This paper presents a new approach to online solution of inverse kinematics problem based on nonlinear optimization for robots with joints physical constraints. The inverse kinematics problem is stated as a constrained nonlinear optimization problem and is solved using Kuhn-Tucker conditions analysis. Nonlinear multivariable optimization problem is locally converted into independent local linear constrained sub-problems and each sub-problem is solved analytically. For each joint, position limits and velocity limits are considered as physical constraints. The proposed structure benefits from a very low complexity design. The proposed method is fast and requires few calculations. The convergence of proposed algorithm is proven based on a Lyapunov function. While keeping the algorithm stable, it can navigate the manipulator to a desired position under joints physical limits. The algorithm is simulated on a 7 DOF PA-10 manipulator. Results indicate good efficiency for the proposed structure in constrained path planning of joint space.

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