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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS1988

Considerations for automated nap-of-the-earth rotorcraft flight

The authors consider nap-of-the-earth (NOE) rotorcraft flight as one of the applications in which obstacle avoidance plays a key role, and investigate the prospects of automating the guidance functions of NOE flight. Based on a proposed structure for the guidance functions, obstacle detection and obstacle avoidance are identified as the two critical components requiring substantial advancement before an automating guidance system can be realized. The major sources of difficulties in developing these two components are discussed, including sensor requirements for which a systematic analysis is provided.

Cheng, Victor H. L.↗
NASA NTRS1996

Follow-the-Leader Control for the PIPS Prototype Hardware

This report describes the payload inspection and processing system (PIPS), an automated system programmed off-line for inspection of space shuttle payloads after integration and prior to launch. PIPS features a hyper-redundant 18-degree of freedom (DOF) serpentine truss manipulator capable of snake like motions to avoid obstacles. During the summer of 1995, the author worked on the same project, developing a follow-the-leader (FTL) algorithm in graphical simulation which ensures whole arm collision avoidance by forcing ensuing links to follow the same tip trajectory. The summer 1996 work was to control the prototype PIPS hardware in follow-the-leader mode. The project was successful in providing FTL control in hardware. The STS-82 payload mockup was used in the laboratory to demonstrate serpentine motions to avoid obstacles in a realistic environment.

Williams, Robert L. II↗
NASA NTRS1994

The MITy micro-rover: Sensing, control, and operation

The sensory, control, and operation systems of the 'MITy' Mars micro-rover are discussed. It is shown that the customized sun tracker and laser rangefinder provide internal, autonomous dead reckoning and hazard detection in unstructured environments. The micro-rover consists of three articulated platforms with sensing, processing and payload subsystems connected by a dual spring suspension system. A reactive obstacle avoidance routine makes intelligent use of robot-centered laser information to maneuver through cluttered environments. The hazard sensors include a rangefinder, inclinometers, proximity sensors and collision sensors. A 486/66 laptop computer runs the graphical user interface and programming environment. A graphical window displays robot telemetry in real time and a small TV/VCR is used for real time supervisory control. Guidance, navigation, and control routines work in conjunction with the mapping and obstacle avoidance functions to provide heading and speed commands that maneuver the robot around obstacles and towards the target.

Malafeew, Eric↗
NASA NTRS1994

Perception for mobile robot navigation: A survey of the state of the art

In order for mobile robots to navigate safely in unmapped and dynamic environments they must perceive their environment and decide on actions based on those perceptions. There are many different sensing modalities that can be used for mobile robot perception; the two most popular are ultrasonic sonar sensors and vision sensors. This paper examines the state-of-the-art in sensory-based mobile robot navigation. The first issue in mobile robot navigation is safety. This paper summarizes several competing sonar-based obstacle avoidance techniques and compares them. Another issue in mobile robot navigation is determining the robot's position and orientation (sometimes called the robot's pose) in the environment. This paper examines several different classes of vision-based approaches to pose determination. One class of approaches uses detailed, a prior models of the robot's environment. Another class of approaches triangulates using fixed, artificial landmarks. A third class of approaches builds maps using natural landmarks. Example implementations from each of these three classes are described and compared. Finally, the paper presents a completely implemented mobile robot system that integrates sonar-based obstacle avoidance with vision-based pose determination to perform a simple task.

Kortenkamp, David↗
NASA NTRS1992

Reflexive Avoidance Of Obstacles By Robots

Array of sensors at elbow of robotic manipulator detects obstacles. Outputs of sensors processed into synthetic "force field" around each obstacle. Algorithm controlling manipulator uses "force field" to maneuver manipulator arm around each obstacle.

Vold, Havard I.↗
NASA NTRS1993

Simulation development of a forward sensor-enhanced low-altitude guidance system

The requirement to operate aircraft at low-altitude near the terrain is common in the military community and essential for helicopters. The risk and crew workload in this flight regime is severe, with navigation, guidance, and obstacle avoidance demanding high attention. A guidance system relying on digitized terrain elevation maps has been developed that employs airborne navigation, mission requirements, aircraft performance limits, and radar altimeter returns to generate a valley-seeking, low-altitude trajectory between waypoints for display to the pilot. This system has been flight demonstrated to 150 ft above ground level altitude, and is primarily limited by the ability of the pilot to perform obstacle detection and avoidance. In this study, a wide field of view forward sensor has been modeled and incorporated in the guidance system for the purpose of relieving the pilot of the obstacle avoidance duty. The results of a piloted, motion-based simulation of this enhanced low-altitude guidance system is presented. Simulated flights to 50 ft altitude in the presence of obstacles were demonstrated while maintaining situational awareness and close tracking of the guidance trajectory.

Zelenka, Richard E.↗
NASA NTRS1995

Robotic-Vehicle Perception Control For Detecting Obstacles

Obstacle-detection systems designed to make most of limited data-processing resources. Bump or step identified, from stereoscopic images of scene ahead, by obstacle-detection algorithm. If bump or step exceeds specified step height, marked as obstacle, and control actions taken to avoid obstacle.

Grandjean, Pierrick C.↗
NASA NTRS2007

Improved Path Planning Onboard the Mars Exploration Rovers

A revised version of the AutoNav (autonomous navigation with hazard avoidance) software running onboard each Mars Exploration Rover (MER) affords better obstacle avoidance than does the previous version. Both versions include GESTALT (Grid-based Estimation of Surface Traversability Applied to Local Terrain), a navigation program that generates local-terrain models from stereoscopic image pairs captured by onboard rover cameras; uses this information to evaluate candidate arcs that extend across the terrain from the current rover location; ranks the arcs with respect to hazard avoidance, minimization of steering time, and the direction towards the goal; and combines the rankings in a weighted vote to select an arc, along which the rover is then driven. GESTALT works well in navigating around small isolated obstacles, but tends to fail when the goal is on the other side of a large obstacle or multiple closely spaced small obstacles. When that occurs, the goal seeking votes and hazard avoidance votes conflict severely. The hazard avoidance votes will not allow the rover to drive through the unsafe area, and the waypoint votes will not allow enough deviation from the straight-line path for the rover to get around the hazard. The rover becomes stuck and is unable to reach the goal. The revised version of AutoNav utilizes a global path-planning program, Field D*, to evaluate the cost of traveling from the end of each GESTALT arc to the goal. In the voting process, Field D* arc votes supplant GESTALT goal-seeking arc votes. Hazard avoidance, steering bias, and Field D* votes are merged and the rover is driven a preset distance along the arc with the highest vote. Then new images are acquired and the process as described is repeated until the goal is reached. This new technology allows the rovers to autonomously navigate around much more complex obstacle arrangements than was previously possible. In addition, this improved autonomy enables longer traverses per Sol (a day on Mars), and can make planning drives easier for operators on Earth.

Stentz, Anthony↗
NASA NTRS2006

Detecting Negative Obstacles by Use of Radar

Robotic land vehicles would be equipped with small radar systems to detect negative obstacles, according to a proposal. The term "negative obstacles" denotes holes, ditches, and any other terrain features characterized by abrupt steep downslopes that could be hazardous for vehicles. Video cameras and other optically based obstacle-avoidance sensors now installed on some robotic vehicles cannot detect obstacles under adverse lighting conditions. Even under favorable lighting conditions, they cannot detect negative obstacles. A radar system according to the proposal would be of the frequency-modulation/ continuous-wave (FM/CW) type. It would be installed on a vehicle, facing forward, possibly with a downward slant of the main lobe(s) of the radar beam(s) (see figure). It would utilize one or more wavelength(s) of the order of centimeters. Because such wavelengths are comparable to the characteristic dimensions of terrain features associated with negative hazards, a significant amount of diffraction would occur at such features. In effect, the diffraction would afford a limited ability to see corners and to see around corners. Hence, the system might utilize diffraction to detect corners associated with negative obstacles. At the time of reporting the information for this article, preliminary analyses of diffraction at simple negative obstacles had been performed, but an explicit description of how the system would utilize diffraction was not available.

Mittskus, Anthony↗
DOE OSTI2024

Avoiding fusion plasma tearing instability with deep reinforcement learning

For stable and efficient fusion energy production using a tokamak reactor, it is essential to maintain a high-pressure hydrogenic plasma without plasma disruption. Therefore, it is necessary to actively control the tokamak based on the observed plasma state, to manoeuvre high-pressure plasma while avoiding tearing instability, the leading cause of disruptions. This presents an obstacle-avoidance problem for which artificial intelligence based on reinforcement learning has recently shown remarkable performance. However, the obstacle here, the tearing instability, is difficult to forecast and is highly prone to terminating plasma operations, especially in the ITER baseline scenario. Previously, we developed a multimodal dynamic model that estimates the likelihood of future tearing instability based on signals from multiple diagnostics and actuators. Here we harness this dynamic model as a training environment for reinforcement-learning artificial intelligence, facilitating automated instability prevention. We demonstrate artificial intelligence control to lower the possibility of disruptive tearing instabilities in DIII-D, the largest magnetic fusion facility in the United States. The controller maintained the tearing likelihood under a given threshold, even under relatively unfavourable conditions of low safety factor and low torque. In particular, it allowed the plasma to actively track the stable path within the time-varying operational space while maintaining H-mode performance, which was challenging with traditional preprogrammed control. This controller paves the path to developing stable high-performance operational scenarios for future use in ITER.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1991

Status of automated nap-of-the-earth rotorcraft guidance

The status of automated nap-of-the-earth (NOE) flight guidance is discussed by examining two current research efforts that approach the problem of performing obstacle avoidance along a pre-planned course from different perspectives. The first, a real-time guidance system developed by Systems Technology Inc., focuses on the issue of pilot acceptable maneuvers, under the assumption of an ideal, but nonrealizable, obstacle detection system. The second, a system created at NASA Ames, concentrates on the utilization of realistic on-board sensor data for obstacle detection and avoidance-maneuver decisions. This paper examines the strengths and weaknesses of these two approaches, and how they might complement each other. The goal is an automatic NOE system that is both acceptable to pilots and capable of making intelligent use of realistic sensor information and path-selection logic.

Coppenbarger, Richard A.↗
NASA NTRS1986

Single step optimization of feedback-decoupled collision avoidance manipulator maneuvers

Simultaneous robot path planning and path following is shown to be achievable in the presence of motor saturation and obstacle avoidance requirements. The discrete time algorithm derived performs one step ahead mean square optimization of commanded joint accelerations, subject to present actuator force or torque constraints and N step ahead prediction of configuration constraints.

Chen, N.↗
NASA NTRS1991

Trajectory optimization for kinematically redundant arms

A review of local optimization methods for resolving joint configurations in underconstrained manipulation tasks is conducted. A new approach is developed for observing joint limits and avoiding obstacles during the trajectory planning. The methodology is used in a four-link arm example to avoid a workspace singularity and is compared with results using the extended Moore-Penrose technique. An alternative measure of arm 'manipulability' based directly on the rank of the Jacobian is also introduced.

Carignan, Craig R.↗
NASA NTRS1992

Vision-based obstacle detection for rotorcraft flight

An obstacle detection approach to rotorcraft flight is described which is based on feature tracking and recursive range estimation. Flight characteristics are taken into account. A range map derived on the basis of this approach provides an advisory display to the pilot and can serve as input to an automatic obstacle-avoidance guidance system. A NASA CH-47 Chinook helicopter was used to develop an image and rotorcraft flight data base for verification of obstacle detection concepts. The performance of the passive range estimation algorithms is demonstrated using both laboratory image and flight image data.

Sridhar, Banavar↗
NASA NTRS2004

Icing Research Tunnel

The Icing Research Tunnel in Building 11 at the NASA Glenn Research Center is committed to researching the effects of in flight icing on aircraft and testing ways to stop the formation of hazardous icing conditions on planes. During this summer, I worked here with Richard DelRosa, the lead engineer for this area. address one of the major concerns of aviation: icing conditions. During the war, many planes crashed (especially supply planes going over the.Himalayas) because ice built up in their wings and clogged the engines. To this day, it remains the largest ice tunnel in the world, with a test section that measures 6 feet high, 9 feet long, and 20 feet wide. It can simulate airspeeds from 50 to 300 miles per hour at temperatures as low as -50 Fahrenheit. Using these capabilities, IRT can simulate actual conditions at high altitudes. The first thing I did was creating a cross reference in Microsoft Excel. It lists commands for the DPU units that control the pressure and temperature variations in the tunnel, as well as the type of command (keyboard, multiplier, divide, etc). The cross reference also contains the algorithm for every command, and which page it is listed in on the control sheet (visual Auto-CAD graphs, which I helped to make). I actually spent most of the time on the computer using Auto-CAD. I drew a diagram of the entire icing tunnel and then drew diagrams of its various parts. Between my mentor and me, we have drawings of every part of it, from the spray bars to the thermocouples, power cabinets, input-output connectors for power systems, and layouts of various other machines. I was also responsible for drawing schematics for the Escort system (which controls the spray bars), the power system, DPUs, and other electrical systems. In my spare time, I am attempting to build and program the "toddler". Toddler is a walking robot that I have to program in PBASIC language. When complete, it should be able to walk on level terrain while avoiding obstacles in real-time. It features an infrared detector that can keep it from falling over edges, as well as follow or avoid a light source. The toddler is giving me a much better understanding of the basics of electronic circuitry and computer programming.

Chennault, Jonathan↗
NASA NTRS2005

Autonomous Navigation by a Mobile Robot

ROAMAN is a computer program for autonomous navigation of a mobile robot on a long (as much as hundreds of meters) traversal of terrain. Developed for use aboard a robotic vehicle (rover) exploring the surface of a remote planet, ROAMAN could also be adapted to similar use on terrestrial mobile robots. ROAMAN implements a combination of algorithms for (1) long-range path planning based on images acquired by mast-mounted, wide-baseline stereoscopic cameras, and (2) local path planning based on images acquired by body-mounted, narrow-baseline stereoscopic cameras. The long-range path-planning algorithm autonomously generates a series of waypoints that are passed to the local path-planning algorithm, which plans obstacle-avoiding legs between the waypoints. Both the long- and short-range algorithms use an occupancy-grid representation in computations to detect obstacles and plan paths. Maps that are maintained by the long- and short-range portions of the software are not shared because substantial localization errors can accumulate during any long traverse. ROAMAN is not guaranteed to generate an optimal shortest path, but does maintain the safety of the rover.

Huntsberger, Terrance↗
NASA NTRS1992

Neural network structure for navigation using potential fields

A hybrid-network method for obstacle avoidance in the truck-backing system of D. Nguyen and B. Widrow (1989) is presented. A neural network technique for vehicle navigation and control in the presence of obstacles has been developed. A potential function which peaks at the surface of obstacles and has its minimum at the proper vehicle destination is computed using a network structure. The field is guaranteed not to have spurious local minima and does not have the property of flattening-out far from the goal. A feedforward neural network is used to control the steering of the vehicle using local field information. The network is trained in an obstacle-free space to follow the negative gradient of the field, after which the network is able to control and navigate the truck to its target destination in a space of obstacles which may be stationary or movable.

Plumer, Edward S.↗
NASA NTRS1988

Computer vision techniques for rotorcraft low-altitude flight

A description is given of research that applies techniques from computer vision to automation of rotorcraft navigation. The effort emphasizes the development of a methodology for detecting the ranges to obstacles in the region of interest based on the maximum utilization of passive sensors. The range map derived from the obstacle detection approach can be used as obstacle data for the obstacle avoidance in an automataic guidance system and as advisory display to the pilot. The lack of suitable flight imagery data, however, presents a problem in the verification of concepts for obstacle detection. This problem is being addressed by the development of an adequate flight database and by preprocessing of currently available flight imagery. Some comments are made on future work and how research in this area relates to the guidance of other autonomous vehicles.

Sridhar, Banavar↗