In this paper, a novel mechatronic design philosophy is introduced to develop a compact modular rotary elastic joint for a humanoid manipulator. The designed elastic joint is mainly composed of a brushless direct current (DC) motor, harmonic reducer, customized torsional spring, and fail-safe brake. The customized spring considerably reduces the volume of the elastic joint and facilitates the construction of a humanoid manipulator which employs this joint. The large central hole along the joint axis brings convenience for cabling and the fail-safe brake can guarantee safety when the power is off. In order to reduce the computational burden on the central controller and simplify system maintenance, an expandable electrical system, which has a double-layer control structure, is introduced. Furthermore, a robust position controller for the elastic joint is proposed and interpreted in detail. Vibration of the elastic joint is suppressed by means of resonance ratio control (RRC). In this method, the ratio between the resonant and anti-resonant frequency can be arbitrarily designated according to the feedback of the nominal spring torsion. Instead of using an expensive torque sensor, the spring torque can be obtained by calculating the product of spring stiffness and deformation, due to the high linearity of the customized spring. In addition, to improve the system robustness, a motor-side disturbance observer (DOb) and an arm-side DOb are employed to estimate and compensate for external disturbances and system uncertainties, such as model variation, friction, and unknown external load. Validity of the DOb-based RRC is demonstrated in the simulation results. Experimental results show the performance of the modular elastic joint and the viability of the proposed controller further.
Hyper-redundant manipulators have been widely used in the complex and cluttered environment for achieving various kinds of tasks. In this article, we present two contributions. First, we provide a novel algorithm of relating forward and backward reaching inverse kinematic algorithm to velocity obstacles. Our optimization-based algorithm simultaneously handles the task space constraints, the joint limit constraints, and the collision-free constraints for hyper-redundant manipulators based on the generalized framework. Second, we present an extension of our inverse kinematic algorithm to collision avoidance for the hyper-redundant manipulators, where the workspaces may have different types of obstacles. We highlight the performance of our algorithm on hyper-redundant manipulators with various degrees of freedom. The results show that our algorithm has made full use of dexterity of hyper-redundant manipulators in complex environments, enhancing the performance and increasing the flexibility.
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