Dual-arm robot astronaut has more general and dexterous operation ability than single-arm robot, and it can interact with astronaut more friendly. The robot will inevitably use both arms to grasp payloads and transfer them. The force control of the arms in closed chains is an important problem. In this article, the coordinated kinematic and dynamic equations of the dual-arm astronaut are established by considering the closed-chain constraint relationship. Two compliance control methods for dual-arm astronaut coordinated payload manipulating are proposed. The first method is called master–slave force control and the second is the shared force control. For the former, the desired path and operational force of the master arm should be given in advance and that of slave arm are calculated from the dual-arm robot closed-chain constraint equation. In the share control mode, the desired path and end operational force of dual arms are decomposed from the dual-arm robot closed-chain constraint equation directly and equally. Finally, the two control algorithms are verified by simulation. The results of analysis of variance of the simulation data show that the two control methods have no obvious difference in the accuracy of force control but the second control method has a higher position control accuracy, and this proves that the master–slave mode is better for tasks with explicit force distribution requirements and the shared force control is especially suitable for a high-precision requirement.

Compared with non-redundant manipulators, the self-motion of 7-DOF redundant manipulators results in an infinite number of inverse kinematics solutions for a desired end-effector pose. This paper proposes an efficient and accurate analytical solution for inverse kinematics of SSRMS-type redundant manipulators. This solution is applicable to SRS-type manipulators with the same configuration. The proposed method involves introducing an alignment constraint to restrain the self-motion and to decompose the spatial inverse kinematics problem into three independent planar subproblems simultaneously. The resulting geometric equations depend on the part of the joint angles, respectively. These equations are then computed recursively and efficiently using the sequences of $({\theta }_1,{\theta }_7)$, $({\theta }_2,{\theta }_6)$, and $({\theta }_3,{\theta }_4,{\theta }_5)$, generating up to sixteen sets of solutions for a given desired end-effector pose. Additionally, two complementary methods are proposed for overcoming the possible singular configuration and judging unsolvable poses. Finally, numerical simulations are conducted to investigate the performance of the proposed approach in terms of average calculation time, success rate, average position error, and the ability to plan a trajectory with singular configurations.

Exploring the lunar complex and extreme terrain presents formidable challenges for conventional lunar rovers. To address these limitations, this study proposes a novel hexapod jumping hybrid robot that incorporates a “figure-of-eight” (butterfly-shaped) six-branched wheel-legged mechanism and a jumping system that stores elastic energy via deformation of its elastic body. Inspired by the multimodal locomotion of grasshoppers, the robot dynamically switches between two operational modes: high-efficiency wheeled locomotion on relatively flat surfaces and agile jumping to traverse steep slopes and surmount large obstacles. A bio-inspired gait, inspired by the crawling patterns of a hexapod insect, is implemented using a Central Pattern Generator (CPG)-based controller to produce coordinated, rhythmic limb movements. Dynamic simulations of the jumping mechanism were conducted to optimize the critical parameters of the elastic structure and its associated control strategy. Experiments on a physical prototype were conducted to validate the robot’s wheeled mobility and jumping performance. The results demonstrate that the robot exhibits excellent adaptability to rugged terrains and obstacle-dense environments. The integration of multimodal locomotion and adaptive gait control significantly enhances the robot’s operational robustness and survivability in the harsh lunar environment, opening new possibilities for future lunar exploration missions.

This article addresses the challenge of minimizing landing impacts for legged space robots during on-orbit operations. Inspired by the agility of cats, we investigate the role of forelimbs in the landing process. By identifying the kinematic chain of the cat skeleton and tracking it using animal posture estimation, we derive the cushioning strategy that cats use to handle landing impacts. The results indicate that the strategy effectively transforms high-intensity impacts into prolonged low-intensity impacts, thereby safeguarding the brain and internal organs. We adapt this cushioning strategy for robotic platforms through reasonable assumptions and simplifications. Simulations are conducted in both gravitational and zero gravity environments, demonstrating that the optimized strategy not only reduces ground impact and prolongs the cushioning duration but also effectively suppresses the robot’s rebound. In zero gravity, the strategy enhances stable attachment to target surfaces. This research introduces a novel biomimetic control strategy for landing control in the on-orbit operations of space robots.