Search Results (3)

This paper deals with the mechanical design and kinematic analysis of an autonomous wrist for space assembly (AWSA) whose actuators are activated by DC motors and ball screw drives. This robotic wrist was developed and built as a prototype to investigate in-space robotic operations, including maintaining and repairing spacecraft of the US National Aeronautics and Space Administration (NASA), such as the International Space Station (ISS) or satellites. Despite its disadvantages, such as a small workspace and low maneuverability, a parallel structure instead of a serial structure was selected for the design of the AWSA due to several advantages it has over a serial robot manipulator (SRM), including higher payload, greater stiffness, and better stability. The present paper also introduces a hybrid concept for robotic space operations, which combines an SRM performing gross motion and a parallel robot manipulator (PRM) performing fine motion. It then discusses the design and construction of the DC motor actuators and ball screw drives and presents the kinematic equations developed for the AWSA. This paper provides a closed-form solution to the inverse kinematics of the AWSA and a numerical solution using the Newton–Raphson method for its forward kinematics. Full article

The next generation of large space infrastructure will require crucial advancements in current technology. Current methodologies focus on large deployable structures folded into cramped payload fairings or revolutionary assembly techniques requiring many moving components. Utilizing both in-space assembly and deployable concepts, a hybrid mixed assembly scheme was posed using smaller deployable units interspersed with rigid connecting elements to assemble these large architectures. The Built On-Orbit Robotically Assembled Gigatruss (BORG) structure allows for modularity in assembly and repair with the number of separate elements comprising the structure to be reduced, compared to strut-by-strut assembly. The following documents the process of constructing and running physical trials on a prototype BORG architecture. Additionally, a Semantic and Fiducial Aided Graph Simultaneous Localization and Mapping (SF-GraphSLAM) approach is taken to verify the relation of assembled and deployed truss elements to aid in error evaluation and state estimation. This technology demonstration stands as a proof of concept in verifying the viability of the BORG architecture as a method for large structure assembly. Full article

The proliferation of reusable space vehicles has fundamentally changed how assets are injected into the low earth orbit and beyond, increasing both the reliability and frequency of launches. Consequently, it has led to the rapid development and adoption of new technologies in the aerospace sector, including computer vision (CV), machine learning (ML)/artificial intelligence (AI), and distributed networking. All these technologies are necessary to enable truly autonomous “Human-out-of-the-loop” mission tasking for spaceborne applications as spacecrafts travel further into the solar system and our missions become more ambitious. This paper proposes a novel approach for space-based computer vision sensing and machine learning simulation and validation using synthetically trained models to generate the large amounts of space-based imagery needed to train computer vision models. We also introduce a method of image data augmentation known as domain randomization to enhance machine learning performance in the dynamic domain of spaceborne computer vision to tackle unique space-based challenges such as orientation and lighting variations. These synthetically trained computer vision models then apply that capability for hardware-in-the-loop testing and evaluation via free-flying robotic platforms, thus enabling sensor-based orbital vehicle control, onboard decision making, and mobile manipulation similar to air-bearing table methods. Given the current energy constraints of space vehicles using solar-based power plants, cameras provide an energy-efficient means of situational awareness when compared to active sensing instruments. When coupled with computationally efficient machine learning algorithms and methods, it can enable space systems proficient in classifying, tracking, capturing, and ultimately manipulating objects for orbital/planetary assembly and maintenance (tasks commonly referred to as In-Space Assembly and On-Orbit Servicing). Given the inherent dangers of manned spaceflight/extravehicular activities (EVAs) currently employed to perform spacecraft maintenance and the current limitation of long-duration human spaceflight outside the low earth orbit, space robotics armed with generalized sensing and control and machine learning architecture have a unique automation potential. However, the tools and methodologies required for hardware-in-the-loop simulation, testing, and validation at a large scale and at an affordable price point are in developmental stages. By leveraging a drone’s free-flight maneuvering capability, theater projection technology, synthetically generated orbital and celestial environments, and machine learning, this work strives to build a robust hardware-in-the-loop testing suite. While the focus of the specific computer vision models in this paper is narrowed down to solving visual sensing problems in orbit, this work can very well be extended to solve any problem set that requires a robust onboard computer vision, robotic manipulation, and free-flight capabilities. Full article