TY - GEN
T1 - Efficient algorithm for resolving manipulator redundancy--The compact QP method
AU - Cheng, Fan Tien
AU - Chen, Tsing Hua
AU - Sun, York Yih
PY - 1992/4
Y1 - 1992/4
N2 - Due to hardware limitations, physical constraints, such as joint rate bounds and joint angle limits, always exist. In the present work, these constraints are included in the general formulation of the redundant inverse kinematic problem. To take into account these physical constraints, the computationally efficient compact QP (quadratic programming) method is derived to resolve the kinematic redundancy problem. In addition, the compact-inverse QP method is developed to remedy the unescapable singularity problem. The compact QP (compact and inverse QP) method makes use of the compact formulation to obtain the general solutions and to eliminate the equality constraints. As such, the variables are decomposed into basic and free variables, and the basic variables are expressed by the free variables. Thus, the problem size is reduced and it only requires an optimization algorithm, such as QP, for the free variables subject to pure inequality constraints. This approach will expedite the optimization process and make real-time implementation possible. Two examples are given to demonstrate the generality and superiority of these two methods.
AB - Due to hardware limitations, physical constraints, such as joint rate bounds and joint angle limits, always exist. In the present work, these constraints are included in the general formulation of the redundant inverse kinematic problem. To take into account these physical constraints, the computationally efficient compact QP (quadratic programming) method is derived to resolve the kinematic redundancy problem. In addition, the compact-inverse QP method is developed to remedy the unescapable singularity problem. The compact QP (compact and inverse QP) method makes use of the compact formulation to obtain the general solutions and to eliminate the equality constraints. As such, the variables are decomposed into basic and free variables, and the basic variables are expressed by the free variables. Thus, the problem size is reduced and it only requires an optimization algorithm, such as QP, for the free variables subject to pure inequality constraints. This approach will expedite the optimization process and make real-time implementation possible. Two examples are given to demonstrate the generality and superiority of these two methods.
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M3 - Conference contribution
AN - SCOPUS:0026844048
SN - 0818627204
T3 - Proceedings - IEEE International Conference on Robotics and Automation
SP - 508
EP - 513
BT - Proceedings - IEEE International Conference on Robotics and Automation
PB - Publ by IEEE
T2 - Proceedings 1992 IEEE International Conference on Robotics and Automation
Y2 - 12 May 1992 through 14 May 1992
ER -