Autonomous ground-based robotic system for Preparatory Consequence Management radiation mapping and forensic studies
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The global dynamic behavior of multiple interacting autonomous mobile robots with simple navigation strategies is studied. Here, the effective spatial domain of each robot is taken to be a closed ball about its mass center. It is assumed that each robot has a specified cone of visibility such that interaction with other robots takes place only when they enter its visibility cone. Based on a particle model for the robots, various simple homing and collision-avoidance navigation strategies are derived. Then, an analysis of the dynamical behavior of the interacting robots in unbounded spatial domains is made. The article concludes with the results of computer simulations studies of two or more interacting robots.
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An intelligent robotic architecture that autonomously synthesizes goal-oriented behaviors, while connecting sensing and action in real-time, is presented with applications to loosely defined planetary sampling missions. This architecture is composed of perception and action nets interconneted in closed loops.
The following problem is considered: A point robot is placed in a terrain populated by an unknown number of polyhedral obstacles of varied sizes and locations in two/three dimensions. The robot is equipped with a sensor capable of detecting all the obstacle vertices and edges that are visible from the present location of the robot. The robot is required to autonomously navigate and build the complete terrain model using the sensor information. It is established that the necessary number of scanning operations needed for complete terrain model acquisition by any algorithm that is based on scan from vertices strategy is given by the summation of i = 1 (sup n) N(O sub i)-n and summation of i = 1 (sup n) N(O sub i)-2n in two- and three-dimensional terrains respectively, where O = (O sub 1, O sub 2,....O sub n) set of the obstacles in the terrain, and N(O sub i) is the number of vertices of the obstacle O sub i.
The NASA Johnson Space Center Engineering Directorate is developing the Autonomous Extravehicular Robotic Camera (AERCam), a free-flying camera system for remote viewing and inspection of human spacecraft. The AERCam project team is currently developing a miniaturized version of AERCam known as Mini AERCam, a spherical nanosatellite 7.5 inches in diameter. Mini AERCam development builds on the success of AERCam Sprint, a 1997 Space Shuttle flight experiment, by integrating new on-board sensing and processing capabilities while simultaneously reducing volume by 80%. Achieving these productivity-enhancing capabilities in a smaller package depends on aggressive component miniaturization. Technology innovations being incorporated include micro electromechanical system (MEMS) gyros, "camera-on-a-chip" CMOS imagers, rechargeable xenon gas propulsion, rechargeable lithium ion battery, custom avionics based on the PowerPC 740 microprocessor, GPS relative navigation, digital radio frequency communications and tracking, micropatch antennas, digital instrumentation, and dense mechanical packaging. The Mini AERCam free-flyer will initially be integrated into an approximate flight-like configuration for laboratory demonstration on an airbearing table. A pilot-in-the-loop and hardware-in-the-loop simulation to simulate on-orbit navigation and dynamics will complement the airbearing table demonstration. The Mini AERCam lab demonstration is intended to form the basis for future development of an AERCam flight system that provides on-orbit views of the Space Shuttle and International Space Station unobtainable from fixed cameras, cameras on robotic manipulators, or cameras carried by space-walking crewmembers.
An algorithm has been developed to enable a computer aboard a robot to autonomously plan the path of the manipulator arm of the robot to avoid collisions between the arm and any obstacle, which could be another part of the robot or an external object in the vicinity of the robot. In simplified terms, the algorithm generates trial path segments and tests each segment for potential collisions in an iterative process that ends when a sequence of collision-free segments reaches from the starting point to the destination. The main advantage of this algorithm, relative to prior such algorithms, is computational efficiency: the algorithm is designed to make minimal demands upon the limited computational resources available aboard a robot. This path-planning algorithm utilizes a modified version of the collision-detection method described in "Improved Collision-Detection Method for Robotic Manipulator" (NPO-30356), NASA Tech Briefs, Vol. 27, No. 3 (June 2003), page 72. The method involves utilization of mathematical models of the robot constructed prior to operation and similar models of external objects constructed automatically from sensory data acquired during operation. This method incorporates a previously developed method, known in the art as the method of oriented bounding boxes (OBBs), in which an object is represented approximately, for computational purposes, by a box that encloses its outer boundary. Because many parts of a robotic manipulator are cylindrical, the OBB method has been extended in this method to enable the approximate representation of cylindrical parts by use of octagonal or other multiple-OBB assemblies denoted oriented bounding prisms (OBPs). A multiresolution OBB/OBP representation of the robot and its manipulator arm and a multiresolution OBB representation of external objects (including terrain) are constructed and used in a process in which collisions at successively finer resolutions are detected through computational detection of overlaps between the corresponding OBB and OBP models. For computational efficiency, the process is started at the coarsest resolution and stopped as soon as possible, preferably before reaching the finest resolution. At the coarsest resolution, there is a single OBB enclosing all relevant external objects and a single OBB enclosing the entire robot. At the next finer level of resolution, the coarsest-resolution OBB is divided into two OBBs, and so forth. If no collision is detected at the coarsest resolution, then there is no need for further computation to detect collisions. If a collision is detected at the coarsest resolution, then tests for collisions are performed at the next finer level of resolution. This process is continued to successively finer resolutions until either no more collisions are detected or the finest resolution is reached.
The Regolith Advanced Surface Systems Operations Robot (RASSOR) Phase 2 is an excavation robot for mining regolith on a planet like Mars. The robot is programmed using the Robotic Operating System (ROS) and it also uses a physical simulation program called Gazebo. This internship focused on various functions of the program in order to make it a more professional and efficient robot. During the internship another project called the Smart Autonomous Sand-Swimming Excavator was worked on. This is a robot that is designed to dig through sand and extract sample material. The intern worked on programming the Sand-Swimming robot, and designing the electrical system to power and control the robot.
Artificial intelligence concepts are applied to robotics. Artificial neural networks, expert systems and laser imaging techniques for autonomous space robots are being studied. A computer graphics laser range finder simulator developed by Wu has been used by Weiland and Norwood to study use of artificial neural networks for path planning and obstacle avoidance. Interest is expressed in applications of CLIPS, NETS, and Fuzzy Control. These applications are applied to robot navigation.
Briefing on the parallels between space exploration (planetary rovers) and autonomous vehicles (self-driving cars)
The objective of the project is to develop a robotics enabled eddy current testing system (REECTS) in automatic probe deployment, inspection, and data acquisition and analysis. The main functions of the REECTS are to: 1) identify geometry and locations of heat exchange tubes with assistance of an imaging recognition system; 2) precisely control the position and motion speed of ECT probes by an adaptive control system; 3) facilitate data analysis and real-time decision making for autonomous inspection assisted by machine learning algorithms.
We overview some of our recent work on highly autonomous mobile robots for planetary exploration.
This paper provides an overview of a new manipulation system developed for sampling and instrument placement from small autonomous mobile robots for Mars exploration. Selected out of the design space, two manipulators have been constructed and integrated into the Rocky 7 Mars rover prototype. This paper describes objectives and constraints for these manipulators, and presents the finished system and some results from its operation.
We explore task tolerances, i.e., allowable position or rotation inaccuracy, as an important resource to facilitate smooth and effective telemanipulation. Task tolerances provide a robot flexibility to generate smooth and feasible motions; however, in teleoperation, this flexibility may make the user’s control less direct. In this work, we implemented a telema nipulation system that allows a robot to autonomously adjust its configuration within task tolerances. We conducted a user study comparing a telemanipulation paradigm that exploits task tolerances (functional mimicry) to a paradigm that requires the robot to exactly mimic its human operator (exact mimicry), and assess how the choice in paradigm shapes user experience and task performance. Our results show that autonomous adjustments within task tolerances can lead to performance improvements without sacrificing perceived control of the robot. Additionally, we find that users perceive the robot to be more under control, predictable, fluent, and trustworthy in functional mimicry than in exact mimicry.
The ability to recover robust spatial descriptions from sensory information and to efficiently utilize these descriptions in appropriate planning and problem-solving activities are crucial requirements for the development of more powerful robotic systems. Traditional approaches to sensor interpretation, with their emphasis on geometric models, are of limited use for autonomous mobile robots operating in and exploring unknown and unstructured environments. Here, researchers present a new approach to robot perception that addresses such scenarios using a probabilistic tesselated representation of spatial information called the Occupancy Grid. The Occupancy Grid is a multi-dimensional random field that maintains stochastic estimates of the occupancy state of each cell in the grid. The cell estimates are obtained by interpreting incoming range readings using probabilistic models that capture the uncertainty in the spatial information provided by the sensor. A Bayesian estimation procedure allows the incremental updating of the map using readings taken from several sensors over multiple points of view. An overview of the Occupancy Grid framework is given, and its application to a number of problems in mobile robot mapping and navigation are illustrated. It is argued that a number of robotic problem-solving activities can be performed directly on the Occupancy Grid representation. Some parallels are drawn between operations on Occupancy Grids and related image processing operations.
Research activity has shifted from computer graphics and vision systems to the broader scope of applying concepts of artificial intelligence to robotics. Specifically, the research is directed toward developing Artificial Neural Networks, Expert Systems, and Laser Imaging Techniques for Autonomous Space Robots.
The NASA Johnson Space Center has developed a nanosatellite-class Free Flyer intended for future external inspection and remote viewing of human spacecraft. The Miniature Autonomous Extravehicular Robotic Camera (Mini AERCam) technology demonstration unit has been integrated into the approximate form and function of a flight system. The spherical Mini AERCam Free Flyer is 7.5 inches in diameter and weighs approximately 10 pounds, yet it incorporates significant additional capabilities compared to the 35-pound, 14-inch diameter AERCam Sprint that flew as a Shuttle flight experiment in 1997. Mini AERCam hosts a full suite of miniaturized avionics, instrumentation, communications, navigation, power, propulsion, and imaging subsystems, including digital video cameras and a high resolution still image camera. The vehicle is designed for either remotely piloted operations or supervised autonomous operations, including automatic stationkeeping, point-to-point maneuvering, and waypoint tracking. The Mini AERCam Free Flyer is accompanied by a sophisticated control station for command and control, as well as a docking system for automated deployment, docking, and recharge at a parent spacecraft. Free Flyer functional testing has been conducted successfully on both an airbearing table and in a six-degree-of-freedom closed-loop orbital simulation with avionics hardware in the loop. Mini AERCam aims to provide beneficial on-orbit views that cannot be obtained from fixed cameras, cameras on robotic manipulators, or cameras carried by crewmembers during extravehicular activities (EVA s). On Shuttle or International Space Station (ISS), for example, Mini AERCam could support external robotic operations by supplying orthogonal views to the intravehicular activity (IVA) robotic operator, supply views of EVA operations to IVA and/or ground crews monitoring the EVA, and carry out independent visual inspections of areas of interest around the spacecraft. To enable these future benefits with minimal impact on IVA operators and ground controllers, the Mini AERCam system architecture incorporates intelligent systems attributes that support various autonomous capabilities. 1) A robust command sequencer enables task-level command scripting. Command scripting is employed for operations such as automatic inspection scans over a region of interest, and operator-hands-off automated docking. 2) A system manager built on the same expert-system software as the command sequencer provides detection and smart-response capability for potential system-level anomalies, like loss of communications between the Free Flyer and control station. 3) An AERCam dynamics manager provides nominal and off-nominal management of guidance, navigation, and control (GN&C) functions. It is employed for safe trajectory monitoring, contingency maneuvering, and related roles. This paper will describe these architectural components of Mini AERCam autonomy, as well as the interaction of these elements with a human operator during supervised autonomous control.