Self-positioning microdevices enable adaptable spatial displaying

Adaptable display with spatial imaging, fostering advancements in extended reality with unconventional form requirements, is indispensable in scientific research, telemedicine, rescue, and space exploration. The adjustable photon spin angular momentum derived from chiral optical materials offer applicative lights for binocular stereo imaging displays, thus allowing an unimaginable immersive experience while maintaining awareness of surroundings. However, current chiral illuminant struggles to obtain adequate electroluminescence asymmetry during power-on display. Here, we present a designed self-positioning strategy to build new flexible spatial displays, integrating numerous multilayered circularly polarized electroluminescent microdevices, for real-time depth information control on the screen. With the devices' luminescence asymmetry value of up to 1.0 under electro-excitation, we visualize third-dimensional information using our chiral material-integrated tablet. Afterward, combined with a robot, we realize a series of remote human-machine interaction operations based on extended reality conditions. Our adaptable spatial display bridges the gap between virtuality and reality, making pioneering explorations in chiral luminous fields for extended reality and beyond.

Oxide semiconductor in a neuromorphic chromaticity communication loop for extreme environment exploration

Space exploration, particularly in the extreme space environment, has gained increasing attention. Networked robots capable of real-time environmental perception and autonomous collaboration offer a promising alternative for executing complex precision tasks. Consequently, achieving local reliable communication and preparing irradiation-tolerant materials are essential. Here, we demonstrate a cephalopod-inspired neuromorphic loop that enables chromaticity communication between individual near-sensor processing units. A programmatically aligned aluminum zinc oxide nanofiber array was fabricated and used as conductive channels that can withstand prolonged (~104 seconds) and high-dose (~5 × 1015 ions per square centimeter) proton irradiation. The neuromorphic loop, with capabilities in environmental perception, event-driven processing, adaptive learning, and chromaticity communication, enables the self-driven collaboration of robotic hands based on tactile feedback and ensures reliable mobile links for drone flight control. This work pioneers a direction in neuromorphic visible light communication and marks important progress in the field of biomimetic intelligence.

Artificial Intelligence in Human Reproduction

The use of artificial intelligence (AI) in human reproduction is a rapidly evolving field with both exciting possibilities and ethical considerations. This technology has the potential to improve success rates and reduce the emotional and financial burden of infertility. However, it also raises ethical and privacy concerns. This paper presents an overview of the current and potential applications of AI in human reproduction. It explores the use of AI in various aspects of reproductive medicine, including fertility tracking, assisted reproductive technologies, management of pregnancy complications, and laboratory automation. In addition, we discuss the need for robust ethical frameworks and regulations to ensure the responsible and equitable use of AI in reproductive medicine.

Cybernetic avatars: Teleoperation technologies from in-body monitoring to social interaction

Cybernetic avatars integrate physical and virtual avatars to enhance human capabilities in diverse scales and contexts.

Animal-Morphing Bio-Inspired Mechatronic Systems: Research Framework in Robot Design to Enhance Interplanetary Exploration on the Moon

Over the past 50 years, the space race has potentially grown due to the development of sophisticated mechatronic systems. One of the most important is the bio-inspired mobile-planetary robots, actually for which there is no reported one that currently works physically on the Moon. Nonetheless, significant progress has been made to design biomimetic systems based on animal morphology adapted to sand (granular material) to test them in analog planetary environments, such as regolith simulants. Biomimetics and bio-inspired attributes contribute significantly to advancements across various industries by incorporating features from biological organisms, including autonomy, intelligence, adaptability, energy efficiency, self-repair, robustness, lightweight construction, and digging capabilities-all crucial for space systems. This study includes a scoping review, as of July 2024, focused on the design of animal-inspired robotic hardware for planetary exploration, supported by a bibliometric analysis of 482 papers indexed in Scopus. It also involves the classification and comparison of limbed and limbless animal-inspired robotic systems adapted for movement in soil and sand (locomotion methods such as grabbing-pushing, wriggling, undulating, and rolling) where the most published robots are inspired by worms, moles, snakes, lizards, crabs, and spiders. As a result of this research, this work presents a pioneering methodology for designing bio-inspired robots, justifying the application of biological morphologies for subsurface or surface lunar exploration. By highlighting the technical features of actuators, sensors, and mechanisms, this approach demonstrates the potential for advancing space robotics, by designing biomechatronic systems that mimic animal characteristics.

Improved Bald Eagle Search Optimization Algorithm for the Inverse Kinematics of Robotic Manipulators

The inverse kinematics of robotic manipulators involves determining an appropriate joint configuration to achieve a specified end-effector position. This problem is challenging because the inverse kinematics of manipulators are highly nonlinear and complexly coupled. To address this challenge, the bald eagle search optimization algorithm is introduced. This algorithm combines the advantages of evolutionary and swarm techniques, making it more effective at solving nonlinear problems and improving search efficiency. Due to the tendency of the algorithm to fall into local optima, the Lévy flight strategy is introduced to enhance its performance. This strategy adopts a heavy-tailed distribution to generate long-distance jumps, thereby preventing the algorithm from becoming trapped in local optima and enhancing its global search efficiency. The experiments first evaluated the accuracy and robustness of the proposed algorithm based on the inverse kinematics problem of manipulators, achieving a solution accuracy of up to 10-18 m. Subsequently, the proposed algorithm was compared with other algorithms using the CEC2017 test functions. The results showed that the improved algorithm significantly outperformed the original in accuracy, convergence speed, and stability. Specifically, it achieved over 70% improvement in both standard deviation and mean for several test functions, demonstrating the effectiveness of the Lévy flight strategy in enhancing global search capabilities. Furthermore, the practicality of the proposed algorithm was verified through two real engineering optimization problems.

A comprehensive survey of space robotic manipulators for on-orbit servicing

On-Orbit Servicing (OOS) robots are transforming space exploration by enabling vital maintenance and repair of spacecraft directly in space. However, achieving precise and safe manipulation in microgravity necessitates overcoming significant challenges. This survey delves into four crucial areas essential for successful OOS manipulation: object state estimation, motion planning, and feedback control. Techniques from traditional vision to advanced X-ray and neural network methods are explored for object state estimation. Strategies for fuel-optimized trajectories, docking maneuvers, and collision avoidance are examined in motion planning. The survey also explores control methods for various scenarios, including cooperative manipulation and handling uncertainties, in feedback control. Additionally, this survey examines how Machine learning techniques can further propel OOS robots towards more complex and delicate tasks in space.

Exploring beyond Earth using space robotics

Robotic spacecraft enable exploration of our Solar System beyond our human presence. Although spacecraft have explored every planet in the Solar System, the frontiers of space robotics are at the cutting edge of landers, rovers, and now atmospheric explorers, where robotic spacecraft must interact intimately with their environment to explore beyond the reach of flyby and orbital remote sensing. Here, we describe the tremendous growth in space robotics missions in the past 7 years, with many new entities participating in missions to the surface of the Moon, Mars, and beyond. We also describe the recent development of aerial missions to planets and moons, as exemplified by the Ingenuity helicopter on Mars and the Dragonfly mission to Titan. We focus on suborbital robotics-landers, rovers, and aerial vehicles-with associated challenges in sensing, manipulation, mobility, and system-level autonomy.

Study of the Extremely Low-Frequency Noise Characteristics of a Micro-Thrust Measurement Platform

The critical structural parameters are optimized and studied using the numerical simulation method to improve the resolution and stability of the Micro-Thrust Measurement Platform (MTMP). Under two different ground random vibration environments, the parameters, such as pivot thickness, pendulum rod length, and pivot structure, are focused on analyzing the influence of the system's resolution and stability. The results show that when the thickness of the pivot is 0.04 mm or 0.2 mm, and the pendulum rod length is 2 m, the effect of ground random vibration on the MTMP is minimized. At 0.1 mHz, it can reach 0.0057 μN/Hz. In the series double-pivot structure, an appropriate increase in the distance between the sheets can further optimize the above conclusions. The results and analysis within this study can provide support for the engineering design of the MTMP.

Human-like acceleration and deceleration control of a robot astronaut floating in a space station

The acceleration and deceleration (AD) motions are the basic motion modes of robot astronauts moving in a space station. Controlling the locomotion of the robot astronaut is very challenging due to the strong nonlinearity of its complex multi-body dynamics in a gravity-free environment. However, after training, humans can move well in space stations by pushing the bulkhead, and the motion mechanism of humans is a good reference for robot astronauts. The contribution of this study is modeling the human AD motion in a microgravity environment and proposing a human-like control method for robot astronauts moving in space stations. Specifically, the movement and contact force data of the human body during AD motion were collected on an air-floating platform. Through human AD modeling analysis, the mechanism of human motion is discovered, and semi-sinusoidal primitives of contact forces are proposed for AD motion. Then, a dynamic guidance model of human AD motion is built to complete motion planning under contact constraints, which is used as the expected model for the AD control of robot astronauts. Benefiting from the force primitives, accurate and safe planning of human-like AD motion can be completed. The characteristics and mechanism of human AD motion have been analyzed from the perspective of optimization. Lastly, based on the proposed dynamic guidance model, the AD motion policy is mapped to the robot astronaut system via a system control method based on the equivalent mapping of dynamic responses (force, velocity and pose). Through comparative analysis with real human motion data and simulation results under different conditions, the proposed AD control method can achieve human-like motion efficiently and stably. Even when confronted with errors in the robot's contact velocities and inertia parameters, the method can significantly reduce the motion errors while ensuring stability.

A self-organizing robotic aggregate using solid and liquid-like collective states

Designing robotic systems that can change their physical form factor as well as their compliance to adapt to environmental constraints remains a major conceptual and technical challenge. To address this, we introduce the Granulobot, a modular system that blurs the distinction between soft, modular, and swarm robotics. The system consists of gear-like units that each contain a single actuator such that units can self-assemble into larger, granular aggregates using magnetic coupling. These aggregates can reconfigure dynamically and also split into subsystems that might later recombine. Aggregates can self-organize into collective states with solid- and liquid-like properties, thus displaying widely differing compliance. These states can be perturbed locally via actuators or externally via mechanical feedback from the environment to produce adaptive shape-shifting in a decentralized manner. This, in turn, can generate locomotion strategies adapted to different conditions. Aggregates can move over obstacles without using external sensors or coordinates to maintain a steady gait over different surfaces without electronic communication among units. The modular design highlights a physical, morphological form of control that advances the development of resilient robotic systems with the ability to morph and adapt to different functions and conditions.

RoBétArmé Project: Human-robot collaborative construction system for shotcrete digitization and automation through advanced perception, cognition, mobility and additive manufacturing skills

The importance of construction automation has grown worldwide, aiming to deliver new machineries for the automation of roads, tunnels, bridges, buildings and earth-work construction. This need is mainly driven by (i) the shortage and rising costs of skilled workers, (ii) the tremendous increased needs for new infrastructures to serve the daily activities and (iii) the immense demand for maintenance of ageing infrastructure. Shotcrete (sprayed concrete) is increasingly becoming popular technology among contractors and builders, as its application is extremely economical and flexible as the growth in construction repairs in developed countries demand excessive automation of concrete placement. Even if shotcrete technology is heavily mechanized, the actual application is still performed manually at a large extend. RoBétArméEuropean project targets the Construction 4.0 transformation of the construction with shotcrete with the adoption of breakthrough technologies such as sensors, augmented reality systems, high-performance computing, additive manufacturing, advanced materials, autonomous robots and simulation systems, technologies that have already been studied and applied so far in Industry 4.0. The paper at hand showcases the development of a novel robotic system with advanced perception, cognition and digitization capabilities for the automation of all phases of shotcrete application. In particular, the challenges and barriers in shotcrete automation are presented and the RoBétArmésuggested solutions are outlined. We introduce a basic conceptual architecture of the system to be developed and we demonstrate the four application scenarios on which the system is designated to operate.

Mission analysis, dynamics and robust control of an indoor blimp in a CERN detector magnetic environment

At the European Organization for Nuclear Research (CERN), a Research and Development (R&D) program studies robotic systems for inspection and maintenance of the next-generation of particle detectors. The design and operation of these systems are affected by the detector's cavern harsh environment consisting of high magnetic fields and radiations. This work presents a feasibility study for aerial inspection and mapping around a CERN particle detector using a robotic Lighter-than-Air (LtA) Unmanned Aerial Vehicle (UAV), specifically a blimp. Firstly, mission scenarios and the detector environment are introduced; in this context a new empirical model is proposed for the estimation of magnetic disturbances resulting from the interaction of electromagnetic motors with the external magnetic field. Subsequently, the design of a reference blimp and the control system is presented, comparing different control techniques, namely, Computed Torque Control (CTC), Sliding Mode Control (SMC) and Nonsingular Terminal Sliding Mode Control (NTSMC). Finally, the results of trajectory tracking simulations are reported, considering both the uncertainties of the dynamic parameters and the estimated magnetic disturbances. This work demonstrates that the blimp successfully follows desired trajectory, navigating complex environments while maintaining stability and accuracy. Despite the challenges posed by high magnetic fields, indoor blimps can effectively offer safer and more efficient approaches to facility surveillance and maintenance, reducing radiation exposure for human personnel and minimizing detector downtime.

Bioinspired Rigid-Flexible Coupled Adaptive Compliant Motion Control of Robot Gecko for Space Stations

This paper presents a study on bioinspired rigid-flexible coupling adaptive compliant motion control of a robot gecko with hybrid actuation for space stations. The biomimetic robot gecko is made of a rigid trunk, four motor-driven active legs with dual-degree-of-freedom shoulder joints, and four pneumatic flexible pleated active attachment-detachment feet. The adaptive impedance model consists of four input parameters: the inertia coefficient, stiffness coefficient, damping coefficient, and segmented expected plantar force. The robot gecko is equipped with four force sensors mounted on its four feet, from which the normal force of each foot can be sensed in real-time. Based on the sensor signal, the variable stiffness characteristics of the feet in different states are analyzed. Furthermore, an adaptive active compliance control strategy with whole-body rigidity-flexibility-force feedback coupling is proposed for the robot gecko. Four sets of experiments are presented, including open-loop motion control, static anti-interference experiment, segmented variable stiffness experiment, and adaptative compliant motion control, both in a microgravity environment. The experiment results indicated that the presented control strategy worked well and the robot gecko demonstrates the capability of stable attachment and compliant detachment, thereby normal impact and microgravity instability are avoided. It achieves position tracking and force tracking while exhibiting strong robustness for external disturbances.

Autonomous Robots for Services-State of the Art, Challenges, and Research Areas

It has been almost half a century since the first interest in autonomous robots was shown, and research is still continuing to improve their ability to make perfectly conscious decisions from a user safety point of view. These autonomous robots are now at a fairly advanced level, which means that their adoption rate in social environments is also increasing. This article reviews the current state of development of this technology and highlights the evolution of interest in it. We analyze and discuss specific areas of its use, for example, its functionality and current level of development. Finally, challenges related to the current level of research and new methods that are still being developed for the wider adoption of these autonomous robots are highlighted.

A Comprehensive Study of 3-D Vision-Based Robot Manipulation

Robot manipulation, for example, pick-and-place manipulation, is broadly used for intelligent manufacturing with industrial robots, ocean engineering with underwater robots, service robots, or even healthcare with medical robots. Most traditional robot manipulations adopt 2-D vision systems with plane hypotheses and can only generate 3-DOF (degrees of freedom) pose accordingly. To mimic human intelligence and endow the robot with more flexible working capabilities, 3-D vision-based robot manipulation has been studied. However, this task is still challenging in the open world especially for general object recognition and pose estimation with occlusion in cluttered backgrounds and human-like flexible manipulation. In this article, we propose a comprehensive analysis of recent progress about the 3-D vision for robot manipulation, including 3-D data acquisition and representation, robot-vision calibration, 3-D object detection/recognition, 6-DOF pose estimation, grasping estimation, and motion planning. We then present some public datasets, evaluation criteria, comparisons, and challenges. Finally, the related application domains of robot manipulation are given, and some future directions and open problems are studied as well.

Optimal path planning using a continuous anisotropic model for navigation on irregular terrains

Mobile robots usually need to minimize energy when they are traversing uneven terrains. To reach a location of interest, one strategy consists of making the robot follow the path that demands the least possible amount of energy. Yet, its calculation is not trivial with irregular surfaces. Gravity makes the energy consumption of a robot change according to its heading. Such a variation is subject to the terramechanic characteristics of the surface. This paper introduces a cost function that addresses this variation when traversing slopes. This function presents direction-dependency (anisotropic) and returns the cost for all directions (continuous).. Moreover, it is compatible with the Ordered Upwind Method, which allows finding globally optimal paths in a deterministic way. Besides, the segments of these paths are not restricted to the shape of a grid. Finally, this paper also introduces the description and discussion of a simulation experiment. It served to analyse what kinds of terrain motivate the use of anisotropy. The Ordered Upwind Method was executed on a virtual crater with different terrain parameter configurations, using both isotropic (direction-non-dependent) and anisotropic cost functions. The results evince how in certain situations the use of an anisotropic cost function instead of an isotropic one produces a path that reduces the accumulated cost by up to 20%.

Deep Temporal Model-Based Identity-Aware Hand Detection for Space Human-Robot Interaction

Hand detection is a crucial technology for space human-robot interaction (SHRI), and the awareness of hand identities is particularly critical. However, most advanced works have three limitations: 1) the low detection accuracy of small-size objects; 2) insufficient temporal feature modeling between frames in videos; and 3) the inability of real-time detection. In the article, a temporal detector (called TA-RSSD) is proposed based on the SSD and spatiotemporal long short-term memory (ST-LSTM) for real-time detection in SHRI applications. Next, based on the online tubelet analysis, a real-time identity-awareness module is designed for multiple hand object identification. Several notable properties are described as follows: 1) the hybrid structure of the Resnet-101 and the SSD improves the detection accuracy of small objects; 2) three-level feature pyramidal structure retains rich semantic information without losing detailed information; 3) a group of the redesigned temporal attentional LSTM (TA-LSTM) is utilized for three-level feature map modeling, which effectively achieves background suppression and scale suppression; 4) low-level attention maps are used to eliminate in-class similarity between hand objects, which improves the accuracy of identity awareness; and 5) a novel association training scheme enhances the temporal coherence between frames. The proposed model is evaluated on the SHRI-VID dataset (collected according to the task requirements), the AU-AIR dataset, and the ImageNet-VID benchmark. Extensive ablation studies and comparisons on detection and identity-awareness capacities show the superiority of the proposed model. Finally, a set of actual testing is conducted on a space robot, and the results show that the proposed model achieves a real-time speed and high accuracy.

In situ resource utilization of lunar soil for highly efficient extraterrestrial fuel and oxygen supply

Building up a lunar settlement is the ultimate aim of lunar exploitation. Yet, limited fuel and oxygen supplies restrict human survival on the Moon. Herein, we demonstrate the in situ resource utilization of lunar soil for extraterrestrial fuel and oxygen production, which may power up our solely natural satellite and supply respiratory gas. Specifically, the lunar soil is loaded with Cu species and employed for electrocatalytic CO₂ conversion, demonstrating significant production of methane. In addition, the selected component in lunar soil (i.e. MgSiO₃) loaded with Cu can reach a CH₄ Faradaic efficiency of 72.05% with a CH₄ production rate of 0.8 mL/min at 600 mA/cm². Simultaneously, an O₂ production rate of 2.3 mL/min can be achieved. Furthermore, we demonstrate that our developed process starting from catalyst preparation to electrocatalytic CO₂ conversion is so accessible that it can be operated in an unmmaned manner via a robotic system. Such a highly efficient extraterrestrial fuel and oxygen production system is expected to push forward the development of mankind's civilization toward an extraterrestrial settlement.

Review on the COVID-19 pandemic prevention and control system based on AI

As a new technology, artificial intelligence (AI) has recently received increasing attention from researchers and has been successfully applied to many domains. Currently, the outbreak of the COVID-19 pandemic has not only put people's lives in jeopardy but has also interrupted social activities and stifled economic growth. Artificial intelligence, as the most cutting-edge science field, is critical in the fight against the pandemic. To respond scientifically to major emergencies like COVID-19, this article reviews the use of artificial intelligence in the combat against the pandemic from COVID-19 large data, intelligent devices and systems, and intelligent robots. This article's primary contributions are in two aspects: (1) we summarized the applications of AI in the pandemic, including virus spreading prediction, patient diagnosis, vaccine development, excluding potential virus carriers, telemedicine service, economic recovery, material distribution, disinfection, and health care. (2) We concluded the faced challenges during the AI-based pandemic prevention process, including multidimensional data, sub-intelligent algorithms, and unsystematic, and discussed corresponding solutions, such as 5G, cloud computing, and unsupervised learning algorithms. This article systematically surveyed the applications and challenges of AI technology during the pandemic, which is of great significance to promote the development of AI technology and can serve as a new reference for future emergencies.

Anhydrobiotic chironomid larval motion-based multi-sensing microdevice for the exploration of survivable locations

African chironomid (Polypedilum vanderplanki) larvae can suspend their metabolism by undergoing severe desiccation and then resume this activity by simple rehydration. We present a microdevice using interdigital comb electrodes to detect the larval motion using the natural surface charge of the living larvae in water. The larvae were most active 2 h after soaking them in water at 30°C; they exhibited motions with 2 Hz frequency. This was comparable to the signal obtained from the microdevice via fast Fourier transform (FFT) processing. The amplitude of the voltage and current were 0.11 mV and 730 nA, respectively. They would be enough to be detected by a low power consumption microcomputer. Temperature and pH sensing were demonstrated by detecting the vital motions of the revived larvae under different conditions. This multi-functional biosensor will be a useful microdevice to search for survivable locations under extreme environmental conditions like those on other planets.

Science Autonomy for Ocean Worlds Astrobiology: A Perspective

Astrobiology missions to ocean worlds in our solar system must overcome both scientific and technological challenges due to extreme temperature and radiation conditions, long communication times, and limited bandwidth. While such tools could not replace ground-based analysis by science and engineering teams, machine learning algorithms could enhance the science return of these missions through development of autonomous science capabilities. Examples of science autonomy include onboard data analysis and subsequent instrument optimization, data prioritization (for transmission), and real-time decision-making based on data analysis. Similar advances could be made to develop streamlined data processing software for rapid ground-based analyses. Here we discuss several ways machine learning and autonomy could be used for astrobiology missions, including landing site selection, prioritization and targeting of samples, classification of "features" (e.g., proposed biosignatures) and novelties (uncharacterized, "new" features, which may be of most interest to agnostic astrobiological investigations), and data transmission.

Squeezing Data from a Rock: Machine Learning for Martian Science

Data analysis methods have scarcely kept pace with the rapid increase in Earth observations, spurring the development of novel algorithms, storage methods, and computational techniques. For scientists interested in Mars, the problem is always the same: there is simultaneously never enough of the right data and an overwhelming amount of data in total. Finding sufficient data needles in a haystack to test a hypothesis requires hours of manual data screening, and more needles and hay are added constantly. To date, the vast majority of Martian research has been focused on either one-off local/regional studies or on hugely time-consuming manual global studies. Machine learning in its numerous forms can be helpful for future such work. Machine learning has the potential to help map and classify a large variety of both features and properties on the surface of Mars and to aid in the planning and execution of future missions. Here, we outline the current extent of machine learning as applied to Mars, summarize why machine learning should be an important tool for planetary geomorphology in particular, and suggest numerous research avenues and funding priorities for future efforts. We conclude that: (1) moving toward methods that require less human input (i.e., self- or semi-supervised) is an important paradigm shift for Martian applications, (2) new robust methods using generative adversarial networks to generate synthetic high-resolution digital terrain models represent an exciting new avenue for Martian geomorphologists, (3) more effort and money must be directed toward developing standardized datasets and benchmark tests, and (4) the community needs a large-scale, generalized, and programmatically accessible geographic information system (GIS).

Using Artificial Intelligence for Space Challenges: A Survey

Artificial intelligence is applied to many fields and contributes to many important applications and research areas, such as intelligent data processing, natural language processing, autonomous vehicles, and robots. The adoption of artificial intelligence in several fields has been the subject of many research papers. Still, recently, the space sector is a field where artificial intelligence is receiving significant attention. This paper aims to survey the most relevant problems in the field of space applications solved by artificial intelligence techniques. We focus on applications related to mission design, space exploration, and Earth observation, and we provide a taxonomy of the current challenges. Moreover, we present and discuss current solutions proposed for each challenge to allow researchers to identify and compare the state of the art in this context.

Exploring planet geology through force-feedback telemanipulation from orbit

Current space exploration roadmaps envision exploring the surface geology of celestial bodies with robots for both scientific research and in situ resource utilization. In such unstructured, poorly lit, complex, and remote environments, automation is not always possible, and some tasks, such as geological sampling, require direct teleoperation aided by force-feedback (FF). The operator would be on an orbiting spacecraft, and poor bandwidth, high latency, and packet loss from orbit to ground mean that safe, stable, and transparent interaction is a substantial technical challenge. For this scenario, a control method was developed that ensures stability at high delay without reduction in speed or loss of positioning accuracy. At the same time, a new level of safety is achieved not only through FF itself but also through an intrinsic property of the approach preventing hard impacts. On the basis of this method, a tele-exploration scenario was simulated in the Analog-1 experiment with an astronaut on the International Space Station (ISS) using a 6-degree-of-freedom (DoF) FF capable haptic input device to control a mobile robot with manipulator on Earth to collect rock samples. The 6-DoF FF telemanipulation from space was performed at a round-trip communication delay constantly between 770 and 850 milliseconds and an average packet loss of 1.27%. This experiment showcases the feasibility of a complete space exploration scenario via haptic telemanipulation under spaceflight conditions. The results underline the benefits of this control method for safe and accurate interactions and of haptic feedback in general.

Robot-assisted surgery in space: pros and cons. A review from the surgeon's point of view

The target of human flight in space has changed from permanence on the International Space Station to missions beyond low earth orbit and the Lunar Gateway for deep space exploration and Missions to Mars. Several conditions affecting space missions had to be considered: for example the effect of weightlessness and radiations on the human body, behavioral health decrements or communication latency, and consumable resupply. Telemedicine and telerobotic applications, robot-assisted surgery with some hints on experimental surgical procedures carried out in previous missions, had to be considered as well. The need for greater crew autonomy in health issues is related to the increasing severity of medical and surgical interventions that could occur in these missions, and the presence of a highly trained surgeon on board would be recommended. A surgical robot could be a valuable aid but only inasfar as it is provided with multiple functions, including the capability to perform certain procedures autonomously. Space missions in deep space or on other planets present new challenges for crew health. Providing a multi-function surgical robot is the new frontier. Research in this field shall be paving the way for the development of new structured plans for human health in space, as well as providing new suggestions for clinical applications on Earth.

Internet of Things in Space: A Review of Opportunities and Challenges from Satellite-Aided Computing to Digitally-Enhanced Space Living

Recent scientific and technological advancements driven by the Internet of Things (IoT), Machine Learning (ML) and Artificial Intelligence (AI), distributed computing and data communication technologies have opened up a vast range of opportunities in many scientific fields—spanning from fast, reliable and efficient data communication to large-scale cloud/edge computing and intelligent big data analytics. Technological innovations and developments in these areas have also enabled many opportunities in the space industry. The successful Mars landing of NASA’s Perseverance rover on 18 February 2021 represents another giant leap for humankind in space exploration. Emerging research and developments of connectivity and computing technologies in IoT for space/non-terrestrial environments is expected to yield significant benefits in the near future. This survey paper presents a broad overview of the area and provides a look-ahead of the opportunities made possible by IoT and space-based technologies. We first survey the current developments of IoT and space industry, and identify key challenges and opportunities in these areas. We then review the state-of-the-art and discuss future opportunities for IoT developments, deployment and integration to support future endeavors in space exploration.

Rock Segmentation in the Navigation Vision of the Planetary Rovers

Visual navigation is an essential part of planetary rover autonomy. Rock segmentation emerged as an important interdisciplinary topic among image processing, robotics, and mathematical modeling. Rock segmentation is a challenging topic for rover autonomy because of the high computational consumption, real-time requirement, and annotation difficulty. This research proposes a rock segmentation framework and a rock segmentation network (NI-U-Net++) to aid with the visual navigation of rovers. The framework consists of two stages: the pre-training process and the transfer-training process. The pre-training process applies the synthetic algorithm to generate the synthetic images; then, it uses the generated images to pre-train NI-U-Net++. The synthetic algorithm increases the size of the image dataset and provides pixel-level masks—both of which are challenges with machine learning tasks. The pre-training process accomplishes the state-of-the-art compared with the related studies, which achieved an accuracy, intersection over union (IoU), Dice score, and root mean squared error (RMSE) of 99.41%, 0.8991, 0.9459, and 0.0775, respectively. The transfer-training process fine-tunes the pre-trained NI-U-Net++ using the real-life images, which achieved an accuracy, IoU, Dice score, and RMSE of 99.58%, 0.7476, 0.8556, and 0.0557, respectively. Finally, the transfer-trained NI-U-Net++ is integrated into a planetary rover navigation vision and achieves a real-time performance of 32.57 frames per second (or the inference time is 0.0307 s per frame). The framework only manually annotates about 8% (183 images) of the 2250 images in the navigation vision, which is a labor-saving solution for rock segmentation tasks. The proposed rock segmentation framework and NI-U-Net++ improve the performance of the state-of-the-art models. The synthetic algorithm improves the process of creating valid data for the challenge of rock segmentation. All source codes, datasets, and trained models of this research are openly available in Cranfield Online Research Data (CORD).

Soft robotics: the route to true robotic organisms

Soft Robotics has come to the fore in the last decade as a new way of conceptualising, designing and fabricating robots. Soft materials empower robots with locomotion, manipulation, and adaptability capabilities beyond those possible with conventional rigid robots. Soft robots can also be made from biological, biocompatible and biodegradable materials. This offers the tantalising possibility of bridging the gap between robots and organisms. Here, we discuss the properties of soft materials and soft systems that make them so attractive for future robots. In doing so, we consider how future robots can behave like, and have abilities akin to, biological organisms. These include huge numbers, finite lifetime, homeostasis and minimal—and even positive—environmental impact. This paves the way for future robots, not as machines, but as robotic organisms.

Progress in robotics for combating infectious diseases

The world was unprepared for the COVID-19 pandemic, and recovery is likely to be a long process. Robots have long been heralded to take on dangerous, dull, and dirty jobs, often in environments that are unsuitable for humans. Could robots be used to fight future pandemics? We review the fundamental requirements for robotics for infectious disease management and outline how robotic technologies can be used in different scenarios, including disease prevention and monitoring, clinical care, laboratory automation, logistics, and maintenance of socioeconomic activities. We also address some of the open challenges for developing advanced robots that are application oriented, reliable, safe, and rapidly deployable when needed. Last, we look at the ethical use of robots and call for globally sustained efforts in order for robots to be ready for future outbreaks.

Research Trends and Future Perspectives in Marine Biomimicking Robotics

Mechatronic and soft robotics are taking inspiration from the animal kingdom to create new high-performance robots. Here, we focused on marine biomimetic research and used innovative bibliographic statistics tools, to highlight established and emerging knowledge domains. A total of 6980 scientific publications retrieved from the Scopus database (1950-2020), evidencing a sharp research increase in 2003-2004. Clustering analysis of countries collaborations showed two major Asian-North America and European clusters. Three significant areas appeared: (i) energy provision, whose advancement mainly relies on microbial fuel cells, (ii) biomaterials for not yet fully operational soft-robotic solutions; and finally (iii), design and control, chiefly oriented to locomotor designs. In this scenario, marine biomimicking robotics still lacks solutions for the long-lasting energy provision, which presently hinders operation autonomy. In the research environment, identifying natural processes by which living organisms obtain energy is thus urgent to sustain energy-demanding tasks while, at the same time, the natural designs must increasingly inform to optimize energy consumption.

Recent developments in terrain identification, classification, parameter estimation for the navigation of autonomous robots

The work presents a review on ongoing researches in terrain-related challenges influencing the navigation of Autonomous Robots, specifically Unmanned Ground ones. The paper aims to highlight the recent developments in robot design and advanced computing techniques in terrain identification, classification, parameter estimation, and developing modern control strategies. The objective of our research is to familiarize the gaps and opportunities of the aforementioned areas to the researchers who are passionate to take up research in the field of autonomous robots. The paper brings recent works related to terrain strategies under a single platform focusing on the advancements in planetary rovers, rescue robots, military robots, agricultural robots, etc. Finally, this paper provides a comprehensive analysis of the related works which can bridge the AI techniques and advanced control strategies to improve navigation. The study focuses on various Deep Learning techniques and Fuzzy Logic Systems in detail. The work can be extended to develop new control schemes to improve multiple terrain navigation performance.

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.

Why Human Enhancement is Necessary for Successful Human Deep-space Missions

While humans have made enormous progress in the exploration and exploitation of Earth, exploration of outer space remains beyond current human capabilities. The principal challenges lie in current space technology and engineering which includes the protection of astronauts from the hazards of working and living in the space environment. These challenges may lead to a paradoxical situation where progress in space technology and the ability to ensure acceptable risk/benefit for human space exploration becomes dissociated and the rate of scientific discovery declines. In this paper, we discuss the predominant challenges of the space environment for human health and argue that development and deployment of a human enhancement policy, initially confined to astronauts - for the purpose of future human space programmes is a rational solution to these challenges.

Chemical Ionization Mass Spectrometry: Applications for the In Situ Measurement of Nonvolatile Organics at Ocean Worlds

A new technique that has applications for the detection of nonvolatile organics on Ocean Worlds has been developed. Here, liquid mixtures of fatty acids (FAs) and/or amino acids (AAs) are introduced directly into a miniature quadrupole ion trap mass spectrometer (QITMS) developed at Jet Propulsion Laboratory and analyzed. Two ionization methods, electron impact and chemical ionization (EI and CI, respectively), are compared and contrasted. Further, multiple CI reagents are tested to explore their potential to "soften" ionization of FAs and AAs. Both EI and CI yield mass spectra that bear signatures of FAs or AAs; however, soft CI yields significantly cleaner mass spectra that are easier to interpret. The combination of soft CI with tandem mass spectrometry (MS/MS) has also been demonstrated for AAs, generating "fingerprint" mass spectra of fragments from protonated parent ions. To mimic potential Ocean World conditions, water is used as the primary collision gas in MS/MS experiments. This technique has the potential for the in situ analysis of molecules in the cryogenic plumes of Ocean Worlds (e.g., Enceladus) and comets with the ultimate goal of detecting potential biosignatures.