (2020) proposed a UAV route design algorithm for farmland with obstacles.…”

Section: Introductionmentioning

confidence: 99%

Design and validation of a multi-objective waypoint planning algorithm for UAV spraying in orchards based on improved ant colony algorithm

Tian

et al. 2023

Front. Plant Sci.

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IntroductionCurrent aerial plant protection with Unmanned Aerial Vehicles (UAV) usually applies full coverage route planning, which is challenging for plant protection operations in the orchards in South China. Because the fruit planting has the characteristics of dispersal and irregularity, full-coverage route spraying causes re-application as well as missed application, resulting in environmental pollution. Therefore, it is of great significance to plan an efficient, low-consumption and accurate plant protection route considering the flight characteristics of UAVs and orchard planting characteristics.MethodsThis study proposes a plant protection route planning algorithm to solve the waypoint planning problem of UAV multi-objective tasks in orchard scenes. By improving the heuristic function in Ant Colony Optimization (ACO), the algorithm combines corner cost and distance cost for multi-objective node optimization. At the same time, a sorting optimization mechanism was introduced to speed up the iteration speed of the algorithm and avoid the influence of inferior paths on the optimal results. Finally, Multi-source Ant Colony Optimization (MS-ACO) was proposed after cleaning the nodes of the solution path.ResultsThe simulation results of the three test fields show that compared with ACO, the path length optimization rate of MS-ACO are 3.89%, 4.6% and 2.86%, respectively, the optimization rate of total path angles are 21.94%, 45.06% and 55.94%, respectively, and the optimization rate of node numbers are 61.05%, 74.84% and 75.47%, respectively. MS-ACO can effectively reduce the corner cost and the number of nodes. The results of field experiments show that for each test field, MS-ACO has a significant optimization effect compared with ACO, with an optimization rate of energy consumption per meter of more than 30%, the optimization rate of flight time are 46.67%, 56% and 59.01%, respectively, and the optimization rate of corner angle are 50.76%, 61.78% and 71.1%, respectively.DiscussionThe feasibility and effectiveness of the algorithm were further verified. The algorithm proposed in this study can optimize the spraying path according to the position of each fruit tree and the flight characteristics of UAV, effectively reduce the energy consumption of UAV flight, improve the operating efficiency, and provide technical reference for the waypoint planning of plant protection UAV in the orchard scene.

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Design and field testing of a polygonal paddy infield path planner for unmanned tillage operations

Cited by 11 publications

References 39 publications

Applications of Autonomous Navigation Technologies for Unmanned Agricultural Tractors: A Review

Applications of Autonomous Navigation Technologies for Unmanned Agricultural Tractors: A Review

The Intelligent Path Planning System of Agricultural Robot via Reinforcement Learning

Design and validation of a multi-objective waypoint planning algorithm for UAV spraying in orchards based on improved ant colony algorithm

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