The current desire to go and explore space is as strong as ever. Past space powers have been gradually joined by a flurry of new nations eager to test and demonstrate their technologies and contribute to an increasing body of knowledge. Space robotics and autonomous systems (RAS) are important to human's overall ability to explore or operate in space, by providing greater access beyond human spaceflight limitations in the harsh environment of space and operational handling that extends astronauts' capabilities. RAS can help reduce the cognitive load on humans given the abundance of information that has to be reasoned upon in a timely fashion and hence are critical for improving human and systems' safety. RAS can also enable the deployment and operation of multiple assets without the same order of magnitude increase in ground support. Given the potential reduction to the cost and risk of spaceflight both manned and robotic, space RAS are deemed relevant across all mission phases such as development, flight system production, launch, and operation. This chapter introduces the book by providing the basis of space RAS, such as key technological challenges, relevant applications over the horizon as well as the recent advances to be presented in the remainder of the book.

Rover locomotion on extra-terrestrial surfaces is of general interest to the space community. Understanding and characterizing the surface processes that contribute to locomotion can increase efficiency, safety, and mission duration. This chapter presents an explanation of recent methods developed for modelling rover locomotion in granular media at a fundamental level. We begin by examining a brief progression of granular locomotion modelling, important regolith characteristics, and how these inform the choice to use a scaling approach in the remainder of the chapter. We then address recent experiments that reveal limitations of such theories, and how one can develop simple criteria and test methods that may allow better design of roving vehicles. Finally, we close by examining current and future directions that can possibly lead to better modelling.

In space environments, astronauts have duties that need to be addressed and some of them can be overwhelming, especially considering, in most cases, the crew members in the space station are normally a few. This can be resolved by accompanying the astronauts with assistant robots to reduce the workload. Thus, deployment of soft robots, inspired by the morphological adaptation ability existing in octopus tentacles, elephant trunks, and snakes, is promising in a very long horizon for space exploration. In this chapter, we will discuss one of the soft robotic technologies we developed based on pneumatic muscle actuators' (PMAs) principle and show how they can be effective to improve the existing robotic systems that can be implemented in space environments. These developed PMAs are promising and can serve as alternatives to the traditional rigid robotic systems, by performing dexterous activities that are tedious, repetitive, and difficult to perform.

Natural organisms are constantly having to adapt in order to overcome the challenges posed by their environment, with the most beneficial traits being continuously improved and refined over millions of years of evolution. This long refinement process takes place at all scale levels, from the nano to the macroscopic, resulting in materials and processes far superior to human solutions to similar problems. As such, studying these natural techniques and adapting them for man-made applications can lead to innovations and new improvements in current and future technologies.

Space robotic missions with increased levels of autonomy are being pursued in wide-array of orbital applications including on-orbit servicing (OOS), on-orbit assembly (OOA), and active debris removal (ADR). In these missions, the spacecraft is expected to perform most guidance and navigation tasks such as far-, mid- and close-range rendezvous, relative navigation, and proximity operations with minimal human-in-loop. This goal brings the focus towards vision-based spacecraft navigation utilising the state-of-the-art technologies in the field of computer vision especially the deep-learning algorithms for the pose estimation. This chapter explores major deep-learning approaches suitable for spacecraft pose estimation along with the discussion on different software simulation tools that are currently used for rendering realistic images of the target in orbit to train and validate the deep-learning models, and finally the ground-based testbed is used for validating the close-proximity operations.

With the exploration of space, the number of orbiting spacecrafts has been accumulating. A considerable part of them are non-cooperative targets such as the rocket end-stages or disposed satellites, which seriously threaten active spacecrafts. So the reasonable disposal of space non-cooperative targets is very urgent. On-orbit capture is the premise of most on-orbit operations. As non-cooperative objects are unable to supply any prior information and its inertial parameters are not available, the capture process owns the feature of complex time varying, strong coupling and nonlinearity, and thereby the space operation via space robotic system requires better adaptive and real-time property. This chapter focuses on the inertial parameter identification of a non-cooperative object, the adaptive reactionless path planning strategy for the robot arm during the identification and stability control strategy for the whole system. The relative content is organized as follows: The first section establishes the basic dynamic model of the space robotic system that operates in the post-capture stage. According to the system's kinematics and dynamics equations, the two basic equations of identification for the non-cooperative object are constructed: the equation of Momentum Conservation as well as the equation of Newton-Euler, to obtain the basis of two-step identification and the error mechanism analysis. The second section proposes the theory of error mechanism analysis and designing an improved inertial parameter identification method via the contact force measure. To deal with the strong coupling existing in the conventional identification equation, the two-step scheme is proposed, and the sufficient condition for identification as well as the error mechanism analysis is deduced for the improved identification method, which employs the contact force information to deal with the error accumulation and thus can improve the accuracy. The third section designs an adaptive reactionless path planning method for the manipulator to deal with the motion disturbance and proposes a robust adaptive control strategy for joints' controllers and feedforward control strategy for the spacecraft's controller to ensure the stability of the whole system. The Slide-windowed Recursive Least Square (RLS) algorithm is employed to identify and compensate the momentum coefficient matrix that updates online, and thus the Adaptive Reaction Null Space (ARNS) algorithm is constructed and the dynamic path planning is completed for the manipulator. The robust adaptive control strategy for the manipulator is proposed to track the planned path, and the feedforward control strategy via coupling torque compensation for the spacecraft ensures the attitude stabilization in the process of capturing and parameter identification.

Capturing a target will always be a crucial part of any orbital activity for space robots. This chapter aims to present an overview of the developed technologies and algorithms that enable a robot to grasp a target in microgravity. Studies on human grasping in microgravity are described both for providing a solid background on orbital grasping and as inspiration for the development of robotic systems to aid astronauts. The chapter also describes the most important past and future applications of target capturing with robotic arms. The core analysis of the chapter consists of a large number of studies and engineering milestones on orbital robotic capturing that are categorised based on the means of interaction with the target, as well as reporting the state-of-the-art grasping methods. A number of important missions that have grasping as their basic demonstrated technology are also presented. The chapter ends with outlining the most important physical, algorithmic, and operational challenges in orbital robotic grasping, and setting up the capabilities that future robotic systems need to possess.

Astronauts have to complete hundreds of hours of training with simulation systems that help them to improve their ability to operate robotic arms for docking opera-tions. In docking tasks, performed for example on the Canadarm2, the operator does not have direct view of the International Space Station (ISS) but relies instead on visual feedback from multiple 2D camera views. Failure to accomplish the tasks on time costs millions of pounds and can potentially endanger the life of the crew members. Even in simulated tasks of the Soyuz-TMA(Transportation Modified Anthropometric) approach and docking, tension and anxiety build up quickly as the precision required is high and virtual fuels are limited. In this chapter, we investigate how simulation systems can be used as a platform to enhance and measure an operator's performance, as well as to design and evaluate semi-autonomous modes of operation that facilitates effective human-robot collaboration. Furthermore, we review how brain-computer interfaces (BCIs) can monitor workload, attention and fatigue. These systems can be evolved to provide an intuitive human-robot interaction experience that provides guidance and feedback as they are needed.

Space is a hostile environment for life and can induce profound changes in the physiology of astronauts. These changes mainly result from space radiation exposure, microgravity, the lack of an atmosphere and resulting body fluid shifts. The human body proceeds with various homeostatic adaptations that influence the cardiovascular, nervous, musculoskeletal, endocrine and other physiological systems. To adopt and develop wearable technologies that facilitate human-robot interaction in space and astronaut biomonitoring, the underlying physiological changes should be considered. In this chapter, we review basic concepts of these body adaptations, as well as biomarkers and biosignals for biomonitoring and how these can change due to the space environment. Finally, we review wearable biosignal monitoring technologies that can quantify sweat, heart rate (HR) and endocrine responses, which can act as indices of acute stress and increased workload.

The rise of autonomous space systems drives the need to expand and enrich the field of human-robot interaction (HRI). As opposed to becoming obsolete in the face of autonomous systems, HRI research becomes more important as autonomous systems are far from being a self-planning and self-actuating agent - on Earth or in Space. New tools and approaches are necessary for the successful adoption and integration of autonomous space robots in new human-robot teaming paradigms and in the shift from teleoperations to shared autonomy. This chapter investigates the existing challenges, trends in capabilities, future opportunities and motivations for developments of HRI in space.

Verification techniques such as formal verification, simulation and testing are useful when ensuring systems are safe, trustworthy and meet their stated requirements. They are needed for space robotics as failures in space may be much more critical, costly and harder to resolve. We have discussed several tools and techniques for verification of space robotics with reference to some simple space scenarios. Recommendations include designing systems for verification using a modular approach separating concerns, embedding verification and validation into engineering process, and using a range of tools and techniques to improve confidence in space systems. Future trends include the greater need for and use of autonomy in space robotics, e.g., to support planetary missions with robots working closely with astronauts, where safety and functional correctness is crucial. Additionally, verification and validation is needed for the New Space sector to conform regulation and standards for applications such as satellite communication, imaging, navigation, space tourism and mining.

This chapter presents the details of our recent works on the security of such emerging space systems. Specifically, we present a method for identifying the attack surface of New Space systems. We describe a reference architecture (RA) to provide a visual aid to support the identification of attack surfaces. We then describe approaches for threat modelling that can be used for space system and discuss the requirements for effective threat modelling. We also present the challenges in cyber risk management for space systems and highlight the key characteristics for a thorough risk management approach and framework following works in. Furthermore, we describe a method for assurance of space systems which integrates threat modelling into the formal verification methodology of the system.