Abstract
Collision‐free autonomous path planning under a dynamic and uncertainty vineyard environment is the most important issue which needs to be resolved firstly in the process of improving robotic harvesting manipulator intelligence. We present and apply energy optimal and artificial potential field to develop a path planning method for six degree of freedom (DOF) serial harvesting robot under dynamic uncertain environment. Firstly, the kinematical model of Six‐DOF serial manipulator was constructed by using the Denavit‐Hartenberg (D‐H) method. The model of obstacles was defined by axis‐aligned bounding box, and then the configuration space of harvesting robot was described by combining the obstacles and arm space of robot. Secondly, the harvesting sequence in path planning was computed by energy optimal method, and the anticollision path points were automatically generated based on the artificial potential field and sampling searching method. Finally, to verify and test the proposed path planning algorithm, a virtual test system based on virtual reality was developed. After obtaining the space coordinates of grape picking point and anticollision bounding volume, the path points were drew out by the proposed method. 10 times picking tests for grape anticollision path planning were implemented on the developed simulation system, and the success rate was up to 90%. The results showed that the proposed path planning method can be used to the harvesting robot.
Highlights
Planning optimal paths is an important branch in the research field of intelligent robot and an ideal path planning method is very important for improving the performance of robots [1, 2]
To conduct an undamaged robotic grape-harvesting in an unstructured vineyard, a collision-free autonomous path planning method based on minimum energy and artificial potential field for grape harvesting robot was developed in this study
The harvesting sequence in path planning was computed by energy optimal method, and the anticollision path points were generated based on the artificial potential field and sampling searching method
Summary
Planning optimal paths is an important branch in the research field of intelligent robot and an ideal path planning method is very important for improving the performance of robots [1, 2]. In the process of solving the path planning problem, if the environment information is completely knowable, the time global planning can be used to obtain an optimal path to reach the target state from the initial state [5]. The dynamic uncertainty of the obstacle and the location of the picking target make the path planning a difficult problem. In such case, it can only be based on the real-time detection of the environmental information and reprogramming to get the moving path from the current state to the target state
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