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At least 19 records

Autonomous Robotic Manipulator Software

Autonomous robotic manipulation requires a deep and wide stack of supporting software. This paper presents Autonomous Robotic Manipulator Software (ARMS), a software suite designed at NASA Langley Research Center to support research and development of different algorithms for In-space Servicing, Assembly and Manufacturing (ISAM). ARMS solves common challenges along the autonomous manipulation software stack. Various challenges, such as integration with commercial hardware, simulation, and path planning, are solved through the use of Robot Operating System 2 and its community-developed packages. Other challenges, such as configuration management and task definition, and execution are solved in software built on those tools. The result is a modular approach to robotic system definition, agent actions, and assembly task definitions. ARMS has been used in two ISAM projects at NASA Langley Research Center, the Precision Assembled Space Structures project and the Built On-orbit Robotically assembled Gigatruss project.

Collin J Cresta↗

Robotic Specialization in Autonomous Robotic Structural Assembly

Robotic in-space assembly of large space structures is a long-term NASA goal to reduce launch costs and enable larger scale missions. Recently, researchers have proposed using discrete lattice building blocks and co-designed robots to build high-performance, scalable primary structure for various on-orbit and surface applications. These robots would locomote on the lattice and work in teams to build and reconfigure building-blocks into functional structure. However, the most reliable and efficient robotic system architecture, characterized by the number of different robotic 'species' and the allocation of functionality between species, is an open question. To address this problem, we decompose the robotic building-block assembly task into functional primitives and, in simulation, study the performance of the the variety of possible resulting architectures. For a set consisting of five process types (move self, move block, move friend, align bock, fasten block), we describe a method of feature space exploration and ranking based on energy and reliability cost functions. The solution space is enumerated, filtered for unique solutions, and evaluated against energy and reliability cost functions for various simulated build sizes. We find that a 2 species system, dividing the five mentioned process types between one unit cell transport robot and one fastening robot, results in the lowest energy cost system, at some cost to reliability. This system enables fastening functionality to occupy the build front while reducing the need for that functional mass to travel back and forth from a feed station. Because the details of a robot design affect the weighting and final allocation of functionality, a sensitivity analysis was conducted to evaluate the effect of changing mass allocations on architecture performance. Future systems with additional functionalities such as repair, inspection, and others may use this process to analyze and determine alternative robot architectures.

Bernus, Borbala↗

Autonomous Robotic Manipulator Software (ARMS)

Autonomous robotic manipulation requires a deep and wide stack of supporting software. This paper presents Autonomous Robotic Manipulator Software (ARMS), a software suite designed at NASA Langley Research Center to support research and development of different algorithms for In-space Servicing, Assembly and Manufacturing (ISAM). ARMS solves common challenges along the autonomous manipulation software stack. Various challenges, such as integration with commercial hardware, simulation, and path planning, are solved through the use of Robot Operating System 2 and its community-developed packages. Other challenges, such as configuration management and task definition, and execution are solved in software built on those tools. The result is a modular approach to robotic system definition, agent actions, and assembly task definitions. ARMS has been used in two ISAM projects at NASA Langley Research Center, the Precision Assembled Space Structures project and the Built On-orbit Robotically assembled Gigatruss project.

Collin J Cresta↗

Sim2Real Autonomous Robotic Exploration [Poster]

Autonomous robots offer promising solutions for exploration in environments that are inaccessible or hazardous to humans. Despite this, physical training of such robots is often constrained by safety risks, high cost or limited accessibility. This project presents an end-to-end simulation to reality pipeline leveraging Nvidia Isaac Sim and Boston Dynamics' Spot to enable autonomous navigation in indoor environments. A reinforcement learning policy is first trained using Nvidia Isaac Lab to establish Spot's locomotion pattern. Virtual LiDAR sensors are then integrated to perform SLAM-based navigation using simulated odometry. Finally, the simulated navigation scheme is transferred to a physical Spot robot to inspect and record images of a real-world room by repeating the learnt trajectory. The proposed framework highlights the potential of scalable training in simulation and reliable deployment in physical environments. Future directions include dynamic trajectory generation in unseen and challenging environments and integration of environmental sensing like temperature, radiation or humidity via sensor and material simulation.

97 - MATHEMATICS AND COMPUTING↗

Development of a Commercially Viable, Modular Autonomous Robotic Systems for Converting any Vehicle to Autonomous Control

A Modular Autonomous Robotic System (MARS), consisting of a modular autonomous vehicle control system that can be retrofit on to any vehicle to convert it to autonomous control and support a modular payload for multiple applications is being developed. The MARS design is scalable, reconfigurable, and cost effective due to the use of modern open system architecture design methodologies, including serial control bus technology to simplify system wiring and enhance scalability. The design is augmented with modular, object oriented (C++) software implementing a hierarchy of five levels of control including teleoperated, continuous guidepath following, periodic guidepath following, absolute position autonomous navigation, and relative position autonomous navigation. The present effort is focused on producing a system that is commercially viable for routine autonomous patrolling of known, semistructured environments, like environmental monitoring of chemical and petroleum refineries, exterior physical security and surveillance, perimeter patrolling, and intrafacility transport applications.

guidepath position navigation control system↗

Diagnosing faults in autonomous robot plan execution

A major requirement for an autonomous robot is the capability to diagnose faults during plan execution in an uncertain environment. Many diagnostic researches concentrate only on hardware failures within an autonomous robot. Taking a different approach, the implementation of a Telerobot Diagnostic System that addresses, in addition to the hardware failures, failures caused by unexpected event changes in the environment or failures due to plan errors, is described. One feature of the system is the utilization of task-plan knowledge and context information to deduce fault symptoms. This forward deduction provides valuable information on past activities and the current expectations of a robotic event, both of which can guide the plan-execution inference process. The inference process adopts a model-based technique to recreate the plan-execution process and to confirm fault-source hypotheses. This technique allows the system to diagnose multiple faults due to either unexpected plan failures or hardware errors. This research initiates a major effort to investigate relationships between hardware faults and plan errors, relationships which were not addressed in the past. The results of this research will provide a clear understanding of how to generate a better task planner for an autonomous robot and how to recover the robot from faults in a critical environment.

Lam, Raymond K.↗

Diagnosing faults in autonomous robot plan execution

A major requirement for an autonomous robot is the capability to diagnose faults during plan execution in an uncertain environment. Many diagnostic researches concentrate only on hardware failures within an autonomous robot. Taking a different approach, the implementation of a Telerobot Diagnostic System that addresses, in addition to the hardware failures, failures caused by unexpected event changes in the environment or failures due to plan errors, is described. One feature of the system is the utilization of task-plan knowledge and context information to deduce fault symptoms. This forward deduction provides valuable information on past activities and the current expectations of a robotic event, both of which can guide the plan-execution inference process. The inference process adopts a model-based technique to recreate the plan-execution process and to confirm fault-source hypotheses. This technique allows the system to diagnose multiple faults due to either unexpected plan failures or hardware errors. This research initiates a major effort to investigate relationships between hardware faults and plan errors, relationships which were not addressed in the past. The results of this research will provide a clear understanding of how to generate a better task planner for an autonomous robot and how to recover the robot from faults in a critical environment.

Lam, Raymond K.↗

An architectural approach to create self organizing control systems for practical autonomous robots

For practical industrial applications, the development of trainable robots is an important and immediate objective. Therefore, the developing of flexible intelligence directly applicable to training is emphasized. It is generally agreed upon by the AI community that the fusion of expert systems, neural networks, and conventionally programmed modules (e.g., a trajectory generator) is promising in the quest for autonomous robotic intelligence. Autonomous robot development is hindered by integration and architectural problems. Some obstacles towards the construction of more general robot control systems are as follows: (1) Growth problem; (2) Software generation; (3) Interaction with environment; (4) Reliability; and (5) Resource limitation. Neural networks can be successfully applied to some of these problems. However, current implementations of neural networks are hampered by the resource limitation problem and must be trained extensively to produce computationally accurate output. A generalization of conventional neural nets is proposed, and an architecture is offered in an attempt to address the above problems.

Greiner, Helen↗

Applications of concurrent neuromorphic algorithms for autonomous robots

This article provides an overview of studies at the Oak Ridge National Laboratory (ORNL) of neural networks running on parallel machines applied to the problems of autonomous robotics. The first section provides the motivation for our work in autonomous robotics and introduces the computational hardware in use. Section 2 presents two theorems concerning the storage capacity and stability of neural networks. Section 3 presents a novel load-balancing algorithm implemented with a neural network. Section 4 introduces the robotics test bed now in place. Section 5 concerns navigation issues in the test-bed system. Finally, Section 6 presents a frequency-coded network model and shows how Darwinian techniques are applied to issues of parameter optimization and on-line design.

Barhen, J.↗

Demonstration of a Spoken Dialogue Interface for Planning Activities of a Semi-autonomous Robot

Planning and scheduling in the face of uncertainty and change pushes the capabilities of both planning and dialogue technologies by requiring complex negotiation to arrive at a workable plan. Planning for use of semi-autonomous robots involves negotiation among multiple participants with competing scientific and engineering goals to co-construct a complex plan. In NASA applications this plan construction is done under severe time pressure so having a dialogue interface to the plan construction tools can aid rapid completion of the process. But, this will put significant demands on spoken dialogue technology, particularly in the areas of dialogue management and generation. The dialogue interface will need to be able to handle the complex dialogue strategies that occur in negotiation dialogues, including hypotheticals and revisions, and the generation component will require an ability to summarize complex plans. This demonstration will describe a work in progress towards building a spoken dialogue interface to the EUROPA planner for the purposes of planning and scheduling the activities of a semi-autonomous robot. A prototype interface has been built for planning the schedule of the Personal Satellite Assistant (PSA), a mobile robot designed for micro-gravity environments that is intended for use on the Space Shuttle and International Space Station. The spoken dialogue interface gives the user the capability to ask for a description of the plan, ask specific questions about the plan, and update or modify the plan. We anticipate that a spoken dialogue interface to the planner will provide a natural augmentation or alternative to the visualization interface, in situations in which the user needs very targeted information about the plan, in situations where natural language can express complex ideas more concisely than GUI actions, or in situations in which a graphical user interface is not appropriate.

Dowding, John↗

Detection of Water Hazards for Autonomous Robotic Vehicles

Four methods of detection of bodies of water are under development as means to enable autonomous robotic ground vehicles to avoid water hazards when traversing off-road terrain. The methods involve processing of digitized outputs of optoelectronic sensors aboard the vehicles. It is planned to implement these methods in hardware and software that would operate in conjunction with the hardware and software for navigation and for avoidance of solid terrain obstacles and hazards. The first method, intended for use during the day, is based on the observation that, under most off-road conditions, reflections of sky from water are easily discriminated from the adjacent terrain by their color and brightness, regardless of the weather and of the state of surface waves on the water. Accordingly, this method involves collection of color imagery by a video camera and processing of the image data by an algorithm that classifies each pixel as soil, water, or vegetation according to its color and brightness values (see figure). Among the issues that arise is the fact that in the presence of reflections of objects on the opposite shore, it is difficult to distinguish water by color and brightness alone. Another issue is that once a body of water has been identified by means of color and brightness, its boundary must be mapped for use in navigation. Techniques for addressing these issues are under investigation. The second method, which is not limited by time of day, is based on the observation that ladar returns from bodies of water are usually too weak to be detected. In this method, ladar scans of the terrain are analyzed for returns and the absence thereof. In appropriate regions, the presence of water can be inferred from the absence of returns. Under some conditions in which reflections from the bottom are detectable, ladar returns could, in principle, be used to determine depth. The third method involves the recognition of bodies of water as dark areas in short-wavelength infrared (SWIR) images. This method is based on the fact, well known among experts in remote sensing, that water bodies of any appreciable depth appear very dark in near-infrared, overhead imagery. Even under a thick layer of marine fog, SWIR illumination is present. Hence, this method may work even in the presence of clouds, though it is unlikely to work at night. Snow and ice also exhibit very strong absorption at wavelengths greater than about 1.4 m. Hence, the wavelength range of about 1.5 to 1.6 m might be useable in this method for recognizing water, snow, and ice. One notable drawback of this method is that useful look-ahead distance could be limited by surface reflections. The fourth method, intended for use at night, involves the contrast between water and terrain in thermal-infrared (medium-wavelength infrared) imagery. This method is based on the fact that at night, water is usually warmer than the adjacent terrain. Look-ahead distance could be limited in this method because, for reasons not yet fully understood, water appears to darken in the thermal infrared with increasing distance.

Matthes, Larry↗

The Ocean’s Breath: How Autonomous Robotic Vehicles at Sea and Super Computers are Helping us Understand the Carbon Cycle in the Ocean

Carbon dioxide due to human activities such as fossil fuel burning has well-known and concerning implications for global temperatures as well as terrestrial and marine ecosystems. Hence, an important objective of NASA Earth science is to observe and understand the variations in the global carbon cycle and the associated climate and ecosystem impacts in the context of past and projected anthropogenic emissions. In my presentation, I will tell the story of how some of my collaborators and I are using autonomous robotic vehicles and super computers to try to untangle the mysteries of the carbon cycle in the ocean, which depends in fascinating ways on atmospheric and ocean physics, marine ecosystem dynamics, as well as the chemistry of the seawater.

Ocean's breath↗

Modeling Deformable Linear Objects for Autonomous Robotic Outfitting of Lunar Surface Systems

The construction of in-space facilities is a significant capability for the establishment of a long-term human presence in space. Autonomous robotic technologies are a critical tool to enabling the construction and maintenance of such permanent facilities. This paper evaluates the outfitting portion of the construction process, focusing on modeling required for robotic manipulation of cable-like objects, referred to as Deformable Linear Objects (DLOs). DLOs contain a high degree of variability, which makes accurate real-time autonomous operations a difficult task. Different modeling methods for DLOs such as discrete mass-spring systems and Cosserat elastic rod models range in problem complexity and accuracy, a trade-off that must be understood to fully realize autonomous cable routing technologies. This paper validates different DLO models through simulation and a hardware experiment, highlighting the size of the state space and accuracy of different approaches. The development of DLO manipulation models for outfitting enables an autonomous architecture for the construction of lunar surface systems.

Amy M Quartaro↗

Modeling Deformable Linear Objects for Autonomous Robotic Outfitting of Lunar Surface Systems

The construction of in-space facilities is a significant capability for the establishment of a long-term human presence in space. Autonomous robotic technologies are a critical tool to enabling the construction and maintenance of such permanent facilities. This paper evaluates the outfitting portion of the construction process, focusing on modeling required for robotic manipulation of cable-like objects, referred to as Deformable Linear Objects (DLOs). DLOs contain a high degree of variability, which makes accurate real-time autonomous operations a difficult task. Different modeling methods for DLOs such as discrete mass-spring systems and Cosserat elastic rod models range in problem complexity and accuracy, a trade-off that must be understood to fully realize autonomous cable routing technologies. This paper validates different DLO models through simulation and a hardware experiment, highlighting the size of the state space and accuracy of different approaches. The development of DLO manipulation models for outfitting enables an autonomous architecture for the construction of lunar surface systems.

Deformable Modeling↗

Tele-assistance for semi-autonomous robots

This paper describes a new approach in semi-autonomous mobile robots. In this approach the robot has sufficient computerized intelligence to function autonomously under a certain set of conditions, while the local system is a cooperative decision making unit that combines human and machine intelligence. Communication is then allowed to take place in a common mode and in a common language. A number of exception-handling scenarios that were constructed as a result of experiments with actual sensor data collected from two mobile robots were presented.

Rogers, Erika↗

Knowledge/geometry-based Mobile Autonomous Robot Simulator (KMARS)

Ongoing applied research is focused on developing guidance system for robot vehicles. Problems facing the basic research needed to support this development (e.g., scene understanding, real-time vision processing, etc.) are major impediments to progress. Due to the complexity and the unpredictable nature of a vehicle's area of operation, more advanced vehicle control systems must be able to learn about obstacles within the range of its sensor(s). A better understanding of the basic exploration process is needed to provide critical support to developers of both sensor systems and intelligent control systems which can be used in a wide spectrum of autonomous vehicles. Elcee Computek, Inc. has been working under contract to the Flight Dynamics Laboratory, Wright Research and Development Center, Wright-Patterson AFB, Ohio to develop a Knowledge/Geometry-based Mobile Autonomous Robot Simulator (KMARS). KMARS has two parts: a geometry base and a knowledge base. The knowledge base part of the system employs the expert-system shell CLIPS ('C' Language Integrated Production System) and necessary rules that control both the vehicle's use of an obstacle detecting sensor and the overall exploration process. The initial phase project has focused on the simulation of a point robot vehicle operating in a 2D environment.

Cheng, Linfu↗

Vision Based Autonomous Robotic Control for Advanced Inspection and Repair

The advanced inspection system is an autonomous control and analysis system that improves the inspection and remediation operations for ground and surface systems. It uses optical imaging technology with intelligent computer vision algorithms to analyze physical features of the real-world environment to make decisions and learn from experience. The advanced inspection system plans to control a robotic manipulator arm, an unmanned ground vehicle and cameras remotely, automatically and autonomously. There are many computer vision, image processing and machine learning techniques available as open source for using vision as a sensory feedback in decision-making and autonomous robotic movement. My responsibilities for the advanced inspection system are to create a software architecture that integrates and provides a framework for all the different subsystem components; identify open-source algorithms and techniques; and integrate robot hardware.

sensory feedback↗