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\"Discusses how robots are used to explore planets and other bodies in space, advances in space robotics, and what we can learn from the data these robots gather\"--Provided by publisher.

Selected, peer reviewed papers from the 3th International Conference on Biomechanics, Neurorehabilitation, Mechanical Engineering, Manufacturing Systems, Robotics and Aerospace, October 26-28, 2012, Bucharest, Romania.

\"Did you know that robots play a very large role in the lives of humans? Discover the new ways that scientists hope to use robotics in the future in this high-interest book.\"-- Provided by publisher.

Robotic science missions in remote environments, such as deep ocean and outer space, can involve studying phenomena that cannot directly be observed using on-board sensors but must be deduced by combining measurements of correlated variables with domain knowledge. Traditionally, in such missions, robots passively gather data along prescribed paths, while inference, path planning, and other high level decision making is largely performed by a supervisory science team located at a different location, often at a great distance. However, communication constraints hinder these processes, and hence the rate of scientific progress. This paper presents an active perception approach that aims to reduce robots’ reliance on human supervision and improve science productivity by encoding scientists’ domain knowledge and decision making process on-board. We present a Bayesian network architecture to compactly model critical aspects of scientific knowledge while remaining robust to observation and modeling uncertainty. We then formulate path planning and sensor scheduling as an information gain maximization problem, and propose a sampling-based solution based on Monte Carlo tree search to plan informative sensing actions which exploit the knowledge encoded in the network. The computational complexity of our framework does not grow with the number of observations taken and allows long horizon planning in an anytime manner, making it highly applicable to field robotics with constrained computing. Simulation results show statistically significant performance improvements over baseline methods, and we validate the practicality of our approach through both hardware experiments and simulated experiments with field data gathered during the NASA Mojave Volatiles Prospector science expedition.

Readers learn all about the robots that have left Earth's atmosphere to explore space and perform special missions. The book provides an overview of the history of space robots, as well as the development of the newest robots used today. Readers will learn about how robots are changing our knowledge about space and unlocking its many secrets. This book also discusses the future of space technology.

In the five decades since NASA was created, the agency has sustained its legacy from the National Advisory Committee on Aeronautics (NACA) in playing a major role in U.S. aeronautics research and has contributed substantially to United States preeminence in civil and military aviation. This preeminence has contributed significantly to the overall economy and balance of trade of the United States through the sales of aircraft throughout the world. NASA's contributions have included advanced flight control systems, de-icing devices, thrust-vectoring systems, wing fuselage drag reduction configurations, aircraft noise reduction, advanced transonic airfoil and winglet designs, and flight systems. Each of these contributions was successfully demonstrated through NASA flight research programs. Equally important, the aircraft industry would not have adopted these and similar advances without NASA flight demonstration on full-scale aircraft flying in an environment identical to that which the aircraft are to operate-in other words, flight research. Flight research is a tool, not a conclusion. It often informs simulation and modeling and wind tunnel testing. Aeronautics research does not follow a linear path from simulation to wind tunnels to flying an aircraft. The loss of flight research capabilities at NASA has therefore hindered the agency's ability to make progress throughout its aeronautics program by removing a primary tool for research. Recapturing NASA's Aeronautics Flight Research Capabilities discusses the motivation for NASA to pursue flight research, addressing the aspects of the committee's task such as identifying the challenges where research program success can be achieved most effectively through flight research. The report contains three case studies chosen to illustrate the state of NASA ARMD. These include the ERA program and the Fundamental Research Program's hypersonics and supersonics projects. Following these case studies, the report describes issues with the NASA ARMD organization and management and offers solutions. In addition, the chapter discusses current impediments to progress, including demonstrating relevancy to stakeholders, leadership, and the lack of focus relative to available resources. Recapturing NASA's Aeronautics Flight Research Capabilities concludes that the type and sophistication of flight research currently being conducted by NASA today is relatively low and that the agency's overall progress in aeronautics is severely constrained by its inability to actually advance its research projects to the flight research stage, a step that is vital to bridging the confidence gap. NASA has spent much effort protecting existing research projects conducted at low levels, but it has not been able to pursue most of these projects to the point where they actually produce anything useful. Without the ability to actually take flight, NASA's aeronautics research cannot progress, cannot make new discoveries, and cannot contribute to U.S. aerospace preeminence.

\"Robots don't need to breathe, eat, or sleep. This makes them perfectly suited for work in the vacuum of space. Rovers on Mars have given humanity a wealth of knowledge about this planet, and machines that repair shuttles and other equipment are invaluable to astronauts. In this exciting STEM exploration, readers learn about space robots. Intriguing sidebars explore the ways science fiction has influenced the creation of real robots, and informative fact boxes and accessible main text discuss robots of the past, present, and future. Full-color photographs and a list of critical-thinking questions keep readers engaged as they learn\"-- Provided by publisher.

We present a novel method by which an aerial robot can learn sequences of task-relevant camera views within a multitasking environment. The robot learns these views by tracking the visual gaze of a human collaborator wearing an augmented reality headset. The spatial footprint of the human’s visual field is integrated in time and then fit to a Gaussian mixture model via expectation maximization. The modes of this model represent the visual-interest regions of the environment with each visual-interest region containing one human task. Using Q-learning, the robot is trained as to which visual-interest region it should photograph given the human’s most recent sequence of K tasks. This sequence of K tasks forms one state of a Markov Decision Process whose entry triggers an action—the robot’s selection of visual-interest region. The robot’s camera view is continuously streamed to the human’s augmented reality headset in order to artificially expand the human’s visual field-of-view. The intent is to increase the human’s multitasking performance and decrease their physical and mental effort. An experimental study is presented in which 24 humans were asked to complete toy construction tasks in parallel with spatially separated persistent monitoring tasks (e.g., buttons which would flash at random times to request input). Subjects participated in four 2-h sessions over multiple days. The efficacy of the autonomous view selection system is compared against control trials containing no assistance as well as supervised trials in which the subjects could directly command the robot to switch between views. The merits of this system were evaluated through both subjective measures, e.g., System Usability Scale and NASA Task Load Index, as well as objective measures, e.g., task completion time, reflex time, and head angular velocity. This algorithm is applicable to multitasking environments that require persistent monitoring of regions outside of a human’s (possibly restricted) field of view, e.g., spacecraft extravehicular activity.

This volume gathers the latest advances, innovations, and applications in the field of space robots as presented at the International Conference on Robots for Space Applications in Orbital Stations (TORVEASTRO), held in Rome, Italy on April 20-21, 2023. Topics addressed include history of space and robotics, bio-inspired space robotics, grasping, handling and intelligent manipulation, kinematics and dynamics, navigation & motion planning, robot vision and control, human-machine interfaces, new designs and prototypes, humanoid astronaut robots, and service space robots.

Manipulator mounted on an unmanned satellite could be used for performing orbital capture maneuver in order to repair satellites or remove space debris from orbit. Use of manipulators for such purposes presents unique challenges, as high level of autonomy is required and the motion of the manipulator influences the position and orientation of the manipulator-equipped satellite. This paper presents a new control system that consists of two modules: trajectory planning module (based on trajectory optimization algorithm) and Model Predictive Controller. Both modules take into account the free-floating nature of the satellite-manipulator system. Proposed control system was tested in numerical simulations performed for a simplified planar case. In the first set of simulations Nonlinear Model Predictive Control (NMPC) was used to ensure realization of a square reference end-effector trajectory, while in the second set control system was used for optimizing and then ensuring realization of the trajectory that leads to grasping of the rotating target satellite. Simulations were performed with disturbances and with the assumed non-perfect knowledge of parameters of the satellite-manipulator system. Results obtained with NMPC are better than results obtained with the controller based on the Dynamic Jacobian inverse and with the Modified Simple Adaptive Control (MSAC).