Soft robots are characterized by ultraflexible electromechanical structures that are capable of large deformations and movements. However, the challenge of generating the high output forces necessary for manipulation persists because of the low Young’s moduli of soft materials, which limit their effectiveness in tasks such as carrying, grasping, and providing support. Inspired by shrimp exoskeletons, we designed an origami exoskeleton that features flexible-rigid arrangements and mechanical multistability. This design offers high stiffness in stable states while allowing for long-stroke transitions, making it suitable for integration with soft actuators to balance deformability and load-bearing capacity. With advantages such as high compressibility and rapid integration, these exoskeleton-enhanced arms have promising applications when mounted on unmanned ground and aerial vehicles, facilitating large-scale dexterous manipulation with the necessary load capacities. For existing soft arms, the proposed origami exoskeleton enhances the potential for manipulation within their original design framework.

This paper proposes a full-size and free-flying humanoid robot named Taikobot that aims to assist astronauts in a space station and maintain spacecrafts between human visits. Taikobot adopts a compact and lightweight (∼25 kg) design to work in microgravity, which also reduces launch costs and improves safety during human–robot collaboration. Taikobot’s anthropomorphic dual arm system and zero-g legs allow it to share a set of intravehicular human–machine interfaces. Unlike ground-walking robots, Taikobot maneuvers in a novel push–flight–park (PFP) strategy as an equivalent astronaut in a space station to maximize workspace and flexibility. We propose a PFP motion planning and control method based on centroidal dynamics and multi-contact model. Based on the proposed method, we carried out extensive simulations and verified the feasibility and advantages of the novel locomotion strategy. We also developed a prototype of Taikobot and carried out several ground experiments on typical scenarios where the robot collaborates with human astronauts. The experiments show that Taikobot can do some simple and repetitive tasks along with astronauts and has the potential to help astronauts improve their onboard working efficiency.

This paper proposes an integrated panoramic sun sensor (IPSS) for the small spherical satellite Q-SAT that has been working in orbit since 2020. IPSS is essentially a set of temperature-compensated photoelectric cells distributed on the spherical surface of Q-SAT. Compared with traditional sun sensors, IPSS has full spherical coverage of 4$\pi$ so that the sun vector from any direction can be inversed. The mechatronic design and mathematical model of the proposed IPSS are presented. In-depth error analyses in terms of albedo effect, sampling error, parameter deviation, etc. are carried out. IPSS can provide a sun vector inversion accuracy of 1.5${}^{\circ }$ where albedo disturbance does not dominate. Simulation results show that the measurement of IPSS together with a COTS magnetometer can support the three-axis attitude determination of satellites in various orbits and can adapt to the seasonal variations of subpolar points. Ground experimental results and on-orbit data have also verified the feasibility and performance of IPSS. Although the panoramic sun sensor is designed for the small spherical Q-SAT, it can also be applied to other satellites with limited power budgets.

The application of intravehicular robotic assistants (IRA) can save valuable working hours for astronauts in space stations. There are various types of IRA, such as an accompanying drone working in microgravity and a dexterous humanoid robot for collaborative operations. In either case, the ability to navigate and work along with human astronauts lays the foundation for their deployment. To address this problem, this paper proposes the framework of simultaneous astronaut accompanying and visual navigation. The framework contains a customized astronaut detector, an intravehicular navigation system, and a probabilistic model for astronaut visual tracking and motion prediction. The customized detector is designed to be lightweight and has achieved superior performance (AP@0.5 of 99.36%) for astronaut detection in diverse postures and orientations during intravehicular activities. A map-based visual navigation method is proposed for accurate and 6DoF localization (1~2 cm, 0.5°) in semi-structured environments. To ensure the robustness of navigation in dynamic scenes, feature points within the detected bounding boxes are filtered out. The probabilistic model is formulated based on the map-based navigation system and the customized astronaut detector. Both trajectory correlation and geometric similarity clues are incorporated into the model for stable visual tracking and trajectory estimation of the astronaut. The overall framework enables the robotic assistant to track and distinguish the served astronaut efficiently during intravehicular activities and to provide foresighted service while in locomotion. The overall performance and superiority of the proposed framework are verified through extensive ground experiments in a space-station mockup.