Redundancy optimization for cooperating manipulators using quadratic inequality constraints

In redundancy optimization problems related to cooperating manipulators, constraints should be considered for physical limits of the manipulators. The constraints have been imposed mostly in the form of linear inequality constraints, which lead to polyhedric feasible regions. We propose quadratic inequality constraints (QICs) which lead to ellipsoidal feasible regions to solve the optimization problem faster and to directly handle constraints on quadratic quantities. We investigate the effect of the use of QICs from the points of view of problem size and change of the feasible region. In order to efficiently deal with QICs, we utilize the dual quadratically constrained quadratic programming (QCQP) method. The proposed scheme and another well-known quadratic programming method are applied to numerical examples and compared with each other. The results show that the use of QICs makes it possible to make trade-off between optimality and fast computation capability and implements faster computation than the existing method.