Seven-degree-of-freedom redundant manipulators with link offset have many advantages, including obvious geometric significance and suitability for configuration control. Their configuration is similar to that of the experimental module manipulator (EMM) in the Chinese Space Station Remote Manipulator System. However, finding the analytical solution of an EMM on the basis of arm angle parameterization is difficult. This study proposes a high-precision, semi-analytical inverse method for EMMs. Firstly, the analytical inverse kinematic solution is established based on joint angle parameterization. Secondly, the analytical inverse kinematic solution for a non-offset spherical-roll-spherical (SRS) redundant manipulator is derived based on arm angle parameterization. The approximate solution of the EMM is calculated in accordance with the relationship between the joint angles of the EMM and the SRS manipulator. Thirdly, the error is corrected using a numerical method through the analytical inverse solution based on joint angle parameterization. After selecting the stride and termination condition, the precise inverse solution is computed for the EMM based on arm angle parameterization. Lastly, case solutions confirm that this method has high precision, and the arm angle parameterization method is superior to the joint angle parameterization method in terms of parameter selection.

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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.