In the development of safety and mission-critical systems, including autonomous space robotic missions, complex behaviour is captured during the requirements elicitation phase. Requirements are typically expressed using natural language which is ambiguous and not amenable to formal verification methods that can provide robust guarantees of system behaviour. To support the definition of formal requirements, specification patterns provide reusable, logic-based templates. A suite of robotic specification patterns, along with their formalisation in NASA's Formal Requirements Elicitation Tool (FRET) already exists. These pre-existing requirement patterns are domain agnostic and, in this paper we explore their applicability for space missions. To achieve this we carried out a literature review of existing space missions and formalised their requirements using FRET, contributing a corpus of space mission requirements. We categorised these requirements using pre-existing specification patterns which demonstrated their applicability in space missions. However, not all of the requirements that we formalised corresponded to an existing pattern so we have contributed 5 new requirement specification patterns as well as several variants of the existing and new patterns. We also conducted an expert evaluation of the new patterns, highlighting their benefits and limitations.

In recent years, the space community has been exploring the possibilities of Artificial Intelligence (AI), specifically Artificial Neural Networks (ANNs), for a variety of on board applications. However, this development is limited by the restricted energy budget of smallsats and cubesats as well as radiation concerns plaguing modern chips. This necessitates research into neural network accelerators capable of meeting these requirements whilst satisfying the compute and performance needs of the application. This paper explores the use of Phase-Change Memory (PCM) and Resistive Random-Access Memory (RRAM) memristors for on-board in-memory computing AI acceleration in space applications. A guidance and control neural network (G\&CNET) accelerated using memristors is simulated in a variety of scenarios and with both device types to evaluate the performance of memristor-based accelerators, considering device non-idealities such as noise and conductance drift. We show that the memristive accelerator is able to learn the expert actions, though challenges remain with the impact of noise on accuracy. We also show that re-training after degradation is able to restore performance to nominal levels. This study provides a foundation for future research into memristor-based AI accelerators for space, highlighting their potential and the need for further investigation.

These sensors and processing has led to an unprecedented increase in spacecraft methods are already attracting growing interest launches and large-scale constellation projects. As a in the space community [7] with the first SNN on result, the orbits around our planet are becoming congested board in space [8] and studies on EBC behaviour under and the risk of collisions is increasing due to radiation [9]. With this paper, we propose the first the presence of fast-moving space debris [1]. Recognizing fully event-based approach for spacecraft pose estimation, the potential dangers, the Inter-Agency Space Debris but also a novel method to account for the event Coordination Committee (IADC) has established stream. Section 2 introduces the event-based camera, guidelines for the containment of space debris and spiking neural network and pose estimation for space the safe disposal of satellites at the end of their operational application. In Section 3 we present the dataset and life. Agencies and companies have planned the network used, and finally in Section 4 we discuss missions such as On-Orbit Servicing (OOS) or Active the results and future works. Debris Removal (ADR) [2][3][4] to extend the life of satellites and address the problem of space debris.

This work presents the computational design and validation of the Multi-Arm Relocatable Manipulator (MARM), a three-limb robot for space applications, with particular reference to the MIRROR (i.e., the Multi-arm Installation Robot for Readying ORUs and Reflectors) use-case scenario as proposed by the European Space Agency. A holistic computational design and validation pipeline is proposed, with the aim of comparing different limb designs, as well as ensuring that valid limb candidates enable MARM to perform the complex loco-manipulation tasks required. Motivated by the task complexity in terms of kinematic reachability, (self)-collision avoidance, contact wrench limits, and motor torque limits affecting Earth experiments, this work leverages on multiple state-of-art planning and control approaches to aid the robot design and validation. These include sampling-based planning on manifolds, non-linear trajectory optimization, and quadratic programs for inverse dynamics computations with constraints. Finally, we present the attained MARM design and conduct preliminary tests for hardware validation through a set of lab experiments.

Aitech, a maker of rugged computers for military, aerospace and space applications, has tapped Nvidia's Jetson TX2i system-on-module (SoM) for a new radiation-characterized system, it announced recently. The Aitech S-A1760 Venus is a commercial off-the-shelf (COTS) system that can be used for spacecraft and small satellites and takes advantage of around 1 FP32 TFLOPS of "AI performance," as Nvidia puts it. There is a growing need for advanced imaging and data processing in various space applications, but equipping a small satellite with a high-performance, rad-hardened computer is extremely expensive, since tiny satellites are supposed to be light and tiny. This is where Aitech's S-A1760 Venus system comes into play. According to the Aitech, the S-A1760 Venus is targets "short duration spaceflight" as well as near earth orbit (NEO) and low earth orbit (LEO) satellite applications.

Vision-based navigation solutions require training and validation datasets that are as close as possible to real images. Our team and partners develop computer vision algorithms for space exploration (Mars, Jupiter, asteroids, the Moon), and for in-orbit operations (rendezvous, robotic arms, space debris removal). There is a new wave of missions targeting cislunar orbit or the Moon surface. Of course "real images" are rarely available before the mission. Ground-based test facilities such as robotic test benches embarking mock-ups or experiences with scaled mission analogues (mars terrain analogue, drones flights, etc.) are useful, yet they are limited.

Machine learning, particularly deep learning, is being increasing utilised in space applications, mirroring the groundbreaking success in many earthbound problems. Deploying a space device, e.g. a satellite, is becoming more accessible to small actors due to the development of modular satellites and commercial space launches, which fuels further growth of this area. Deep learning's ability to deliver sophisticated computational intelligence makes it an attractive option to facilitate various tasks on space devices and reduce operational costs. In this work, we identify deep learning in space as one of development directions for mobile and embedded machine learning. We collate various applications of machine learning to space data, such as satellite imaging, and describe how on-device deep learning can meaningfully improve the operation of a spacecraft, such as by reducing communication costs or facilitating navigation. We detail and contextualise compute platform of satellites and draw parallels with embedded systems and current research in deep learning for resource-constrained environments.

One of the small drones aboard the ISS taught itself how to go around station with just one eye, and it was a lot harder than you might think. For starters, the SPHERE drone (that's short for Synchronized Position Hold Engage and Reorient Experimental Satellite) learned on its own by using machine learning. That method isn't typically used for space applications, because if it fails, it could result in a costly catastrophe. This is the first time a drone in space employed the technique to teach itself. Plus, the drone was operating in microgravity, floating around in a place where there's no up or down.

We are pleased to introduce the space application issue articles in this issue of AI Magazine. The exploration of space is a testament to human curiosity and the desire to understand the universe that we inhabit. As many space agencies around the world design and deploy missions, it is apparent that there is a need for intelligent, exploring systems that can make decisions on their own in remote, potentially hostile environments. At the same time, the monetary cost of operating missions, combined with the growing complexity of the instruments and vehicles being deployed, make it apparent that substantial improvements can be made by the judicious use of automation in mission operations.

We are pleased to introduce the space application issue articles in this issue of AI Magazine. The exploration of space is a testament to human curiosity and the desire to understand the universe that we inhabit. As many space agencies around the world design and deploy missions, it is apparent that there is a need for intelligent, exploring systems that can make decisions on their own in remote, potentially hostile environments. At the same time, the monetary cost of operating missions, combined with the growing complexity of the instruments and vehicles being deployed, make it apparent that substantial improvements can be made by the judicious use of automation in mission operations.