Trajectory Planning of Redundant Manipulators Moving along Constrained Path and Avoiding Obstacles

Chembuly, V. V. M. J. Satish; Voruganti, Hari K.

doi:10.1016/j.procs.2018.07.094

Cited by 22 publications

(9 citation statements)

References 16 publications

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“…This is because the collision between the manipulator and obstacle would cause the failure of path planning, and may further cause the damage to the manipulator (and obstacle). Numerous studies have been reported and presented to realize the obstacle avoidance of redundant manipulators [1]- [3], [7]- [9].…”

Section: Introductionmentioning

confidence: 99%

Acceleration-Level Obstacle Avoidance of Redundant Manipulators

2019

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Avoiding obstacle during path planning is a crucial issue in redundant manipulators. In this study, the obstacle avoidance of redundant manipulators, which is investigated at acceleration level, is presented. Specifically, a new acceleration-level inequality is designed and formulated, which is proven to be capable of avoiding obstacle. By combining the end-effector planning requirement (formulated as equality constraint) and incorporating the physical limits (formulated as bound constraints), the accelerationlevel obstacle avoidance (ALOA) scheme for redundant manipulators is proposed. Such scheme is transformed into a quadratic program and is computed by a numerical algorithm. Simulation results under the PA10 manipulator with different obstacles further validate the effective performance of the proposed ALOA scheme. INDEX TERMS Acceleration-level obstacle avoidance, redundant manipulators, quadratic program.

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Section: Introductionmentioning

confidence: 99%

Acceleration-Level Obstacle Avoidance of Redundant Manipulators

2019

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“…In order to inspect the predictive effect of the method, four commonly measure standard are used to evaluate it, including Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Accuracy (Acc), and determination coefficient R-square ( 2 R ). The corresponding expressions are shown in (23).…”

Section: Evaluation Metricsmentioning

confidence: 99%

“…It was proved that the experimental results were superior to the method in [24]. Chembuly et al [23] proposed a trajectory planning method based on the minimizing total power consumption taking into account kinematics, dynamics constraints, and obstacles in the workspace. Joint impact was the derivative of angular acceleration, which had direct effect on motion stability.…”

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confidence: 99%

Joint Trajectory Prediction of Multi-Linkage Robot Based on Graph Convolutional Network

Yang

2020

IEEE Access

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“…A robot manipulator is a nonlinear and uncertain system. Manipulator trajectory planning should not only consider obstacle avoidance, trajectory accuracy, smooth operation, energy consumption, among other factors, but also needs to consider the problems of external interference, communication delay, and the nonlinearity and uncertainty of robot manipulators [ 2 , 3 , 4 , 5 ]. In order to solve these problems, many researchers have studied the kinematics formula, dynamic model, and control technology of robot manipulators.…”

Section: Introductionmentioning

confidence: 99%

Trajectory Planning of Robot Manipulator Based on RBF Neural Network

Song

Bai

et al. 2021

Entropy

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Robot manipulator trajectory planning is one of the core robot technologies, and the design of controllers can improve the trajectory accuracy of manipulators. However, most of the controllers designed at this stage have not been able to effectively solve the nonlinearity and uncertainty problems of the high degree of freedom manipulators. In order to overcome these problems and improve the trajectory performance of the high degree of freedom manipulators, a manipulator trajectory planning method based on a radial basis function (RBF) neural network is proposed in this work. Firstly, a 6-DOF robot experimental platform was designed and built. Secondly, the overall manipulator trajectory planning framework was designed, which included manipulator kinematics and dynamics and a quintic polynomial interpolation algorithm. Then, an adaptive robust controller based on an RBF neural network was designed to deal with the nonlinearity and uncertainty problems, and Lyapunov theory was used to ensure the stability of the manipulator control system and the convergence of the tracking error. Finally, to test the method, a simulation and experiment were carried out. The simulation results showed that the proposed method improved the response and tracking performance to a certain extent, reduced the adjustment time and chattering, and ensured the smooth operation of the manipulator in the course of trajectory planning. The experimental results verified the effectiveness and feasibility of the method proposed in this paper.

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Trajectory Planning of Redundant Manipulators Moving along Constrained Path and Avoiding Obstacles

Cited by 22 publications

References 16 publications

Acceleration-Level Obstacle Avoidance of Redundant Manipulators

Acceleration-Level Obstacle Avoidance of Redundant Manipulators

Joint Trajectory Prediction of Multi-Linkage Robot Based on Graph Convolutional Network

Trajectory Planning of Robot Manipulator Based on RBF Neural Network

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