Exploration telepresence: A strategy for optimizing scientific research at remote space destinations

Reinforcement learning based variable damping control of wearable robotic limbs for maintaining astronaut pose during extravehicular activity

As astronauts perform on-orbit servicing of extravehicular activity (EVA) without the help of the space station's robotic arms, it will be rather difficult and labor-consuming to maintain the appropriate position in case of impact. In order to solve this problem, we propose the development of a wearable robotic limb system for astronaut assistance and a variable damping control method for maintaining the astronaut's position. The requirements of the astronaut's impact-resisting ability during EVA were analyzed, including the capabilities of deviation resistance, fast return, oscillation resistance, and accurate return. To meet these needs, the system of the astronaut with robotic limbs was modeled and simplified. In combination with this simplified model and a reinforcement learning algorithm, a variable damping controller for the end of the robotic limb was obtained, which can regulate the dynamic performance of the robot end to resist oscillation after impact. A weightless simulation environment for the astronaut with robotic limbs was constructed. The simulation results demonstrate that the proposed method can meet the recommended requirements for maintaining an astronaut's position during EVA. No matter how the damping coefficient was set, the fixed damping control method failed to meet all four requirements at the same time. In comparison to the fixed damping control method, the variable damping controller proposed in this paper fully satisfied all the impact-resisting requirements by itself. It could prevent excessive deviation from the original position and was able to achieve a fast return to the starting point. The maximum deviation displacement was reduced by 39.3% and the recovery time was cut by 17.7%. Besides, it also had the ability to prevent reciprocating oscillation and return to the original position accurately.

On Aerial Robots with Grasping and Perching Capabilities: A Comprehensive Review

Over the last decade, there has been an increased interest in developing aerial robotic platforms that exhibit grasping and perching capabilities not only within the research community but also in companies across different industry sectors. Aerial robots range from standard multicopter vehicles/drones, to autonomous helicopters, and fixed-wing or hybrid devices. Such devices rely on a range of different solutions for achieving grasping and perching. These solutions can be classified as: 1) simple gripper systems, 2) arm-gripper systems, 3) tethered gripping mechanisms, 4) reconfigurable robot frames, 5) adhesion solutions, and 6) embedment solutions. Grasping and perching are two crucial capabilities that allow aerial robots to interact with the environment and execute a plethora of complex tasks, facilitating new applications that range from autonomous package delivery and search and rescue to autonomous inspection of dangerous or remote environments. In this review paper, we present the state-of-the-art in aerial grasping and perching mechanisms and we provide a comprehensive comparison of their characteristics. Furthermore, we analyze these mechanisms by comparing the advantages and disadvantages of the proposed technologies and we summarize the significant achievements in these two research topics. Finally, we conclude the review by suggesting a series of potential future research directions that we believe that are promising.

Modular Robotic Limbs for Astronaut Activities Assistance

In order to meet the assist requirements of extravehicular activity (EVA) for astronauts, such as moving outside the international space station (ISS) or performing on-orbit tasks by a single astronaut, this paper proposes an astronaut robotic limbs system (AstroLimbs) for extravehicular activities assistance. This system has two robotic limbs that can be fixed on the backpack of the astronaut. Each limb is composed of several basic module units with identical structure and function, which makes it modularized and reconfigurable. The robotic limbs can work as extra arms of the astronaut to assist them outside the space station cabin. In this paper, the robotic limbs are designed and developed. The reinforcement learning method is introduced to achieve autonomous motion planning capacity for the robot, which makes the robot intelligent enough to assist the astronaut in unstructured environment. In the meantime, the movement of the robot is also planned to make it move smoothly. The structure scene of the ISS for extravehicular activities is modeled in a simulation environment, which verified the effectiveness of the proposed method.

Is Human Enhancement in Space a Moral Duty? Missions to Mars, Advanced AI and Genome Editing in Space

Any space program involving long-term human missions will have to cope with serious risks to human health and life. Because currently available countermeasures are insufficient in the long term, there is a need for new, more radical solutions. One possibility is a program of human enhancement for future deep space mission astronauts. This paper discusses the challenges for long-term human missions of a space environment, opening the possibility of serious consideration of human enhancement and a fully automated space exploration, based on highly advanced AI. The author argues that for such projects, there are strong reasons to consider human enhancement, including gene editing of germ line and somatic cells, as a moral duty.