Research on modular robots for space exploration has primarily focused on reconfiguration, with limited attention given to the maneuverability in space environment, which is essential for harnessing the advantages of reconfiguration. In this paper, a modular snake-like robot (MSR) is designed, which is expected to emulate a snake to navigate complex environments and employ the reconfiguration capability for on-site shape-shifting. To this end, a snake-like motion analysis and planning method is proposed for MSR. Firstly, we explore the feasibility of utilizing modules in realizing snake-like motion, including functional compatibility and structural constraints. Secondly, we analyze the kinematics of MSR and design joint coordination motion schemes to meet the requirements of snake-like motion. Finally, a path planning method based on reinforcement learning is proposed, which fully considers the motion characteristics and the structural constraints. Through motion analysis and planning, a balance between environmental adaptability and versatility can be achieved. Simulations of comparisons and potential applications further demonstrate the significant advantages of MSR in space exploration.

Supernumerary Robotic Limbs (SRLs) are designed to collaborate with the wearer, enhancing operational capabilities. When human limbs are occupied with primary tasks, controlling SRLs flexibly and naturally becomes a challenge. Existing methods such as electromyography (EMG) control and redundant limb control partially address SRL control issues. However, they still face limitations like restricted degrees of freedom and complex data requirements, which hinder their applicability in real-world scenarios. Additionally, fully autonomous control methods, while efficient, often lack the flexibility needed for complex tasks, as they do not allow for real-time user adjustments. In contrast, shared control combines machine autonomy with human input, enabling finer control and more intuitive task completion. Building on our previous work with the mouth-and-tongue interface, this paper integrates a mixed reality (MR) device to form an interactive system that enables shared control of the SRL. The system allows users to dynamically switch between voluntary and autonomous control, providing both flexibility and efficiency. A random forest model classifies 14 distinct tongue and mouth operations, mapping them to six-degree-of-freedom SRL control. In comparative experiments involving ten healthy subjects performing assembly tasks under three control modes (shared control, autonomous control, and voluntary control), shared control demonstrates a balance between machine autonomy and human input. While autonomous control offers higher task efficiency, shared control achieves greater task success rates and improves user experience by combining the advantages of both autonomous operation and voluntary control. This study validates the feasibility of shared control and highlights its advantages in providing flexible switching between autonomy and user intervention, offering new insights into SRL control.

Robots are becoming an integral part of space facilities construction and maintenance, and may need to move to and from different work locations. Robotic arms that are widely employed, which are mounted on fixed bases, have difficulty coping with increasingly complex missions. The challenge discussed in this paper is the problem of the efficient locomotion of robotic systems. Inspired by the gliding motion of a flying snake launched from a tree and combined with the weightlessness of the space environment, we design similar motions for the robot, including the following three steps. First, the robot folds its body like a snake and initiates flight by accelerating the global center of mass (CM), focusing on the movement direction and generating suitable momentum. Then, during the flight (free-floating) phase, the joint motions are planned using a nonlinear optimization technique, considering the nonholonomic constraints introduced by the momentum conservation and the system states at the initial and final states of the floating. Meanwhile, the difficulties caused by long-distance flights are addressed to reduce the motion computational cost and energy consumption by introducing the phase plane analysis method. Finally, the landing motion is designed to avoid rigid collisions and rollover on the radial plane. The numerical simulations illustrate that the three phases of maneuvers are smooth and continuous, which can help the space robots efficiently traverse the environment.

Architectured soft structures have unlocked new possibilities for designing continuum manipulators with tailored mechanical performance. This work introduces a bioinspired soft manipulator based on modular wave spring units, leveraging the high elasticity of wave spring structures to enable compliant deformation and axial extensibility of the soft manipulator. To achieve precise control of the soft manipulator, a neural network-based inverse kinematics framework is developed to establish an efficient mapping from desired end poses to tendon actuation lengths. An iterative learning control strategy is further integrated to compensate for material hysteresis, friction, and external disturbances. A prototype is fabricated using flexible 3D-printable material (TPU), and comprehensive experiments are conducted to validate extensible performance, point positioning accuracy, trajectory-tracking accuracy, and compliant interaction capability. The results demonstrate that the proposed wave spring-based soft manipulator achieves a high extension ratio and reliable control precision.