SEARCH · Engineering Papers
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
This work presents a novel cooperative path planning for formation keeping robots traversing along a road with obstacles and possible narrow passages. A unique challenge in this problem is a requirement for spatial and temporal coordination between vehicles while ensuring collision and obstacle avoidance.
Human exploration of Mars will involve both crewed and robotic systems. Many mission concepts involve the deployment and assembly of mission support assets prior to crew arrival on the surface. Some of these deployment and assembly activities will be performed autonomously while others will be performed using teleoperations. However, significant communications latencies between the Earth and Mars make teleoperations challenging. Alternatively, low latency teleoperations are possible from locations in Mars orbit like Mars' moons Phobos and Deimos. To explore these latency opportunities, NASA is conducting a series of studies to investigate the effects of latency on telerobotic deployment and assembly activities. These studies are being conducted in laboratory environments at NASA's Johnson Space Center (JSC), the Human Exploration Research Analog (HERA) at JSC and the NASA Extreme Environment Mission Operations (NEEMO) underwater habitat off the coast of Florida. The studies involve two human-in-the-loop interactive simulations developed by the NASA Exploration Systems Simulations (NExSyS) team at JSC. The first simulation investigates manipulation related activities while the second simulation investigates mobility related activities. The first simulation provides a simple real-time operator interface with displays and controls for a simulated 6 degree of freedom end effector. The initial version of the simulation uses a simple control mode to decouple the robotic kinematic constraints and a communications delay to model latency effects. This provides the basis for early testing with more detailed manipulation simulations planned for the future. Subjects are tested using five operating latencies that represent teleoperation conditions from local surface operations to orbital operations at Phobos, Deimos and ultimately high Martian orbit. Subject performance is measured and correlated with three distance-to-target zones of interest. Each zone represents a target distance ranging from beyond 10m in Zone 1, through 1 cm to contact in Zone 5 with a step size factor of 10. Collected data consists of both objective simulation data (time, distance, hand controller inputs, velocity) and subjective questionnaire data. The second simulation provides a simple real-time operator interface with displays and control of a simulated surface rover. The rover traverses a synthetic Mars-like terrain and must be maneuvered to avoid obstacles while progressing to its destination. Like the manipulator simulation, subjects are tested using five operating latencies that represent teleoperation conditions from local surface operations to orbital operations at Phobos, Deimos and ultimately high Martian orbit. The rover is also operated at three different traverse speeds to assess the correlation between latency and speed. Collected data consisted of both objective simulation data (time, distance, hand controller inputs, braking) and subjective questionnaire data. These studies are exploring relationships between task complexity, operating speeds, operator efficiencies, and communications latencies for low latency teleoperations in support of human planetary exploration. This paper presents early results from these studies along with the current observations and conclusions. These and planned future studies will help to inform NASA on the potential for low latency teleoperations to support human exploration of Mars and inform the design of robotic systems and exploration missions.
Deep Neural Networks (DNNs) are considered to be key components in many autonomous systems. Applications range from vision-based obstacle avoidance to intelligent/learning control and planning. Safety-critical applications as found in the aerospace domain require that the behavior of the DNN is validated and tested rigorously for safety of the autonomous system (AUS). In this paper, we present a framework to support testing of DNNs and the analysis of the network structure. Our framework employs techniques from statistical modeling and active learning to effectively generate test cases for DNN safety testing and performance analysis. We will present results of a case study on a physics-based Deep recurrent residual neural network (DR-RNN), which has been trained to emulate the aerodynamics behavior of a fixed-wing aircraft.
To command a rover to go to a location of scientific interest on a remote planet, the rover must be capable of reliably tracking the target designated by the scientist from ten or more meters away. The rover must maintain lock on the target while traversing rough terrain and avoiding obstacles without the need for communication with Earth.
One of NASA priorities is the in-situ exploration of ocean worlds in the solar system where potentially there might be life under the ice shell. This requires reaching the ocean below extremely cold through significant deep ice. Jupiter’s moon, Europa, is such a challenging body, where it is estimated to have a 40 km thick ice shell. An approach for reaching the ocean has been conceived using a melting probe Cryobot concept that has been studied for a potential future mission. A lander is assumed to be the platform from which the Cryobot would be deployed. The ice penetrating vehicle concept consists of a cylindrical, narrow-body probe that encases a radioisotope heat/power source that would be used to do the penetration by melting through the icy crust. The baseline design of the probe includes a suite of science instruments to analyze the ice during descent and the liquid ocean underneath. For communication, a set of fiber optic wire as well as wireless RF in the very cold porous top layer is assumed, and then acoustic modules would be used for the communication in warmer denser ice over distance of 25 km between the modules. In addition to the acoustic communication modules, a sonar is part of the concept, for obstacle avoidance. The focus of this paper is on the use of elastic waves in the 1kHz range.
The Volatiles Investigation Polar Exploration Rover (VIPER) is a lunar polar volatiles prospecting mission developed through NASA’s Science Mission Directorate (SMD) Planetary Science Di-vision[1].VIPER is scheduled to land on Mons Mouton near the lunar South Pole in late 2024. VIPER’s primary mission goal is to characterize the distribution of water and volatiles across a range of thermal environments. This characterization aims to assist in understanding the origin of lunar polar volatiles and also help evaluate the In-Situ Resource Utilization (ISRU) potential of the lunar poles. The VIPER rover is a four-wheeled robotic vehicle weighing ~450 kg. It is solar-powered and teleoperated from Earth over a line-of-sight radio link. The rover can move at up to 20 cm/s on flat terrain. Accounting for commanding, localization, navigation, and obstacle-avoidance delays, however, the effective speed is closer to 1 cm/s. This effective speed is known as “Speed Made Good” (SMG), adopted from maritime culture, and kept as a key performance metric for VIPER oper-ations planning, execution, and evaluation. The mission duration is anticipated to be more than 90 Earth days and involves up to 20 km of driving. VIPER’s prospecting payload consists of spectrometers to detect volatiles and assess concentrations, context imagers, and a drill for sub-surface measurements down to one meter depth [2]. Mobility, combined with the prospecting and drill instrument suite, makes VIPER an analytically powerful resource mapper.
The Lightweight Surface Manipulation System, or LSMS, is a family of scalable long-reach cable-actuated manipulators. The design of the LSMS has a high payload ratio for efficient operations on planetary surfaces like the Moon or Mars. The LSMS has nonlinear, coupled, and hybrid dynamics. The engineering decisions that led to these challenging dynamics make this structure light and efficient. This paper proposes a novel trajectory tracking algorithm for these cranes that facilitates precise autonomous and teleoperated operations. This algorithm enables these robots to follow complex trajectories that avoid obstacles and pickup regolith in a construction site.
The Lightweight Surface Manipulation System, or LSMS, is a family of scalable long-reach cable-actuated manipulators. The design of the LSMS has a high payload ratio for efficient operations on planetary surfaces like the Moon or Mars. The LSMS has nonlinear, coupled, and hybrid dynamics. The engineering decisions that led to these challenging dynamics make this structure light and efficient. This paper proposes a novel trajectory tracking algorithm for these cranes that facilitates precise autonomous and teleoperated operations. This algorithm enables these robots to follow complex trajectories that avoid obstacles and pickup regolith in a construction site.
This research presents a simulation framework for autonomous research for a UAM vehicle using the NASA Revolutionary Vertical Lift Technology Lift+Cruise concept vehicle. Our research results were produced using the open-source, six degree of freedom, rigid-body, nonlinear generic urban air mobility (GUAM) simulation. The intent of this paper is to demonstrate the GUAM simulation and a series of Challenge Problems that our researchers have posed to the broader autonomous vehicle research community. Our team has developed the GUAM simulation for the express purpose of providing a high-fidelity transition vehicle dynamics model to foster collaboration and algorithm performance comparison across research teams. In this paper, we demonstrate some of the autonomous flight research challenges and some of our current approaches to tackling basic autonomous flight tasks (e.g., trajectory following, stationary and moving obstacle avoidance). Additionally, we propose some flight metrics to assess autonomous algorithm performance while accomplishing these basic autonomous tasks.
Understanding spatial relations of objects is critical in many robotic applications such as grasping, manipulation, and obstacle avoidance. Humans can simply reason object's spatial relations from a glimpse of a scene based on prior knowledge of spatial constraints. The proposed method enables a robot to comprehend spatial relationships among objects from RGB-D data. This paper proposed a neural-logic learning framework to learn and reason spatial relations from raw data by following logic rules on spatial constraints. The neural-logic network consists of three blocks: grounding block, spatial logic block, and inference block. The grounding block extracts high-level features from the raw sensory data. The spatial logic blocks can predicate fundamental spatial relations by training a neural network with spatial constraints. The inference block can infer complex spatial relations based on the predicated fundamental spatial relations. Simulations and robotic experiments evaluated the performance of the proposed method.
Teleautonomous guidance (TG), a technique for the remote guidance of fast mobile robots, has been developed and implemented. With TG, the mobile robot follows the general direction prescribed by an operator. However, if the robot encounters an obstacle, it autonomously avoids collision with that obstacle while trying to match the prescribed direction as closely as possible. This type of shared control is completely transparent and transfers control between teleoperation and autonomous obstacle avoidance gradually. TG allows the operator to steer vehicles and robots at high speeds and in cluttered environments, even without visual contact. TG is based on the virtual force field (VFF) method, which was developed earlier for autonomous obstacle avoidance. The VFF method is especially suited to the accommodation of inaccurate sensor data (such as that produced by ultrasonic sensors) and sensor fusion, and allows the mobile robot to travel quickly without stopping for obstacles.
This paper discusses the technologies required for automating rotorcraft nap-of-the-earth flight, where the use of natural obstacles for masking from the enemy is intentional and the danger of undesirable obstacles such as enemy traps is real. Specifically, the automatic guidance structure is modeled by three decision-making levels: the far-field mission planning and the mid-field terrain-masking trajectory shaping are both driven by prestored terrain data, whereas the nearfield obstacle detection/avoidance is driven by real-time on-board sensor data. This paper summarizes the far-field and mid-field accomplishments, and reports on the status of the more-recent efforts in obstacle detection and avoidance development. Obstacle detection is based primarily on passive imaging sensors for the desirable properties of covertness and wide field of view, although active sensors are included in the structure to provide the much needed high resolution for thin-wire detection.
The ability of a mobile robot system to plan and move intelligently in a dynamic system is needed if robots are to be useful in areas other than controlled environments. An example of a use for this system is to control an autonomous mobile robot in a space station, or other isolated area where it is hard or impossible for human life to exist for long periods of time (e.g., Mars). The system would allow the robot to be programmed to carry out the duties normally accomplished by a human being. Some of the duties that could be accomplished include operating instruments, transporting objects, and maintenance of the environment. The main focus of our early work has been on developing a fuzzy controller that takes a path and adapts it to a given environment. The robot only uses information gathered from the sensors, but retains the ability to avoid dynamically placed obstacles near and along the path. Our fuzzy logic controller is based on the following algorithm: (1) determine the desired direction of travel; (2) determine the allowed direction of travel; and (3) combine the desired and allowed directions in order to determine a direciton that is both desired and allowed. The desired direction of travel is determined by projecting ahead to a point along the path that is closer to the goal. This gives a local direction of travel for the robot and helps to avoid obstacles.
A proposed optoelectronic system, to be mounted aboard an exploratory robotic vehicle, would be used to generate a three-dimensional (3D) map of nearby terrain and obstacles for purposes of navigating the vehicle across the terrain and avoiding the obstacles. The difference between this system and the other systems would lie in the details of implementation. In this system, the illumination would be provided by a laser. The beam from the laser would pass through a two-dimensional diffraction grating, which would divide the beam into multiple beams propagating in different, fixed, known directions. These beams would form a grid of bright spots on the nearby terrain and obstacles. The centroid of each bright spot in the image would be computed. For each such spot, the combination of (1) the centroid, (2) the known direction of the light beam that produced the spot, and (3) the known baseline would constitute sufficient information for calculating the 3D position of the spot.
Trajectory, mission, and vehicle engineers concern themselves with finding the best way for an object to get from one place to another. These engineers rely upon special software to assist them in this. For a number of years, many engineers have used the OTIS program for this assistance. With OTIS, an engineer can fully optimize trajectories for airplanes, launch vehicles like the space shuttle, interplanetary spacecraft, and orbital transfer vehicles. OTIS provides four modes of operation, with each mode providing successively stronger optimization capability. The most powerful mode uses a mathematical method called implicit integration to solve what engineers and mathematicians call the optimal control problem. OTIS 3.2, which was developed at the NASA Glenn Research Center, is the latest release of this industry workhorse and features new capabilities for parameter optimization and mission design. OTIS stands for Optimal Control by Implicit Simulation, and it is implicit integration that makes OTIS so powerful at solving trajectory optimization problems. Why is this so important? The optimization process not only determines how to get from point A to point B, but it can also determine how to do this with the least amount of propellant, with the lightest starting weight, or in the fastest time possible while avoiding certain obstacles along the way. There are numerous conditions that engineers can use to define optimal, or best. OTIS provides a framework for defining the starting and ending points of the trajectory (point A and point B), the constraints on the trajectory (requirements like "avoid these regions where obstacles occur"), and what is being optimized (e.g., minimize propellant). The implicit integration method can find solutions to very complicated problems when there is not a lot of information available about what the optimal trajectory might be. The method was first developed for solving two-point boundary value problems and was adapted for use in OTIS. Implicit integration usually allows OTIS to find solutions to problems much faster than programs that use explicit integration and parametric methods. Consequently, OTIS is best suited to solving very complicated and highly constrained problems.
A multi-agent autonomous system for exploration of hazardous or inaccessible locations. The multi-agent autonomous system includes simple surface-based agents or craft controlled by an airborne tracking and command system. The airborne tracking and command system includes an instrument suite used to image an operational area and any craft deployed within the operational area. The image data is used to identify the craft, targets for exploration, and obstacles in the operational area. The tracking and command system determines paths for the surface-based craft using the identified targets and obstacles and commands the craft using simple movement commands to move through the operational area to the targets while avoiding the obstacles. Each craft includes its own instrument suite to collect information about the operational area that is transmitted back to the tracking and command system. The tracking and command system may be further coupled to a satellite system to provide additional image information about the operational area and provide operational and location commands to the tracking and command system.
Despite achievements in the remarkable photoelectrochemical (PEC) performance of photoelectrodes based on organometal halide perovskites (OHPs), the scaling up of small-scale OHP-based PEC systems to large-scale systems remains a great challenge for their practical application in solar water splitting. Significant resistive losses and intrinsic defects are major obstacles to the scaling up of OHP-based PEC systems, leading to the PEC performance degradation of large-scale OHP photoelectrodes. Herein, a scalable design of the OHP-based PEC systems by modularization of the optimized OHP photoelectrodes exhibiting a high solar-to-hydrogen conversion efficiency of 10.4% is suggested. As a proof-of-concept, the OHP-based PEC module achieves an optimal PEC performance by avoiding major obstacles in the scaling up of the OHP photoelectrodes. The constructed OHP module is composed of a total of 16 OHP photoelectrodes, and a photocurrent of 11.52 mA is achieved under natural sunlight without external bias. The successful operation of unassisted solar water splitting using the OHP module without external bias can provide insights into the design of scalable OHP-based PEC systems for future practical application and commercialization.
Surface Acoustic Waves (SAW) are prevalent in a variety of scientific fields. Typically, the ability to manipulate these surface waves has a significant impact on the efficacy of any SAW-based device. In this paper, we present a simple method for designing localized regions that have minimal SAW amplitude. These localized regions have valley-like drops in SAW amplitude and are achieved by using the spatial beat phenomenon of quasi-Rayleigh waves. Controlling the beat phenomenon allows us to manipulate where and how these localized regions appear in the waveguide. In particular, the localized regions can be designed to have variable widths and can be positioned consecutively due to minimal wave scattering. The design methodology and physics are described in detail, and several supplementary simulations are provided to demonstrate the efficacy of the proposed method. Finally, we briefly discuss how the proposed approach could potentially be exploited to implement a localized high-pass filter and enables SAW wave propagation that avoids surface obstacles.