Constrained Multilegged Robot System Modeling and Fuzzy Control With Uncertain Kinematics and Dynamics Incorporating Foot Force Optimization

This paper studies the optimal distribution of feet forces and control of multilegged robots with uncertainties in both kinematics and dynamics. First, a constrained dynamics for multilegged robots and the constrained environment model are established by considering both kinematic and dynamic uncert...

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Vydáno v:IEEE transactions on systems, man, and cybernetics. Systems Ročník 46; číslo 1; s. 1 - 15
Hlavní autoři: Li, Zhijun, Xiao, Shengtao, Ge, Shuzhi Sam, Su, Hang
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York IEEE 01.01.2016
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Constraints
Dynamical systems
Dynamics
Foot
Force
Forces distribution
Fuzzy logic
fuzzy-based motion/force control
Kinematics
kinematics and dynamics uncertainties
Legged locomotion
multilegged robot
Neural networks
Nonlinear dynamics
Optimization
Robots
Uncertainty
multilegged robot
Forces distribution
fuzzy-based motion/force control
kinematics and dynamics uncertainties
ISSN:2168-2216, 2168-2232
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Shrnutí:This paper studies the optimal distribution of feet forces and control of multilegged robots with uncertainties in both kinematics and dynamics. First, a constrained dynamics for multilegged robots and the constrained environment model are established by considering both kinematic and dynamic uncertainties. Under an external wrench for multilegged robots, the foot forces and moments of the supporting legs can be formulated as quadratic programming problems subject to linear and nonlinear constraints. The neurodynamics of recurrent neural network is developed for foot force optimization. For the obtained optimized tip-point force and the motion of legs, we propose a hybrid task-space trajectory and force tracking based on fuzzy system and adaptive mechanism that are used to compensate for the external perturbation, kinematics, and dynamics uncertainties. The tracking of task-space trajectory and constraint force is achieved under unknown dynamical parameters, constraints, and disturbances. Extensive simulations have been provided to verify the effectiveness of the proposed scheme.
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ISSN:2168-2216
2168-2232
DOI:10.1109/TSMC.2015.2422267