Application of GRNN to Plan Trajectory for a Picking Robot

Abstract:

In order to supply reference inputs for the robot system, a high-order polynomial function is proposed according to particular strategies to plan trajectory in joint space, which could provide continuous velocity, acceleration and jerk to ensure safety for electric actuators and stability of the robot during the motion process. The general regression neural network (GRNN) is constructed to realize the proposed polynomial function for its powerful non-linear mapping ability. Parameters of the spreading factor and number of training samples which influence the performances of the network are detailed analyzed. Simulation results show that GRNN has advantages of favorable stability and high precision of function approaching even with few samples. Finally, the trajectory planner based on GRNN is successfully applied for an articulated picking robot to realize the real-time control.

• Periodical: Advanced Materials Research (Volumes 468-471)
• Pages: 194-199
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.468-471.194
• Online since: February 2012
• Authors: Yan Wang, Hui Lin Yao
• Keywords: General Regression Neural Network (GRNN), Picking Robot, Polynomial Function, Trajectory Planning

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