Abstract
Motion planning is a very important part of robot technology, where the quality of planning directly affects the energy consumption and safety of robots. Focusing on the shortcomings of traditional RRT methods such as long, unsmooth paths, and uncoupling with robot control system, an automatic robot motion planning method was proposed based on Rapid Exploring Random Tree called AM-RRT* (automatic motion planning based on RRT*). First, the RRT algorithm was improved by increasing the attractive potential fields of the target points of the environment, making it more directional during the sampling process. Then, a path optimization method based on a dynamic model and cubic B-spline curve was designed to make the planned path coupling with the robot controller. Finally, an RRT speed planning algorithm was added to the planned path to avoid dynamic obstacles in real time. To verify the feasibility of AM-RRT*, a detailed comparison was made between AM-RRT* and the traditional RRT series algorithms. The results showed that AM-RRT* improved the shortcomings of RRT and made it more suitable for robot motion planning in a dynamic environment. The proposal of AM-RRT* can provide a new idea for robots to replace human labor in complex environments such as underwater, nuclear power, and mines.
Supported by Guangdong Provincial Science and Technology Plan Project (Grant number 2021B1515420006, Grant number 2021B1515120026); Guangdong Province Marine Economic Development Special Fund Project(Six Major Marine Industries) (GDNRC [2021]46); National Natural Science Foundation of China (Grant number U2141216, Grant number 51875212); Shenzhen Technology Research Project (JSGG20201201100401005, JSGG20201201100400001).
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Chi, P. et al. (2024). AM-RRT*: An Automatic Robot Motion Planning Algorithm Based on RRT. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Lecture Notes in Computer Science, vol 14447. Springer, Singapore. https://doi.org/10.1007/978-981-99-8079-6_8
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