Q-learning trajectory planning based on Takagi–Sugeno fuzzy parallel distributed compensation structure of humanoid manipulator

Wen, Shuhuan; Hu, Xueheng; Lv, Xiaohan; Wang, Zongtao; Peng, Yong

doi:10.1177/1729881419830204

Cited by 11 publications

(12 citation statements)

References 25 publications

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“…NAO (Wen et al, 2019) robot shown in Figure 12 is a humanoid robot with biped walking. There is a laser scanner at the head of NAO robot.…”

Section: Methodsmentioning

confidence: 99%

NAO robot obstacle avoidance based on fuzzy Q-learning

Wen

et al. 2019

Self Cite

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Purpose This paper aims to propose a novel active SLAM framework to realize avoid obstacles and finish the autonomous navigation in indoor environment. Design/methodology/approach The improved fuzzy optimized Q-Learning (FOQL) algorithm is used to solve the avoidance obstacles problem of the robot in the environment. To reduce the motion deviation of the robot, fractional controller is designed. The localization of the robot is based on FastSLAM algorithm. Findings Simulation results of avoiding obstacles using traditional Q-learning algorithm, optimized Q-learning algorithm and FOQL algorithm are compared. The simulation results show that the improved FOQL algorithm has a faster learning speed than other two algorithms. To verify the simulation result, the FOQL algorithm is implemented on a NAO robot and the experimental results demonstrate that the improved fuzzy optimized Q-Learning obstacle avoidance algorithm is feasible and effective. Originality/value The improved fuzzy optimized Q-Learning (FOQL) algorithm is used to solve the avoidance obstacles problem of the robot in the environment. To reduce the motion deviation of the robot, fractional controller is designed. To verify the simulation result, the FOQL algorithm is implemented on a NAO robot and the experimental results demonstrate that the improved fuzzy optimized Q-Learning obstacle avoidance algorithm is feasible and effective.

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“…NAO (Wen et al, 2019) robot shown in Figure 12 is a humanoid robot with biped walking. There is a laser scanner at the head of NAO robot.…”

Section: Methodsmentioning

confidence: 99%

NAO robot obstacle avoidance based on fuzzy Q-learning

Wen

et al. 2019

Self Cite

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“…From [33], it is known that if the sufficient conditions ( 18)-( 20) are satisfied by Theorem 1, the ellipsoid (31), which is inside the domain of attraction, is contractively invariant. Then, the following condition of actuator saturation is obtained by (10) and (14).…”

Section: Theorem 1 the It2 T-s Fuzzy System (mentioning

confidence: 99%

“…The T-S fuzzy model can also be used to solve the actuator saturation problem for nonlinear systems [7][8][9]. In order to develop the fuzzy controller design method, a so-called "Parallel Distributed Compensation" (PDC) method was proposed for the T-S fuzzy model [10][11][12][13]. However, the state of practical engineer systems or controllers may differ from the original due to service life and frequency.…”

Section: Introductionmentioning

confidence: 99%

Actuator Saturated Fuzzy Controller Design for Interval Type-2 Takagi-Sugeno Fuzzy Models with Multiplicative Noises

Chang

Lin

et al. 2021

Processes

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In many practical systems, stochastic behaviors usually occur and need to be considered in the controller design. To ensure the system performance under the effect of stochastic behaviors, the controller may become bigger even beyond the capacity of practical applications. Therefore, the actuator saturation problem also must be considered in the controller design. The type-2 Takagi-Sugeno (T-S) fuzzy model can describe the parameter uncertainties more completely than the type-1 T-S fuzzy model for a class of nonlinear systems. A fuzzy controller design method is proposed in this paper based on the Interval Type-2 (IT2) T-S fuzzy model for stochastic nonlinear systems subject to actuator saturation. The stability analysis and some corresponding sufficient conditions for the IT2 T-S fuzzy model are developed using Lyapunov theory. Via transferring the stability and control problem into Linear Matrix Inequality (LMI) problem, the proposed fuzzy control problem can be solved by the convex optimization algorithm. Finally, a nonlinear ship steering system is considered in the simulations to verify the feasibility and efficiency of the proposed fuzzy controller design method.

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“…14 Wen et al proposed a trajectory planning based on reinforcement learning, which is archived by applying the Q-learning algorithm. 15 Zajačko et al applied artificial intelligence to an automated quality control process through an automated error detection system. 16 Pivarčiová et al controlled and corrected the programmed mobile robot trajectory by implementing an inertial navigation system.…”

Section: Introductionmentioning

confidence: 99%

Dual-optimization trajectory planning based on parametric curves for a robot manipulator

Kuo

Lin

2020

International Journal of Advanced Robotic Systems

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This article presents a dual-optimization trajectory planning algorithm, which consists of the optimal path planning and the optimal motion profile planning for robot manipulators, where the path planning is based on parametric curves. In path planning, a virtual-knot interpolation is proposed for the paths required to pass through all control points, so the common curves, such as Bézier curves and B-splines, can be incorporated into it. Besides, an optimal B-spline is proposed to generate a smoother and shorter path, and this scheme is especially suitable for closed paths. In motion profile planning, a generalized formulation of time-optimal velocity profiles is proposed, which can be implemented to any types of motion profiles with equality and inequality constraints. Also, a multisegment cubic velocity profile is proposed by solving a multiobjective optimization problem. Furthermore, a case study of a dispensing robot is investigated through the proposed dual-optimization algorithm applied to numerical simulations and experimental work.

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Q-learning trajectory planning based on Takagi–Sugeno fuzzy parallel distributed compensation structure of humanoid manipulator

Cited by 11 publications

References 25 publications

NAO robot obstacle avoidance based on fuzzy Q-learning

NAO robot obstacle avoidance based on fuzzy Q-learning

Actuator Saturated Fuzzy Controller Design for Interval Type-2 Takagi-Sugeno Fuzzy Models with Multiplicative Noises

Dual-optimization trajectory planning based on parametric curves for a robot manipulator

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