Browse Topic: Navigation and guidance systems
LIDAR-based autonomous mobile robots (AMRs) are gradually being used for gas detection in industries. They detect tiny changes in the composition of the environment in indoor areas that is too risky for humans, making it ideal for the detection of gases. This current work focusses on the basic aspect of gas detection and avoiding unwanted accidents in industrial sectors by using an AMR with LIDAR sensor capable of autonomous navigation and MQ2 a gas detection sensor for identifying the leakages including toxic and explosive gases, and can alert the necessary personnel in real-time by using simultaneous localization and mapping (SLAM) algorithm and gas distribution mapping (GDM). GDM in accordance with SLAM algorithm directs the robot towards the leakage point immediately thereby avoiding accidents. Raspberry Pi 4 is used for efficient data processing and hardware part accomplished with PGM45775 DC motor for movements with 2D LIDAR allowing 360° mapping. The adoption of LIDAR-based AMRs
There are certain situations when landing an Advanced Air Mobility (AAM) aircraft is required to be performed without assistance from GPS data. For example, AAM aircraft flying in an urban environment with tall buildings and narrow canyons may affect the ability of the AAM aircraft to effectively use GPS to access a landing area. Incorporating a vision-based navigation method, NASA Ames has developed a novel Alternative Position, Navigation, and Timing (APNT) solution for AAM aircraft in environments where GPS is not available
ABSTRACT We present the results of an exploratory investigation of applying a hybrid quantum-classical architecture to an off-road vehicle mobility problem, namely the generation of go/no-go maps posed as a machine learning problem. The premise of this work rests on two observations. First, quantum computing allows in principle for algorithms that provide a speedup over the best known classical counterparts. However, as it is to be expected of such novel and complex tools (both hardware and algorithmic) at this early developmental stage, current quantum algorithms do not always perform well on real-world problems. Second, complex physics-based vehicle and terramechanics models and simulations, currently advocated for high-fidelity high-accuracy ground vehicle–terrain interaction analyses, pose significant computational burden, especially when applied to mobility studies which may require numerous simulation runs. We describe the Quantum-Assisted Helmholtz Machine formulation, suitable
ABSTRACT The NAUS ATO (2004-2009) was a follow-on program to the Robotic Follower ATO (2000- 2004) and built on the concept of semi-autonomous leader follower technology to achieve dynamic robotic movement in tactical formations. The NAUS ATO also developed and tested an Unmanned Ground Vehicle (UGV) Self-Security system capable of detecting, tracking, and predicting the intent of human beings in the vicinity of the vehicle. The ATO concluded its Engineering and Evaluation Testing (EET) with a capstone demonstration in October 2008. This paper will detail the technology developed and utilized under the program as well as report on the EET results to the robotic community
ABSTRACT This paper presents a new concept in GNSS navigation: Sequential Lock GPS (GPS-SL). The new concept and prototype provide a variety of advantages for robustness, solution maintenance, and jamming resistance. Under normal circumstances, GNSS receivers need to receive signals from four satellites simultaneously to get a fix on position and the receiver time bias. If three or less satellites are visible given the occlusions provided by the environment, or because someone/something is intentionally or unintentionally jamming the space, no benefit is provided to the navigation solution. In other words, four or more simultaneous satellites give you a fix, three or less simultaneous satellites usually do not contribute (with some caveats) at all
ABSTRACT Cold regions are becoming increasingly more important for off-road vehicle mobility, including autonomous navigation. Most of the time, these regions are covered by snow, and vehicles are forced to operate under active snowfall conditions. In such scenarios, realistic and effective models to predict performance of on-board sensors during snowfalls become of paramount importance. This paper describes a stochastic approach for two-dimensional numerical simulation of dynamic snow scenes that eventually will be used for driving condition visualization and vehicle sensor performance predictions. The model captures realistic snow particle size distribution, terminal near-surface particle speeds, and adequately describes interactions with wind. Citation: S. N. Vecherin, M. E. Tedesche, M. W. Parker, “Dynamic Snowfall Scene Simulations for Autonomous Vehicle Sensor Performance”, In Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium (GVSETS), NDIA, Novi, MI
ABSTRACT Localization refers to the process of estimating ones location (and often orientation) within an environment. Ground vehicle automation, which offers the potential for substantial safety and logistical benefits, requires accurate, robust localization. Current localization solutions, including GPS/INS, LIDAR, and image registration, are all inherently limited in adverse conditions. This paper presents a method of localization that is robust to most conditions that hinder existing techniques. MIT Lincoln Laboratory has developed a new class of ground penetrating radar (GPR) with a novel antenna array design that allows mapping of the subsurface domain for the purpose of localization. A vehicle driving through the mapped area uses a novel real-time correlation-based registration algorithm to estimate the location and orientation of the vehicle with respect to the subsurface map. A demonstration system has achieved localization accuracy of 2 cm. We also discuss tracking results
ABSTRACT Global Positioning System (GPS) technology has seen increased use in many different military applications worldwide, beyond navigation. The Warfighter uses GPS to enhance Situational Awareness on the battle field with systems such as Land Warrior, Blue Force Tracker, TIGR, and various electronic mission planning tools in locations where the GPS signals are normally not available. For example, this includes the inside of a HMMWV, Stryker, or MRAP. GPS retransmission, or the art of repeating a live GPS signal, has evolved into a technically advanced solution to provide GPS signals to the Warfighter mounted inside ground vehicles, protecting themselves from sniper and IED threats, while providing mobility and Situational Awareness from vehicle mounted communication & navigation systems. The objective of this technical paper is to communicate a relevant understanding of how this technology is being embraced by the Warfighter to accomplish their mission safer and more efficiently
ABSTRACT Ultra-wideband (UWB) radio ranging technology was integrated into a local positioning system (LPS) for tracking mobile robots. A practical issue was the occasional large sporadic errors in the radio range data due to multipath due to reflections and attenuation effect caused by radio penetration through mediums. In this paper, we present a filtering and system integration of the radios with vehicle sensors to produce location and orientation of a moving object being tracked. We introduced a fuzzy neighborhood filter to remove outliers from range data, a progressive trilateration filter to improve update rate and produce a fused estimate of vehicle location with a compass and wheel speed sensors. Experiments were recorded and estimated position and orientation were validated against the video recording of vehicle ground truth. The UWB LPS can be used for navigation and guidance of multiple mobile robots around a command vehicle, and employed for tracking of assets of interest
ABSTRACT The Advanced Systems Engineering Capability (ASEC) developed by TARDEC Systems Engineering & Integration (SE&I) group is an integrated Systems Engineering (SE) knowledge creation and capture framework built on a decision centric method, high quality data visualizations, intuitive navigation and systems information management that enable continuous data traceability, real time collaboration and knowledge pattern leverage to support the entire system lifecycle. The ASEC framework has evolved significantly over the past year. New tools have been added for capturing lessons learned from warfighter experiences in theater and for analyzing and validating the needs of ground domains platforms/systems. These stakeholder needs analysis tools may be used to refine the ground domain capability model (functional decomposition) and to help identify opportunities for common solutions across platforms. On-going development of ASEC will migrate all tools to a single virtual desktop to promote
ABSTRACT The VICTORY initiative has been broadly adopted across the US Defense ground vehicle community. Last year, PEO GCS generated Acquisition Decision Memorandums (ADM) guiding the Platform community to incorporate VICTORY architecture in many vehicle modernization efforts, as well as new start vehicle programs. The community can generally agree that VICTORY is driving the vehicle architecture in a positive direction, providing a much more efficient architecture to enable current, and future, technology integration. A major component of the VICTORY standards addresses the distribution of GPS-supplied information for position, heading, elevation, and timing. The vast majority of major subsystems on today’s military ground vehicles utilize GPS data in some form. These systems include fire control computers, navigation and blue force tracking equipment, ISR assets, electronic warfare devices, personal navigation equipment, laser range finders, command & control (C2) computers, UAV’s
ABSTRACT This work presents the development of a high fidelity Simulation In the Loop/Hardware In the Loop simulation environment using add-ons to Autonomous Navigation Virtual Environment Laboratory (ANVEL) and a navigation unit developed by Auburn University’s GPS and Vehicle Dynamics Lab (GAVLAB) in support of the United States Army’s Autonomous Ground Resupply Science Technology Objective. The developed add-ons include a real time interface for ANVEL, Inertial Measurement Unit module, Wheel Speed Sensor module, and a GPS module that allows simulated signals or generated Radio Frequency signals. The developed add-ons allow for faster development of navigation algorithms and controllers due to a readily available, highly accurate truth from ANVEL and can be configured to introduce realistic errors from sensors, hardware, and GPS signals such that algorithm and controller robustness can be easily examined
ABSTRACT Geotechnical site characterization is the process of collecting geophysical and geospatial characteristics about the surface and subsurface to create a 3-dimensional (3D) model. Current Robot Operating System (ROS) world models are designed primarily for navigation in unknown environments; however, they do not store the geotechnical characteristics requisite for environmental assessment, archaeology, construction engineering, or disaster response. The automotive industry is researching High Definition (HD) Maps, which contain more information and are currently being used by autonomous vehicles for ground truth localization, but they are static and primarily used for navigation in highly regulated infrastructure. Modern site characterization and HD mapping methods involve survey engineers working on-site followed by lengthy post processing. This research addresses the shortcomings for current world models and site characterization by introducing Site Model Geospatial System
ABSTRACT New generations of ground vehicles are required to perform tasks with an increased level of autonomy. Autonomous navigation and Artificial Intelligence on the edge are growing fields that require more sensors and more computational power to perform these missions. Furthermore, new sensors in the market produce better quality data at higher rates while new processors can increase substantially the computational power. Therefore, near-future ground vehicles will be equipped with large number of sensors that will produce data at rates that has not been seen before, while at the same time, data processing power will be significantly increased. This new scenario of advanced ground vehicles applications and increase in data amount and processing power, has brought new challenges with it: low determinism, excessive power needs, data losses and large response latency. In this article, a novel approach to on-board artificial intelligence (AI) is presented that is based on state-of-the
ABSTRACT Autonomous vehicles rely on path planning to guide them towards their destination. These paths are susceptible to interruption by impassable hazards detected at the local scale via on-board sensors, and malicious disruption. We define robustness as an additional parameter which can be incorporated into multi-objective optimization functions for path planning. The robustness at any point is the output of a function of the isochrone map at that point for a set travel time. The function calculates the sum of the difference in area between the isochrone map and the isochrone map with an impassable semi-circle hazard inserted in each of the four cardinal directions. We calculate and compare two different Pareto paths which use robustness as an input parameter with different weights. Citation: T. Jonsson Damgaard, M. Rittri, P. Franz, A. Halota “Robust Path Planning in the Battlefield,” In Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium (GVSETS), NDIA
ABSTRACT As part of an Internal Research and Design effort to take existing disparate technologies and integrate them into a single autonomous vehicle to advance the state-of-the-art in unmanned ground vehicle autonomy, SwRI has developed a data representation and routing algorithm to deal with the complexities of interconnecting urban roadways and the static and dynamic hazards in such an environment. The program was designed to utilize data from a Route Network Definition File (RNDF), which contains a priori roadway network data. Using its known location and a given destination, the vehicle determines the shortest route to completion. If, during traversal of that route, the vehicle detects an obstacle in its path using its on-board sensors, it will dynamically re-route its path whether that requires changing lanes on a multiple lane road or turning around completely and finding a different route if the path is completely blocked
Items per page:
50
1 – 50 of 1090