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A real-time collision-free path planning of a rust removal robot using an improved neural network

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Abstract

In this paper, a real-time collision-free path planning of the rust removal robot in a ship environment is proposed, which is based on an improved biologically inspired neural network algorithm. This improved algorithm is based on the biologically inspired neural network and modified with obstacle detection sensors and kinematic state templates, and is implemented in a ship rust removal robot planning system for dynamic trajectory generation. The real-time optimal trajectory is generated by the biologically inspired neural network, and the moving obstacle detection process of a ship robot working on the wall is simulated with the obstacle detection sensors models. The local real-time trajectory can be re-planned by the updated local map information, where the obstacle detection sensors are used to inspect partial environment information and update the robot nearby information in real time in the original neural network algorithm. At the same time, the method of the kinematic state templates matching and searching is used to solve the pipes’ influence of the rust removal robot climbing on the wall, which can not only provide a smooth path, but also can judge the motion direction and turning angle of the robot. Comparison of the proposed approach with the simulation shows that the improved algorithm is capable of planning a real-time collision-free path with achieving the local environmental information and judging the rust removal robot’s motion direction and turning angle. This proposed algorithm can be good used in the ship rust removal robot.

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Authors and Affiliations

  1. School of Mechanical Engineering, Hangzhou Dianzi University, Hangzhou, 310018, China

    Ling Sun  (孙玲)

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  1. Ling Sun  (孙玲)
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Correspondence to Ling Sun  (孙玲).

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Sun, L. A real-time collision-free path planning of a rust removal robot using an improved neural network. J. Shanghai Jiaotong Univ. (Sci.) 22, 633–640 (2017). https://doi.org/10.1007/s12204-017-1881-x

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  • DOI: https://doi.org/10.1007/s12204-017-1881-x

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