Path planning for mobile robots in unstructured orchard environments: An improved kinematically constrained bi-directional RRT approach

Ye, Lei; Wu, Fengyun; Zou, Xiangjun; Li, Jin

doi:10.1016/j.compag.2023.108453

Cited by 49 publications

(16 citation statements)

References 16 publications

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“…Traditional manual harvesting, with low efficiency and high labor costs, cannot meet the demand for efficient harvesting [3]. The development of fruit-picking robots could improve fruit harvesting efficiency and save labor costs [4][5][6]. Ripeness identification is a key step for picking robots to realize selective harvesting and a prerequisite for counting crops and estimating yields [7,8].…”

Section: Introductionmentioning

confidence: 99%

Strawberry Detection and Ripeness Classification Using YOLOv8+ Model and Image Processing Method

Wang,

Han

et al. 2024

Agriculture

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As strawberries are a widely grown cash crop, the development of strawberry fruit-picking robots for an intelligent harvesting system should match the rapid development of strawberry cultivation technology. Ripeness identification is a key step to realizing selective harvesting by strawberry fruit-picking robots. Therefore, this study proposes combining deep learning and image processing for target detection and classification of ripe strawberries. First, the YOLOv8+ model is proposed for identifying ripe and unripe strawberries and extracting ripe strawberry targets in images. The ECA attention mechanism is added to the backbone network of YOLOv8+ to improve the performance of the model, and Focal-EIOU loss is used in loss function to solve the problem of imbalance between easy- and difficult-to-classify samples. Second, the centerline of the ripe strawberries is extracted, and the red pixels in the centerline of the ripe strawberries are counted according to the H-channel of their hue, saturation, and value (HSV). The percentage of red pixels in the centerline is calculated as a new parameter to quantify ripeness, and the ripe strawberries are classified as either fully ripe strawberries or not fully ripe strawberries. The results show that the improved YOLOv8+ model can accurately and comprehensively identify whether the strawberries are ripe or not, and the mAP50 curve steadily increases and converges to a relatively high value, with an accuracy of 97.81%, a recall of 96.36%, and an F1 score of 97.07. The accuracy of the image processing method for classifying ripe strawberries was 91.91%, FPR was 5.03%, and FNR was 14.28%. This study demonstrates the program’s ability to quickly and accurately identify strawberries at different stages of ripeness in a facility environment, which can provide guidance for selective picking by subsequent fruit-picking robots.

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Section: Introductionmentioning

confidence: 99%

Strawberry Detection and Ripeness Classification Using YOLOv8+ Model and Image Processing Method

Wang,

Han

et al. 2024

Agriculture

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“…Owing to their high degree of automation, mobile robots can improve productivity and provide various conveniences. Mobile robots are currently being utilized in various scenarios ( Ferguson and Stentz, 2006 ; Zhao et al., 2014 ; Ljungqvist et al., 2019 ; Reid et al., 2020 ; Strader et al., 2020 ; Buchanan et al., 2021 ; Ye et al., 2023 ). As agricultural automation research has gained widespread attention in recent years, mobile robots have begun to make their way into agricultural settings ( Xiong et al., 2017 ; Blok et al., 2019 ; Ren et al., 2020 ).…”

Section: Introductionmentioning

confidence: 99%

“…In large and complex orchard environments, efficiently determining the navigation paths for mobile robots is of concern ( Ye et al., 2023 ). The rapid-exploration random tree (RRT) algorithm is a framework for path planning algorithms widely used by mobile robots.…”

Section: Introductionmentioning

confidence: 99%

Improving path planning for mobile robots in complex orchard environments: the continuous bidirectional Quick-RRT* algorithm

Ye,

Li,

2024

Front. Plant Sci.

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Efficient obstacle-avoidance path planning is critical for orchards with numerous irregular obstacles. This paper presents a continuous bidirectional Quick-RRT* (CBQ-RRT*) algorithm based on the bidirectional RRT (Bi-RRT) and Quick-RRT* algorithms and proposes an expansion cost function that evaluates path smoothness and length to overcome the limitations of the Quick-RRT* algorithm for non-holonomic mobile robot applications. To improve the zigzag between dual trees caused by the dual-tree expansion of the Bi-RRT algorithm, CBQ-RRT* proposes the CreateConnectNode optimization method, which effectively solves the path smoothness problem at the junction of dual trees. Simulations conducted on the ROS platform showed that the CBQ-RRT* outperformed the unidirectional Quick-RRT* in terms of efficiency for various orchard layouts and terrain conditions. Compared to Bi-RRT*, CBQ-RRT* reduced the average path length and maximum heading angle by 8.5% and 21.7%, respectively. In addition, field tests confirmed the superior performance of the CBQ-RRT*, as evidenced by an average maximum path lateral error of 0.334 m, a significant improvement over Bi-RRT* and Quick-RRT*. These improvements demonstrate the effectiveness of the CBQ-RRT* in complex orchard environments.

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“…With advances in the Internet ofThings, mobile platforms such as moblie inspection robots make precision agriculture develop quickly ( Ye et al., 2023 ; Tang et al., 2024 ). Due to the conflict between the high computational power requirements of the models and the limited computational power of plant protection equipment, it is a challenging task to deploy efficient and lightweight plant disease detection models on mobile platforms.…”

Section: Introductionmentioning

confidence: 99%

LFMNet: a lightweight model for identifying leaf diseases of maize with high similarity

Hu,

Jiang,

Gao

et al. 2024

Front. Plant Sci.

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Maize leaf diseases significantly impact yield and quality. However, recognizing these diseases from images taken in natural environments is challenging due to complex backgrounds and high similarity of disease spots between classes.This study proposes a lightweight multi-level attention fusion network (LFMNet) which can identify maize leaf diseases with high similarity in natural environment. The main components of LFMNet are PMFFM and MAttion blocks, with three key improvements relative to existing essential blocks. First, it improves the adaptability to the change of maize leaf disease scale through the dense connection of partial convolution with different expansion rates and reduces the parameters at the same time. The second improvement is that it replaces a adaptable pooling kernel according to the size of the input feature map on the original PPA, and the convolution layer to reshape to enhance the feature extraction of maize leaves under complex background. The third improvement is that it replaces different pooling kernels to obtain features of different scales based on GMDC and generate feature weighting matrix to enhance important regional features. Experimental results show that the accuracy of the LFMNet model on the test dataset reaches 94.12%, which is better than the existing heavyweight networks, such as ResNet50 and Inception v3, and lightweight networks such as DenseNet 121,MobileNet(V3-large) and ShuffleNet V2. The number of parameters is only 0.88m, which is better than the current mainstream lightweight network. It is also effective to identify the disease types with similar disease spots in leaves.

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Path planning for mobile robots in unstructured orchard environments: An improved kinematically constrained bi-directional RRT approach

Cited by 49 publications

References 16 publications

Strawberry Detection and Ripeness Classification Using YOLOv8+ Model and Image Processing Method

Strawberry Detection and Ripeness Classification Using YOLOv8+ Model and Image Processing Method

Improving path planning for mobile robots in complex orchard environments: the continuous bidirectional Quick-RRT* algorithm

LFMNet: a lightweight model for identifying leaf diseases of maize with high similarity

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