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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.

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All sources NASA NTRS DOE OSTI

DOE OSTI2024

Optimization and Evaluation of Energy Savings for Connected and Autonomous Off-Road Vehicles

Off-road vehicles, such as wheel loaders, excavators, and harvesters, are extensively utilized across a wide range of industries, including construction, agriculture, and mining. These machines have become indispensable in supporting the day-to-day operational needs of a nation, playing a critical role in various sectors' infrastructure and productivity. However, despite their utility, off-road vehicles are significant consumers of fossil fuels, resulting in substantial emissions that contribute to environmental degradation. This highlights the pressing need for research and technological advancements aimed at improving their energy efficiency and reducing their carbon footprint. There are, however, two primary challenges that must be addressed to achieve these goals. First, off-road vehicles typically perform both driving and working tasks simultaneously, which introduces a high level of complexity into their overall dynamic systems. Analysis the interactions between these functions is challenging. Second, research into off-road vehicles is inherently interdisciplinary, demanding expertise across several domains such as fluid power systems, vehicle dynamics, control theory, optimization techniques, and real-world implementation. Recognizing these challenges, we proposed the project titled "Optimization and Evaluation of Energy Savings for Connected and Autonomous Off-Road Vehicles" as a comprehensive solution to enhance fuel efficiency while simultaneously improving productivity. This project specifically focuses on autonomous off-road vehicles, with particular attention to wheel loaders, and seeks to develop novel methods to optimize energy consumption without sacrificing operational performance. The project integrates real-time control algorithms, vehicle dynamics modeling, and co-optimization of powertrain system and vehicle system to achieve these goals. Our optimization strategy dynamically co-optimizes critical parameters at both the powertrain and vehicle levels, including vehicle speed, working tool movements, powertrain dynamics, and engine operations in real-time. To streamline this optimization process, we developed a vehicle model that captures the key dynamics while significantly enhancing computational efficiency. This allows the system to intelligently minimize fuel consumption, all while maintaining or even improving productivity through real-time calculations during various off-road operations. To validate the effectiveness of this energy optimization method, we introduced a state-of-the-art Hardware-in-the-Loop (HIL) testbed. This reconfigurable testbed seamlessly integrates the actual engine with virtual models of the wheel loader's subsystems, allowing for accurate emulation of real-world operational loads and environments. By simulating these conditions, the HIL testbed enables us to evaluate the wheel loader’s performance under diverse working scenarios, ensuring the developed solution is applicable in real-world operations. This testbed proved to be instrumental in validating the optimization algorithms and demonstrating the system's practical effectiveness. During the evaluation and testing phase, we employed the HIL testbed to rigorously assess the energy savings and productivity improvements generated by the optimized system. The results were highly encouraging, revealing that the automated wheel loader achieved over 30% fuel savings compared to traditional, human-operated cycles, with comparable or even enhanced levels of productivity. The insights gained from this HIL-based testing provided critical validation of our approach and highlighted the potential for deploying these optimized autonomous technologies in real-world off-road vehicles.

33 ADVANCED PROPULSION SYSTEMS↗

NASA NTRS2004

Daytime Water Detection by Fusing Multiple Cues for Autonomous Off-Road Navigation

Detecting water hazards is a significant challenge to unmanned ground vehicle autonomous off-road navigation. This paper focuses on detecting the presence of water during the daytime using color cameras. A multi-cue approach is taken. Evidence of the presence of water is generated from color, texture, and the detection of reflections in stereo range data. A rule base for fusing water cues was developed by evaluating detection results from an extensive archive of data collection imagery containing water. This software has been implemented into a run-time passive perception subsystem and tested thus far under Linux on a Pentium based processor.

COMPONENT REPORTS↗

DOE OSTI2024

Real-Time Gear-Shift Optimization for an Autonomous Wheel Loader

Off-road vehicles, such as wheel loaders, consume a significant amount of fuel during the transportation of materials. The gear-shifting process is crucial in fuel savings for transportation, and therefore, optimization of gearshifts is important for vehicle control. For an autonomous off-road vehicle, there is potential for more fuel savings by proper coordination of gearshift optimization and optimization of other control inputs. This brief proposes a new method for integrating gear-shifting into transportation optimization for an autonomous wheel loader to minimize fuel consumption. Furthermore, tests conducted on short loading cycles show that this method can save around 10%–20% of fuel on average compared with a conventional gearshift-scheduling method.

33 ADVANCED PROPULSION SYSTEMS↗

NASA NTRS2008

Terrain-Adaptive Navigation Architecture

A navigation system designed for a Mars rover has been designed to deal with rough terrain and/or potential slip when evaluating and executing paths. The system also can be used for any off-road, autonomous vehicles. The system enables vehicles to autonomously navigate different terrain challenges including dry river channel systems, putative shorelines, and gullies emanating from canyon walls. Several of the technologies within this innovation increase the navigation system s capabilities compared to earlier rover navigation algorithms.

Helmick, Daniel M.↗

NASA NTRS2005

Learning for autonomous navigation : extrapolating from underfoot to the far field

Autonomous off-road navigation of robotic ground vehicles has important applications on Earth and in space exploration. Progress in this domain has been retarded by the limited lookahead range of 3-D sensors and by the difficulty of preprogramming systems to understand the traversability of the wide variety of terrain they can encounter. Enabling robots to learn from experience may alleviate both of these problems. We define two paradigms for this, learning from 3-D geometry and learning from proprioception, and describe initial instantiations of them we have developed under DARPA and NASA programs. Field test results show promise for learning traversability of vegetated terrain, learning to extend the lookahead range of the vision system, and learning how slip varies with slope.

autonomous robots↗

NASA NTRS2005

Learning for autonomous navigation

robotics↗

NASA NTRS2003

Detecting water hazards for autonomous off-road navigation

Detecting water hazards for autonomous, off-road navigation of unmanned ground vehicles is a largely unexplored problem. In this paper, we catalog environmental variables that affect the difficulty of this problem, including day vs. night operation, whether the water reflects sky or other terrain features, the size of the water body, and other factors. We briefly survey sensors that are applicable to detecting water hazards in each of these conditions. We then present analyses and results for water detection for four specific sensor cases: 1) using color image classification to recognize sky reflections in water during the day, 2) using ladar to detect the presence of water bodies and to measure their depth, 3) using short-wave infrared (SWIR) imagery to detect water bodies, as well as snow and ice, and 4) using mid-wave infrared (MWIR) imagery to recognize water bodies at night. For color imagery, we demonstrate solid results with a classifier that runs at nearly video rate on a 433 MHz processor. For ladar, we present a detailed propagation analysis that shows the limits of water body detection and depth estimation as a function of look ahead distance, water depth, and ladar wavelength. For SWIR and MWIR, we present sample imagery from a variety of data collections that illustrate the potential of these sensors. These results demonstrate significant progress on this problem.

McHenry, Mike↗

NASA NTRS2011

Unmanned Ground Vehicle Perception Using Thermal Infrared Cameras

The ability to perform off-road autonomous navigation at any time of day or night is a requirement for some unmanned ground vehicle (UGV) programs. Because there are times when it is desirable for military UGVs to operate without emitting strong, detectable electromagnetic signals, a passive only terrain perception mode of operation is also often a requirement. Thermal infrared (TIR) cameras can be used to provide day and night passive terrain perception. TIR cameras have a detector sensitive to either mid-wave infrared (MWIR) radiation (3-5?m) or long-wave infrared (LWIR) radiation (8-12?m). With the recent emergence of high-quality uncooled LWIR cameras, TIR cameras have become viable passive perception options for some UGV programs. The Jet Propulsion Laboratory (JPL) has used a stereo pair of TIR cameras under several UGV programs to perform stereo ranging, terrain mapping, tree-trunk detection, pedestrian detection, negative obstacle detection, and water detection based on object reflections. In addition, we have evaluated stereo range data at a variety of UGV speeds, evaluated dual-band TIR classification of soil, vegetation, and rock terrain types, analyzed 24 hour water and 12 hour mud TIR imagery, and analyzed TIR imagery for hazard detection through smoke. Since TIR cameras do not currently provide the resolution available from megapixel color cameras, a UGV's daytime safe speed is often reduced when using TIR instead of color cameras. In this paper, we summarize the UGV terrain perception work JPL has performed with TIR cameras over the last decade and describe a calibration target developed by General Dynamics Robotic Systems (GDRS) for TIR cameras and other sensors.

terrain classification↗

NASA NTRS2009

Stereo Vision Based Terrain Mapping for Off-Road Autonomous Navigation

Successful off-road autonomous navigation by an unmanned ground vehicle (UGV) requires reliable perception and representation of natural terrain. While perception algorithms are used to detect driving hazards, terrain mapping algorithms are used to represent the detected hazards in a world model a UGV can use to plan safe paths. There are two primary ways to detect driving hazards with perception sensors mounted to a UGV: binary obstacle detection and traversability cost analysis. Binary obstacle detectors label terrain as either traversable or non-traversable, whereas, traversability cost analysis assigns a cost to driving over a discrete patch of terrain. In uncluttered environments where the non-obstacle terrain is equally traversable, binary obstacle detection is sufficient. However, in cluttered environments, some form of traversability cost analysis is necessary. The Jet Propulsion Laboratory (JPL) has explored both approaches using stereo vision systems. A set of binary detectors has been implemented that detect positive obstacles, negative obstacles, tree trunks, tree lines, excessive slope, low overhangs, and water bodies. A compact terrain map is built from each frame of stereo images. The mapping algorithm labels cells that contain obstacles as no-go regions, and encodes terrain elevation, terrain classification, terrain roughness, traversability cost, and a confidence value. The single frame maps are merged into a world map where temporal filtering is applied. In previous papers, we have described our perception algorithms that perform binary obstacle detection. In this paper, we summarize the terrain mapping capabilities that JPL has implemented during several UGV programs over the last decade and discuss some challenges to building terrain maps with stereo range data.

passive perception↗

NASA NTRS2006

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↗

NASA NTRS2003

Obstacle detection in foliage with ladar and radar

Autonomous off-road navigation is central to several important applications of unmanned ground vehicles. This requires the ability to detect obstacles in vegetation. We examine the prospects for doing so with scanning ladar and with a linear array of 2.2 GHz micro-impulse radar transceivers. For ladar, we summarize our work to date on algorithms for detecting obstacles in tall grass with single-axis ladar, then present a simple probabilistic model of the distance into tall grass that ladar-based obstacle detection is possible.

ladar↗

NASA NTRS2005

Learning for Autonomous Navigation

Robotic ground vehicles for outdoor applications have achieved some remarkable successes, notably in autonomous highway following (Dickmanns, 1987), planetary exploration (1), and off-road navigation on Earth (1). Nevertheless, major challenges remain to enable reliable, high-speed, autonomous navigation in a wide variety of complex, off-road terrain. 3-D perception of terrain geometry with imaging range sensors is the mainstay of off-road driving systems. However, the stopping distance at high speed exceeds the effective lookahead distance of existing range sensors. Prospects for extending the range of 3-D sensors is strongly limited by sensor physics, eye safety of lasers, and related issues. Range sensor limitations also allow vehicles to enter large cul-de-sacs even at low speed, leading to long detours. Moreover, sensing only terrain geometry fails to reveal mechanical properties of terrain that are critical to assessing its traversability, such as potential for slippage, sinkage, and the degree of compliance of potential obstacles. Rovers in the Mars Exploration Rover (MER) mission have got stuck in sand dunes and experienced significant downhill slippage in the vicinity of large rock hazards. Earth-based off-road robots today have very limited ability to discriminate traversable vegetation from non-traversable vegetation or rough ground. It is impossible today to preprogram a system with knowledge of these properties for all types of terrain and weather conditions that might be encountered.

mixture of Gaussians↗

NASA NTRS2009

Self-Supervised Learning of Terrain Traversability from Proprioceptive Sensors

Robust and reliable autonomous navigation in unstructured, off-road terrain is a critical element in making unmanned ground vehicles a reality. Existing approaches tend to rely on evaluating the traversability of terrain based on fixed parameters obtained via testing in specific environments. This results in a system that handles the terrain well that it trained in, but is unable to process terrain outside its test parameters. An adaptive system does not take the place of training, but supplements it. Whereas training imprints certain environments, an adaptive system would imprint terrain elements and the interactions amongst them, and allow the vehicle to build a map of local elements using proprioceptive sensors. Such sensors can include velocity, wheel slippage, bumper hits, and accelerometers. Data obtained by the sensors can be compared to observations from ranging sensors such as cameras and LADAR (laser detection and ranging) in order to adapt to any kind of terrain. In this way, it could sample its surroundings not only to create a map of clear space, but also of what kind of space it is and its composition. By having a set of building blocks consisting of terrain features, a vehicle can adapt to terrain that it has never seen before, and thus be robust to a changing environment. New observations could be added to its library, enabling it to infer terrain types that it wasn't trained on. This would be very useful in alien environments, where many of the physical features are known, but some are not. For example, a seemingly flat, hard plain could actually be soft sand, and the vehicle would sense the sand and avoid it automatically.

Bajracharya, Max↗

NASA NTRS2013

Real-Time and High-Fidelity Simulation Environment for Autonomous Ground Vehicle Dynamics

This paper reports on a collaborative project between U.S. Army TARDEC and Jet Propulsion Laboratory (JPL) to develop a unmanned ground vehicle (UGV) simulation model using the ROAMS vehicle modeling framework. Besides modeling the physical suspension of the vehicle, the sensing and navigation of the HMMWV vehicle are simulated. Using models of urban and off-road environments, the HMMWV simulation was tested in several ways, including navigation in an urban environment with obstacle avoidance and the performance of a lane change maneuver.

vehicle sensing↗

NASA NTRS2013

Real-Time and High-Fidelity Simulation Environment for Autonomous Ground Vehicle Dynamics

unmanned ground vehicle (UGV)↗

NASA NTRS2011

Visual SLAM Using Variance Grid Maps

An algorithm denoted Gamma-SLAM performs further processing, in real time, of preprocessed digitized images acquired by a stereoscopic pair of electronic cameras aboard an off-road robotic ground vehicle to build accurate maps of the terrain and determine the location of the vehicle with respect to the maps. Part of the name of the algorithm reflects the fact that the process of building the maps and determining the location with respect to them is denoted simultaneous localization and mapping (SLAM). Most prior real-time SLAM algorithms have been limited in applicability to (1) systems equipped with scanning laser range finders as the primary sensors in (2) indoor environments (or relatively simply structured outdoor environments). The few prior vision-based SLAM algorithms have been feature-based and not suitable for real-time applications and, hence, not suitable for autonomous navigation on irregularly structured terrain. The Gamma-SLAM algorithm incorporates two key innovations: Visual odometry (in contradistinction to wheel odometry) is used to estimate the motion of the vehicle. An elevation variance map (in contradistinction to an occupancy or an elevation map) is used to represent the terrain. The Gamma-SLAM algorithm makes use of a Rao-Blackwellized particle filter (RBPF) from Bayesian estimation theory for maintaining a distribution over poses and maps. The core idea of the RBPF approach is that the SLAM problem can be factored into two parts: (1) finding the distribution over robot trajectories, and (2) finding the map conditioned on any given trajectory. The factorization involves the use of a particle filter in which each particle encodes both a possible trajectory and a map conditioned on that trajectory. The base estimate of the trajectory is derived from visual odometry, and the map conditioned on that trajectory is a Cartesian grid of elevation variances. In comparison with traditional occupancy or elevation grid maps, the grid elevation variance maps are much better for representing the structure of vegetated or rocky terrain.

Howard, Andrew B.↗

NASA NTRS2003

Negative obstacle detection by thermal signature

Detecting negative obstacles (ditches, potholes, and other depressions) is one of the most difficult problems in perception for autonomous, off-road navigation. Past work has largely relied on range imagery, because that is based on the geometry of the obstacle, is largely insensitive to illumination variables, and because there have not been other reliable alternatives. However, the visible aspect of negative obstacles shrinks rapidly with range, making them impossible to detect in time to avoid them at high speed. To relive this problem, we show that the interiors of negative obstacles generally remain warmer than the surrounding terrain throughout the night, making thermal signature a stable property for night-time negative obstacle detection. Experimental results to date have achieved detection distances 45% greater by using thermal signature than by using range data alone. Thermal signature is the first known observable with potential to reveal a deep negative obstacle without actually seeing far into it. Modeling solar illumination has potential to extend the usefulness of thermal signature through daylight hours.

unmanned ground vehicles (UGV's)↗