Rapid citrus harvesting motion planning with pre-harvesting point and quad-tree

Wang, Yi; Liu, Dun; Zhao, Hongmei; Li, Yali; Song, Wei-Meng; Liu, Meilin; Tian, Lei; Yan, Xiaohao

doi:10.1016/j.compag.2022.107348

Cited by 14 publications

(7 citation statements)

References 28 publications

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“…This was also verified by Zhang et al [4] who harvested tomatoes using RRT* with path length reduced by 24%. Similarly, Wang et al [5] concluded that for a fruit-harvesting robot, harvesting time was reduced by 40-60% by using RRT*. Moreover, Lehnert et al [6] picked sweet pepper using RRT* successfully avoiding most obstacles.…”

Section: Literature Analysismentioning

confidence: 99%

In-Depth Evaluation of Automated Fruit Harvesting in Unstructured Environment for Improved Robot Design

Zeeshan,

Aized,

Riaz

2024

Machines

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Using modern machines like robots comes with its set of challenges when encountered with unstructured scenarios like occlusion, shadows, poor illumination, and other environmental factors. Hence, it is essential to consider these factors while designing harvesting robots. Fruit harvesting robots are modern automatic machines that have the ability to improve productivity and replace labor for repetitive and laborious harvesting tasks. Therefore, the aim of this paper is to design an improved orange-harvesting robot for a real-time unstructured environment of orchards, mainly focusing on improved efficiency in occlusion and varying illumination. The article distinguishes itself with not only an efficient structural design but also the use of an enhanced convolutional neural network, methodologically designed and fine-tuned on a dataset tailored for oranges integrated with position visual servoing control system. Enhanced motion planning uses an improved rapidly exploring random tree star algorithm that ensures the optimized path for every robot activity. Moreover, the proposed machine design is rigorously tested to validate the performance of the fruit harvesting robot. The unique aspect of this paper is the in-depth evaluation of robots to test five areas of performance that include not only the accurate detection of the fruit, time of fruit picking, and success rate of fruit picking, but also the damage rate of fruit picked as well as the consistency rate of the robot picking in varying illumination and occlusion. The results are then analyzed and compared with the performance of a previous design of fruit harvesting robot. The study ensures improved results in most aspects of the design for performance in an unstructured environment.

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Section: Literature Analysismentioning

confidence: 99%

In-Depth Evaluation of Automated Fruit Harvesting in Unstructured Environment for Improved Robot Design

Zeeshan,

Aized,

Riaz

2024

Machines

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“…It makes the path quality greatly improved. In the new node extension, and Zhang et al (2019), Liu et al (2020), and Wang et al (2020a, 2022b introduced the dynamic step into the RRT algorithm to adaptively extend a certain step size to determine the location of new nodes. In this way, the RRT algorithm could ensure the success rate of new nodes in the area of dense obstacles and rapidly expand new nodes in the area of sparse obstacles, thus enabling the algorithm to achieve better efficiency.…”

Section: Measuring Connection Processmentioning

confidence: 99%

A survey of path planning of industrial robots based on rapidly exploring random trees

Luo,

Zhang,

Zhuang

et al. 2023

Front. Neurorobot.

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Path planning is an essential part of robot intelligence. In this paper, we summarize the characteristics of path planning of industrial robots. And owing to the probabilistic completeness, we review the rapidly-exploring random tree (RRT) algorithm which is widely used in the path planning of industrial robots. Aiming at the shortcomings of the RRT algorithm, this paper investigates the RRT algorithm for path planning of industrial robots in order to improve its intelligence. Finally, the future development direction of the RRT algorithm for path planning of industrial robots is proposed. The study results have particularly guided significance for the development of the path planning of industrial robots and the applicability and practicability of the RRT algorithm.

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“…Under normal circumstances, the path planned by the blue vehicle cannot pass through these areas under the influence of the structured road. At this time, it can consider Yi et al's improved P_RRT * algorithm [22], Xiang et al's improved A * algorithm [23] or Wang et al's PGI-RRT * algorithm [24]. These algorithms are effective algorithms in the face of complex static obstacles.…”

Section: Related Workmentioning

confidence: 99%

Investigations on Speed Planning Algorithm and Trajectory Tracking Control of Intersection Scenarios Without Traffic Signs

Liang

Xing

2023

IEEE Access

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Efficient intersection planning is one of the most challenging tasks for an autonomous vehicle at present. The degree of response to other traffic participants and whether the path tracking effect is good are the key aspects that determine the performance of planning algorithm for an autonomous vehicle. In order to deal with these problems, in this paper, the frame structure of POMDP (Partially Observable Markov Decision Processes) and the time distance coordinate system are combined to carry out the speed planning for the self-propelled vehicle. The speed planning designs a novel POMDP model to ensure effective prediction of behavioral intentions and decision-making at intersections with no traffic signs. Specifically, the method is considered both the driving position and the time range of arrival of the vehicle at the intersection in realworld situations. At the same time, the algorithm is projected the state information into the time distance coordinate system in the form of probability, and used the value iteration method to solve the optimal solution, so as to improve the adaptability of the algorithm to real-world scenarios. Furthermore, MPC control is added on this basis. Rational use of dynamic MPC and kinematic MPC can effectively reduce the tracking error, improve the stability of tracking, and carry out finer control when the speed frequently fluctuates.INDEX TERMS POMDP, MPC, distance time coordinate system.

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Rapid citrus harvesting motion planning with pre-harvesting point and quad-tree

Cited by 14 publications

References 28 publications

In-Depth Evaluation of Automated Fruit Harvesting in Unstructured Environment for Improved Robot Design

In-Depth Evaluation of Automated Fruit Harvesting in Unstructured Environment for Improved Robot Design

A survey of path planning of industrial robots based on rapidly exploring random trees

Investigations on Speed Planning Algorithm and Trajectory Tracking Control of Intersection Scenarios Without Traffic Signs

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