In redundancy optimization problems related to cooperating manipulators such as optimal force distribution, constraints on the physical limits of the manipulators should be considered. We propose quadratic inequality constraints (QICs), which lead to ellipsoidal feasible regions, to solve the optimization problem more efficiently. We investigate the effect of the use of QICs from the points of view of problem size and change of the feasible region. To efficiently deal with the QICs, we also propose the dual quadratically constrained quadratic programming (QCQP) method. In this method, the size of the optimization problem is reduced so that the computational burden is lightened. The proposed method and another well-known quadratic programming method are applied to the two PUMA robots system and compared with each other. The results show that the use of QICs with the dual QCQP method allows for faster computation than the existing method.
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