Browse Topic: Terrain
ABSTRACT One primary system integration challenge for a terrain measurement system is the triggering and time synchronization of all subsystems. Since individual measurement systems vary in their triggering requirements, both in terms of voltage levels and response times, a comprehensive triggering architecture is difficult to implement. Examples of triggering signal inputs include: a transistor-transistor logic (TTL) compliant signal, an RS-232 compliant signal, and an open/close switch circuit. Pulse-triggering signals are also present, and enable continuous time synchronization between instruments. Therefore, a triggering scheme is proposed capable of accurately initiating, synchronizing, and concluding data collection from multiple sensors and subsystems. Simulation of complete circuit designs show that the trigger circuit is capable of properly processing a single physical switch input signal into a TTL-compliant trigger signal. Synchronization pulse signals are likewise amplified
ABSTRACT When building simulation models of military vehicles for mobility analysis over deformable terrain, the powertrain details are often ignored. This is of interest for electric and hybrid-electric vehicles where the maximum torque is produced at low speeds. It is easy to end up with the drive wheels spinning and reducing traction and eventually the vehicle digging itself down in the soil. This paper reveals improvements to mobility results using Traction Control Systems for both wheeled and tracked vehicles. Simulations are performed on hard ground and two types of deformable soil, Lethe sand and snow. For each soft soil, simulations have been performed with a simple terramechanics model (ST) based on Bekker-Wong models and complex terramechanics (CT) using the EDEM discrete element soil model which Pratt & Miller Engineering (PME) has been instrumental in developing. To model the traction control system a PD controller is used that tries to limit the slip velocity at low speed
ABSTRACT This paper presents a new terrain traversability mapping method integrated into the Robotic Technology Kernel (RTK) that produces ground slope traversability cost information from LiDAR height maps. These ground slope maps are robust to a variety of off-road scenarios including areas of sparse or dense vegetation. A few simple and computationally efficient heuristics are applied to the ground slope maps to produce cost data that can be directly consumed by existing path planners in RTK, improving the navigation performance in the presence of steep terrain. Citation: J. Ramsey, R. Brothers, J. Hernandez, “Creation of a Ground Slope Mapping Methodology Within the Robotic Technology Kernel for Improved Navigation Performance,” In Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium (GVSETS), NDIA, Novi, MI, Aug. 16-18, 2022
ABSTRACT Future autonomous combat vehicles will need to travel off-road through poorly mapped environments. Three-dimensional topography may be known only to a limited extent (e.g. coarse height), but this will likely be noisy and of limited resolution. For ground vehicles, 3D topography will impact how far ahead the vehicle can “see”. Higher vantage points and clear views provide much more useful path planning data than lower vantage points and occluded views from trees and structures. The challenge is incorporating this knowledge into a path planning solution. When should the robot climb higher to get a better view or else continue moving along the shortest path predicted by current information? We investigated the use of Deep Q-Networks (DQN) to reason over this decision space, comparing performance to conventional methods. In the presence of significant sensor noise, the DQN was more successful in finding a path to the target than A* for all but one type of terrain. Citation: E
ABSTRACT To realize the full potential of simulation-based evaluation and validation of autonomous ground vehicle systems, the next generation of modeling and simulation (M&S) solutions must provide real-time closed-loop environments that feature the latest physics-based modeling approaches and simulation solvers. Real-time capabilities enable seamless integration of human-in/on-the-loop training and hardware-in-the-loop evaluation and validation studies. Using an open modular architecture to close the loop between the physics-based solvers and autonomy stack components allows for full simulation of unmanned ground vehicles (UGVs) for comprehensive development, training, and testing of artificial intelligence vehicle-based agents and their human team members. This paper presents an introduction to a Proof of Concept for such a UGV M&S solution for severe terrain environments with a discussion of simulation results and future research directions. This conceptual approach features: 1
ABSTRACT To advance development of the off-road autonomous vehicle technology, software simulations are often used as virtual testbeds for vehicle operation. However, this approach requires realistic simulations of natural conditions, which is quite challenging. Specifically, adverse driving conditions, such as snow and ice, are notoriously difficult to simulate realistically. The snow simulations are important for two reasons. One is mechanical properties of snow, which are important for vehicle-snow interactions and estimation of route drivability. The second one is simulation of sensor responses from a snow surface, which plays a major role in terrain classification and depends on snow texture. The presented work describes an overview of several approaches for realistic simulation of snow surface texture. The results indicate that the overall best approach is the one based on the Wiener–Khinchin theorem, while an alternative approach based on the Cholesky decomposition is the second
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 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 Durability analysis as applied to high mobility off-road ground vehicles involves simulating the vehicle on rough terrains and cascading the loads throughout the structure to support the verification of various components. For components within the hull structure, the rigid body accelerations of the hull are transformed to the component location producing a prescribed g-load time history. This modeling method works extremely well for items which are bolted in place but is inappropriate for stowage systems such as boxes and shelves where cargo can experience intermittent contact and impacts. One solution is to create a dynamic contact nonlinear finite element model of the stowage solution with supported cargo and subject them to the same acceleration profile. This approach effectively resolves the stresses needed to perform fatigue evaluations but is a computationally and labor intensive process. The resources required for single design point verification cannot be justified
ABSTRACT A framework for generation of reliability-based stochastic off-road mobility maps is developed to support the Next Generation NATO Reference Mobility Model (NG-NRMM) using full stochastic knowledge of terrain properties and modern complex terramechanics modelling and simulation capabilities. The framework is for carrying out uncertainty quantification and reliability assessment for Speed Made Good and GO/NO-GO decisions for the ground vehicle based on the input variability models of the terrain elevation and soil property parameters. To generate the distribution of the slope at given point, realizations of the elevation raster are generated using the normal distribution. For the soil property parameters, such as cohesion, friction and bulk density, the min and max values obtained from geotechnical databases for each of the soil types are used to generate the normal distribution with a 99% confidence value range. In the framework, the ranges of terramechanics input parameters
ABSTRACT A new integrated testing system for the validation of stochastic vehicle-snow interaction models is presented in this paper. The testing system consists of an instrumented test vehicle, vehicle-mounted laser profilometer and a snow micropenetrometer. The test vehicle is equipped on each tire with a set of 6-axis wheel transducers, and a GPS-based data logger tracks vehicle motion. Data is also simultaneously acquired from the sensors from the test vehicle’s Electronic Stability Program. The test vehicle provides measurements that include three forces and moments at each wheel center, vehicle body slip angle, speed, acceleration, yaw rate, roll, and pitch. The profilometer has a 3-D scanning laser and an Inertial Measurement Unit to compensate for vehicle motion. Depth of snow cover, profile of snow surface and wheel sinkage can be obtained from the profilometer. The snow micropenetrometer measures the strength of the snow cover before and after vehicle traversal. Preliminary
ABSTRACT Robotics makers and application engineers stand to benefit from replacing physical simulation with a digital simulation that can easily represent any number of robots on a terrain and provide ground truth data for comparison with sensor data during analysis. In this research, a digital proxy simulation (DPS) was developed to dynamically simulate any number of articulated robots in real-time using sophisticated robot-environment interaction models. 3D models of the robot and environment objects can be imported or placed conveniently. Parameters of the models can be fine-tuned to mimic the environment with high fidelity. Sensor simulation and control capabilities of the DPS are also highlighted. Common sensors can be simulated including lidar, image sensors, and stereo cameras. Control plugins can be added easily to accomplish complex tasks
ABSTRACT This paper discusses the semi-active suspension system developed by A.M. General to provide mobility and maneuverability for tactical, wheeled vehicles
ABSTRACT GS Engineering has developed technology to advance the sensory perception of autonomous systems. The Automatic Terrain Detection System (ATDS) is a technology that provides real time terrain detection. Vehicles deployed with ATDS have been able to yield improved mobility, automation of systems, and reduced fuel consumption. ATDS has been integrated into the MK23 MTVR, M1151 HMMWV for the ONR Predictive Adaptive Mobility (PAM) program, and into the Autonomous Ground Re-supply (AGR) by-wire kit for the Oshkosh Defense Palletized Load System (PLS). The ATDS is built upon proven sensors running integrated processing to replace or enhance existing vehicle systems. Citation: D. Subert, A. Diepen, K. Hubert, “Automatic Terrain Detection”, In Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium (GVSETS), NDIA, Novi, MI, Aug. 13-15, 2019
ABSTRACT Off-road autonomous navigation poses a challenging problem, as the surrounding terrain is usually unknown, the support surface the vehicle must traverse cannot be considered flat, and environmental features (such as vegetation and water) make it difficult to estimate the support surface elevation. This paper will focus on Robotic Research’s suite of off-road autonomous planning and obstacle avoidance tools. Specifically, this paper will provide an overview of our terrain detection system, which utilizes advanced LADAR processing techniques to provide an estimate of the surface. Additionally, it will describe the kino-dynamic off-road planner which can, in real-time, calculate the optimal route, taking into account the support surface, obstacles sensed in the environment, and more. Finally, the paper will explore how these technologies have been applied to a wide variety of different robotic applications
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