Recent Advances on Manipulator Trajectory Planning Methods

Qiu, Hao-ran; Xu, Zhenbang; Hu, Ping

doi:10.2174/2212797613666200319151513

Cited by 4 publications

(1 citation statement)

References 55 publications

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“…Improving the motion smoothness of the robot has become a key problem to be solved in the practical application of space robots. The trajectory designning research of the manipulator is the premise and foundation to finish off the purpose of whether the manipulator can run smoothly better [2] .…”

Section: Introductionmentioning

confidence: 99%

Research on Trajectory Planning and Simulation of Six-Degree-of-Freedom Manipulator

Jiang

Zhang

2023

J. Phys.: Conf. Ser.

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To make the time of the mechanical arm in joint space movement as short as possible and improve work efficiency, it is momentous to plan the movement trajectory of the mechanical arm to content the necessary condition of a smooth movement curve of the mechanical arm. According to the working principle of the 6-DOF mechanical arm, the D-H modeling method is used to calculate the D-H arguments, and the six-degree-of-freedom mechanical arm D-H model is established using MATLAB’s Robotics toolbox, and the positive kinematics analysis is performed. According to the set up robot arm model, the trajectory designning of the mechanical arm to run by using the three-time polynomial interpolation technique and the five-time polynomial interpolation technique in the joint space comparison, and it is simulated in MATLAB. Simulation experiments show that the five-time polynomial interpolation technique makes the running curve of the mechanical arm smoother and more stable, and has more application value.

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Section: Introductionmentioning

confidence: 99%

Research on Trajectory Planning and Simulation of Six-Degree-of-Freedom Manipulator

Jiang

Zhang

2023

J. Phys.: Conf. Ser.

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Sliding Mode Control of a 2DOF Robot Manipulator: A Simulation Study Using Artificial Neural Networks with Minimum Parameter Learning

Saidi

Touati

2022

MENG

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Background: In this paper, we have developed an intelligent control law for the control of mobile manipulator robots by investigating the various techniques proposed in the literature. Thus, we have adopted a hybrid approach that integrates a part of classical and advanced automation in order to create an efficient control structure that can cope with a certain level of complexity. Our research logic is based on the process of keeping in mind that the control system must comply with the constraints imposed during the implementation of the control architecture. Objective: This paper aims to develop a control law in order to guarantee a certain level of performance, more precisely, during a trajectory tracking application for mobile handling missions. The developed control law guarantees robustness with respect to external disturbances and parametric uncertainties due to the modelling of the system. Methods: In this paper, a study of the basic concepts of robotics and robot modelling is presented in order to set up the dynamic model used for the elaboration of the command. A sliding-mode controller based on a radial base function neural network with minimum parameter learning is developed for the Pelican robot as a two-link robot manipulator. This approach, which combines a radial base function neuronal network (RBFNN) and a sliding mode control (SMC), is presented for the tracking control of this class of systems with unknown non-linearities. The centre and output weights of the RBFNN are updated via online learning in accordance with the adaptive laws, allowing the control output of the neural network to approach the equivalent control in the sliding mode in the predetermined direction. The Lyapunov function is used to develop the adaptive control algorithm based on the RBFNN model. For reducing the computational load and increasing real-time arm performance, an RBFNN-based on the SMC with the Minimum Parameter Learning (MPL) method is designed. Results: Neural network sliding mode control is designed to underline the effectiveness of the approach to control the manipulator;—this method of control is used to ensure the tracking trajectories. Conclusion: The results of the simulation for the manipulator's arm demonstrated the effectiveness of the modelling strategy, the correction, and the robustness of the control approach.

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Feasible Trajectories Generation for Autonomous Driving Vehicles

et al. 2021

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This study presents smooth and fast feasible trajectory generation for autonomous driving vehicles subject to the vehicle physical constraints on the vehicle power, speed, acceleration as well as the hard limitations of the vehicle steering angle and the steering angular speed. This is due to the fact the vehicle speed and the vehicle steering angle are always in a strict relationship for safety purposes, depending on the real vehicle driving constraints, the environmental conditions, and the surrounding obstacles. Three different methods of the position quintic polynomial, speed quartic polynomial, and symmetric polynomial function for generating the vehicle trajectories are presented and illustrated with simulations. The optimal trajectory is selected according to three criteria: Smoother curve, smaller tracking error, and shorter distance. The outcomes of this paper can be used for generating online trajectories for autonomous driving vehicles and auto-parking systems.

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Recent Advances on Manipulator Trajectory Planning Methods

Cited by 4 publications

References 55 publications

Research on Trajectory Planning and Simulation of Six-Degree-of-Freedom Manipulator

Research on Trajectory Planning and Simulation of Six-Degree-of-Freedom Manipulator

Sliding Mode Control of a 2DOF Robot Manipulator: A Simulation Study Using Artificial Neural Networks with Minimum Parameter Learning

Feasible Trajectories Generation for Autonomous Driving Vehicles

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