Time-optimal trajectory planning based on event-trigger and conditional proportional control

Author:

Chen GuangrongORCID,Wei Ningze,Yan Lei,Lu HuaFeng,Li Jin

Abstract

Trajectory planning is an important issue for manipulators and robots. To get a optimal trajectory, many constraints including actuators specifications, motion range of joints, workspace limitations, etc, and many objectives including the shortest time, the shortest distance, the lowest energy consumption, the minimum oscillations, obstacles-avoiding, etc, should be considered both. In this paper, firstly, the forward kinematics and inverse kinematics of a five axis manipulator are deduced. And, a simple method to choose one appropriate solution from multi solutions of inverse kinematics is proposed. Secondly, an easy-implemented optimization method of trajectory planning is proposed based on seventh order polynomial interpolation, event-trigger mechanism and conditional proportional control (P control). The proposed optimization method can capture the time optimal trajectory, and the actuators specifications including velocity, acceleration of motor can be guaranteed as well. Thirdly, comparative simulations and experiments validate the effectiveness and efficiency of proposed optimization method. The research provides an insight for the application of trajectory optimization on the micro controller with low computing capability and high real-time performance requirement.

Funder

Fundamental Research Funds for the Central Universities

National Natural Science Foundation of China

Publisher

Public Library of Science (PLoS)

Subject

Multidisciplinary

Cited by 4 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

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2. Time-Optimized Robot Trajectory Based on Improved Whale Optimization Algorithm in Intelligent Manufacturing;Journal of Computing and Information Science in Engineering;2024-04-16

3. Multi-objective trajectory optimization of the 2-redundancy planar feeding manipulator based on pseudo-attractor and radial basis function neural network;Mechanics Based Design of Structures and Machines;2023-08-21

4. Target Recognition and Automatic Drilling Control for A Tunnel Drilling Robot;2023 42nd Chinese Control Conference (CCC);2023-07-24

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