Autonomous robotic assembly by mobile field robots has seen significant advances in recent decades, yet practicality remains elusive. Identified challenges include better use of state estimation to and reasoning with uncertainty, spreading out tasks to specialized robots, and implementing representative joining methods. This paper proposes replacing 1) self-correcting mechanical linkages with generalized joints for improved applicability, 2) assembly serial manipulators with parallel manipulators for higher precision and stability, and 3) all-in-one robots with a heterogeneous team of specialized robots for agent simplicity. This paper then describes a general assembly algorithm utilizing state estimation. Finally, these concepts are tested in the context of solar array assembly, requiring a team of robots to assemble, bond, and deploy a set of solar panel mockups to a backbone truss to an accuracy not built into the parts. This paper presents the results of these tests
Developing a capability for the assembly of large space structures has the potential to increase the capabilities and performance of future space missions and spacecraft while reducing their cost. One such application is a megawatt-class solar electric propulsion (SEP) tug, representing a critical transportation ability for the NASA lunar, Mars, and solar system exploration missions. A series of robotic assembly experiments were recently completed at Langley Research Center (LaRC) that demonstrate most of the assembly steps for the SEP tug concept. The assembly experiments used a core set of robotic capabilities: long-reach manipulation and dexterous manipulation. This paper describes cross-cutting capabilities and technologies for in-space assembly (ISA), applies the ISA approach to a SEP tug, describes the design and development of two assembly demonstration concepts, and summarizes results of two sets of assembly experiments that validate the SEP tug assembly steps.
As NASA exploration moves beyond earth’s orbit, the need exists for long duration space systems that are resilient to events that compromise safety and performance. Fortunately, technology advances in autonomy, robotic manipulators, and modular plug-and-play architectures over the past two decades have made in-space vehicle assembly and servicing possible at acceptable cost and risk. This study evaluates future space systems needed to support scientific observatories and human/robotic Mars exploration to assess key structural design considerations. The impact of in-space assembly is discussed to identify gaps in structural technology and opportunities for new vehicle designs to support NASA’s future long duration missions.
Satellite servicing is a high priority task for NASA and the space industry, addressing the needs of a variety of missions, and potentially lowering the overall cost of missions through refurbishment and reuse. However, the ability to service satellites is severely limited by the lack of long reach manipulation capability and inability to launch new devices due the end of the Space Transport System, or Space Shuttle Program. This paper describes the design and implementation of a control system for a Tendon-Actuated Lightweight InSpace MANipulator (TALISMAN), including; defining the forward and inverse kinematics, endpoint velocity to motor velocity, required cable tensions, and a proportional-integralderivative (PID) controller. The tensions and velocities necessary to maneuver and capture small and large payloads are also discussed. To demonstrate the utility of the TALISMAN for satellite servicing, this paper also describes a satellite servicing demonstration using two TALISMAN prototypes to grasp and inspect a satellite mockup. Potential avenues for improving the control system are discussed.
A method is described to construct precise truss structures from non-precise commodity parts. Trusses with precision in the order of micrometers, such as the truss of a space telescope, can be accomplished with precisely machined truss connection systems. This approach is expensive, heavy, and prone to failure, e.g., when a single element is lost. In the past, a novel concept was proposed in which non-precise commodity parts can be aligned using precise jigging robots and then welded in place. Even when using highly precise sensors and actuators, this approach can still lead to errors due to thermal expansion and structural deformation. In this paper, an EKF-based SLAM approach is described and experimentally evaluated that allows a team of intelligent precision jigging robots (IPJR) to maintain a common estimate of the structure’s pose, improve this estimate during loop closures in the construction process, and uses this estimate to correct for errors during construction. It is also shown that attaching a new node to the assembly site with the lowest uncertainty significantly increases accuracy.
This paper describes an Intelligent Precision Jigging Robot (IPJR) prototype that enables the precise alignment and welding of titanium space telescope optical benches. The IPJR, equipped with µm accuracy sensors and actuators, worked in tandem with a lower precision remote controlled manipulator. The combined system assembled and welded a 2 m truss from stock titanium components. The calibration of the IPJR, and the difference between the predicted and the truss dimensions as-built, identified additional sources of error that should be addressed in the next generation of IPJRs in 2D and 3D.
An algorithmic approach for assembly path planning is proposed that takes stability of the structure during construction into account. Finite Element Analysis (FEA) is used to evaluate the intermediate stages of the assembly for stability. The algorithm presented here assembles a structure by greedily taking the most stable option at each step in the assembly process, and has complexity O(n!), albeit most structures are effectively assembled with complexity O(n 2 ). The workings of the proposed hybrid discrete/FEA search algorithm are demonstrated in simulation on a series of truss structures. In particular, it is shown that the algorithm is able to identify correct orderings that led to stable assembly, and discuss structures for which a greedy approach with scaffolding might be advantageous over a complete approach.
A description of an Intelligent Precision Jigging Robot (IPJR), which allows high precision assembly of commodity parts with low-precision bonding. The preliminary experiments are presented in 2D that are motivated by the problem of assembling a space telescope optical bench on orbit using inexpensive, stock hardware and low-precision welding. An IPJR is a robot that acts as the precise “jigging”, holding parts of a local assembly site in place while an external low precision assembly agent cuts and welds members. The prototype presented in this paper allows an assembly agent (in this case, a human using only low precision tools), to assemble a 2D truss made of wooden dowels to a precision on the order of millimeters over a span on the order of meters. The challenges of designing the IPJR are presented, including hardware and software, analyzing the error in assembly, documenting the test results over several experiments (including a large-scale ring structure), and describing future work to implement the IPJR in 3D and with micron precision.
Within NASA Space Science, Exploration and the Office of Chief Technologist, there are Grand Challenges and advanced future exploration, science and commercial mission applications that could benefit significantly from large-span and large-area structural systems. Of particular and persistent interest to the Space Science community is the desire for large (in the 10- 50 meter range for main aperture diameter) space telescopes that would revolutionize space astronomy. Achieving these systems will likely require on-orbit assembly, but previous approaches for assembling large-scale telescope truss structures and systems in space have been perceived as very costly because they require high precision and custom components. These components rely on a large number of mechanical connections and supporting infrastructure that are unique to each application. In this paper, a new assembly paradigm that mitigates these concerns is proposed and described. A new assembly approach, developed to implement the paradigm, is developed incorporating: Intelligent Precision Jigging Robots, Electron-Beam welding, robotic handling/manipulation, operations assembly sequence and path planning, and low precision weldable structural elements. Key advantages of the new assembly paradigm, as well as concept descriptions and ongoing research and technology development efforts for each of the major elements are summarized.
In space mission planning, scenarios that lead to mission failure, such as impact and escape for a small body orbiter, must be discovered and avoided. This problem becomes challenging if the spacecraft cannot communicate with Earth in sufficient time to make a decision, or if the dynamics of the system are unknown or uncertain, such as asteroid systems. This paper introduces a technique for automatically and intelligently exploring the reachability set of a spacecraft: the set of trajectories from a given initial condition that are possible under a specified range of control actions, so that ∆V expenditures leading to failure can be identified and avoided. The high dimension of this problem and the nonlinear nature of gravitational interactions make reachability sets hard to compute and all but impossible to visualize. Currently, analytical approximations and heuristic reasoning about variations on known solutions are employed to plan space missions. This could both miss out on novel and improved design solutions, and also be impractical in unknown environments. The goal of this work is to automatically map out the regions that lead to failure and success. Brute-force exploration of reachability sets is computationally prohibitive, so one must focus on regions of interest: the boundaries between impact, escape, and in-system regions, known collectively as the end result regions. Doing so results in higher quality reachability sets with less error, leading to improved confidence in planning. This paper describes the end-result boundary heuristic and compares the accuracy of the end result determination to natively created reachability set meshes.
This paper introduces a new technique for intelligently exploring the reachability set of a spacecraft: the set of trajectories from a given initial condition that are possible under a specified range of control actions. The high dimension of this problem and the nonlinear nature of gravitational interactions make the geometry of these sets complicated, hard to compute, and all but impossible to visualize. Currently, exploration of a problem’s state space is done heuristically, based on previously identified solutions. This potentially misses out on improved mission design solutions that are not close to previous approaches. The goal of the work described here is to map out reachability sets automatically. This would not only aid human mission planners, but also allow a spacecraft to determine its own course without input from Earth-based controllers. Brute-force approaches to this are computationally prohibitive, so one must focus the effort on regions that are of interest: where neighboring trajectories diverge quickly, for instance, or come close to a body that the spacecraft is orbiting. This paper focuses on the first of those two criteria; the goal is to identify regions in the system’s state space where small changes have large effects— or vice versa—and concentrate the computational mesh accordingly