Model predictive control method for position control and obstacle avoidance of hyper-redundant binary manipulator

In this study, a binary manipulator is considered as a controlled plant. The control objective is to bring the end effector of the binary manipulator close to a given target point and orientation. One advantage of the binary manipulator is its high reliability owing to its hyper-redundancy. However, the inverse kinematics problem of the binary manipulator is a combinatorial optimization problem. The model predictive control (MPC) method is applied to calculate the control input required to bring the end effector close to the target. The monotonicity of the objective function is utilized to obtain the expansion/contraction pattern of the binary manipulator. This method is also suitable for resolving the obstacle avoidance problem, which has not been resolved in conventional studies. Numerical simulations were conducted to verify the usefulness of the proposed method.