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Robust autonomous navigation in unstructured environments is an unsolved problem and critical to the operation of autonomous military and rescue ground vehicles. Two-dimensional path planners operating on occupancy grids or costs maps can produce infeasible paths when the operational area includes complex terrain. Recently, sample-based path planners that plan on LiDAR-acquired point-cloud maps have been proposed. These approaches require no discretization of the operational area and provide direct pose estimation by modeling vehicle and terrain interaction. In this paper, we show that direct sample-based path planning on point clouds is effective and robust in unstructured environments. Robustness is demonstrated by completing a system parameter sensitivity analysis of the system in an Unreal simulation environment and partnered with field validation.

Off-road vehicle operation requires constant decision-making under great uncertainty. Such decisions are multi-faceted and range from acquisition decisions to operational decisions. A major input to these decisions is terrain information in the form of soil properties. This information needs to be propagated to path planning algorithms that augment them with other inputs such as visual terrain assessment and other sensors. In this sequence of steps, many resources are needed, and it is not often clear how best to utilize them. We present an integrated approach where a mission’s overall performance is measured using a multiattribute utility function. This framework allows us to evaluate the value of acquiring terrain information and then its use in path planning. The computational effort of optimizing the vehicle path is also considered and optimized. We present our approach using the data acquired from the Keweenaw Research Center terrains and present some results.

Off road navigation demands ground robots to traverse complex and often changing terrain. Classification and assessment of terrain can improve path planning strategies by reducing travel time and energy consumption. In this paper we introduce a terrain classification and assessment framework that relies on both exteroceptive and proprioceptive sensor modalities. The robot captures an image of the terrain it is about to traverse and records corresponding vibration data during traversal. These images are manually labelled and used to train a support vector machine (SVM) in an offline training phase. Images have been captured under different lighting conditions and across multiple locations to achieve diversity and robustness to the model. Acceleration data is used to calculate statistical features that capture the roughness of the terrain whereas angular velocities are used to calculate roll and pitch angles experienced by the robot.

An objective evaluation of ground vehicle performance is a challenging task. This is further exacerbated by the increasing level of autonomy, dynamically changing the roles and capabilities of these vehicles. In the context of decision making involving these vehicles, as the capabilities of the vehicles improve, there is a concurrent change in the preferences of the decision makers operating the vehicles that must be accounted for. Decision based methods are a natural choice when multiple conflicting attributes are present, however, most of the literature focuses on static preferences. In this paper, we provide a sequential Bayesian framework to accommodate time varying preferences. The utility function is considered a stochastic function with the shape parameters themselves being random variables. In the proposed approach, initially the shape parameters model either uncertain preferences or variation in the preferences because of the presence of multiple decision makers.

With the emergence of intelligent ground vehicles, an objective evaluation of vehicle mobility has become an even more challenging task. Vehicle mobility refers to the ability of a ground vehicle to traverse from one point to another, preferably in an optimal way. Numerous techniques exist for evaluating the mobility of vehicles on paved roads, both quantitatively and qualitatively, however, capabilities to evaluate their off-road performance remains limited. Whereas a vehicle’s off-road mobility may be significantly enhanced with intelligence, it also introduces many new variables into the decision making process that must be considered. In this paper, we present a decision analytic framework to accomplish this task. In our approach, a vehicle’s mobility is modeled using an operator’s preferences over multiple mobility attributes of concern. We also provide a method to analyze various operating scenarios including the ability to mitigate uncertainty in the vehicles inputs.

The complete lifecycle of complex systems, such as ground vehicles, consists of multiple phases including design, manufacturing, operation and sustainment (O&S) and finally disposal. For many systems, the majority of the lifecycle costs are incurred during the operation and sustainment phase, specifically in the form of uncertain maintenance costs. Testing and analysis during the design phase, including reliability and supportability analysis, can have a major influence on costs during the O&S phase. However, the cost of the analysis itself must be reconciled with the expected benefits of the reduction in uncertainty. In this paper, we quantify the value of performing the tests and analyses in the design phase by treating it as imperfect information obtained to better estimate uncertain maintenance costs.

This paper presents a computational investigation of the validity of eddy current testing (ECT) for defects embedded in steel using parametrically designed defects. Of particular focus is the depths at which defects can be detected through ECT. Building on this we characterize interior defects by parametrically describing them and then examining the response fields through measurement. Thereby we seek to establish the depth and direction of detectable cracks. As a second step, we match measurements from eddy current excitations to computed fields through finite element optimization. This develops further our previously presented methods of defect characterization. Here rough contours of synthesized shapes are avoided by a novel scheme of averaging neighbor heights rather than using complex Bézier curves, constraints and such like. This avoids the jagged shapes corresponding to mathematically correct but unrealistic synthesized shapes in design and nondestructive evaluation.

This paper presents a computational framework for the physics-based simulation of light vehicles operating on discrete terrain. The focus is on characterizing through simulation the mobility of vehicles that weigh 1000 pounds or less, such as a reconnaissance robot. The terrain is considered to be deformable and is represented as a collection of bodies of spherical shape. The modeling stage relies on a novel formulation of the frictional contact problem that requires at each time step of the numerical simulation the solution of an optimization problem. The proposed computational framework, when run on ubiquitous Graphics Processing Unit (GPU) cards, allows the simulation of systems in which the terrain is represented by more than 0.5 million bodies leading to problems with more than one million degrees of freedom.

The design of vehicles increasingly challenges existing cost, weight, durability, and handling regimes. This challenge is further compounded by pressure to decrease or limit the duration of the design cycle. The simulation of vehicle dynamic behavior commonly applies just rigid, or better rigid and linear flexibility models to predict motions and determine load cases. However, as the boundaries of materials are pushed these are becoming insufficient to accurately predict behavior. Alternatively, complete nonlinear finite element representations of vehicle dynamics are always possible but are presently infeasible for the support of a single design under virtual test, not to mention several design iterations. To address these issues, a novel abstract multi-rate simulation method is outlined which is designed to exploit the richness of available model in the vehicle dynamics domain.

This paper discusses a new Department of Defense (DoD) initiative focused on the development of new generation of MBS computer software that have capabilities and features that are not provided by existing MBS software technology. This three-decade old technology fails to meet new challenges of developing more detailed models in which the effects of significant changes in geometry and large deformations cannot be ignored. New applications require accurate continuum mechanics based vehicle/soil interaction models, belt and chain drive models, efficient and accurate continuum based tire models, cable models used in rescue missions, models that accurately capture large deformations due to thermal and excessive loads, more accurate bio-mechanics models for ligaments, muscles, and soft tissues (LMST), etc.

A simple and robust path follower has been developed for use in road loads simulations as required for virtual evaluation of vehicle durability and strength. The path follower is described as simple because it has a single tunable parameter and minimal control logic. It is deemed robust because the same gain setting can be used over a wide range of terrains, paths, and speeds. Path following performance is successfully demonstrated with a multibody vehicle model of a generic heavy truck for a variety of terrains, paths, and speeds. The convergence to the desired path during parameter tuning is observed to be monotonic. A physical interpretation of the gain as a maximum centripetal acceleration further enables vehicles in the same or similar classes of mobility characteristics (all off-road trucks for example) to utilize the same gain value.

Simulation of road loads in truck suspensions generally requires leaf-spring models. This paper presents a simple and accurate leaf-spring model that can be effectively used in analytical road load simulations using ADAMS software. The model topology is based on the familiar SAE “three-link” model. The model parameters are identified from static force-deflection test data. Alternatively, the parameters can be identified from a target design specification or from analytical “tests” on a detailed finite element model. The new leaf spring model has been validated for static and dynamic performance using laboratory test data.

For powertrain mounts design and durability evaluation, directly measuring all the engine and transmission mount loads is a costly and time-consuming option. Analytical or semi-analytical approaches have been used for estimating maximum powertrain mount loads at the early design stage and also for calculating the dynamic loads when prototype test data is available. After reviewing various semi-analytical approaches or hybrid approaches using MATLAB® [1], this paper introduces a hybrid method using ADAMS® [2] with input of measured engine and transmission accelerations as well as drive shaft or half shaft loads. In this hybrid method the engine is modeled as a rigid body supported by the base structure (vehicle subframe, frame or body) through powertrain mounts with measured nonlinear properties. The method has been studied in various vehicle prototypes, in which the mount loads were measured for analysis and test correlation.

Road loads tire models are used in the automotive industry in full vehicle simulations to compute the loading from the road into the chassis encountered in proving ground durability events. Such events typically include Belgian Block events, bump events, potholes and others. Correctly capturing tire enveloping forces in such events has historically been challenging - several different approaches exist each with its own limitations. In this paper a model is presented which captures the first order tire dynamics (frequencies lower than 80 Hz) and associated enveloping loading without the need of an effective road profile. The theory behind this tire model is briefly introduced. Importantly, a comprehensive study of the validation of the tire model is given which shows correlation for full vehicle dynamic proving ground events. A Virtual Tire Lab (VTL) pre-processing tool is also presented which is used to compute tire model input parameters from a validated non-linear FEA tire model.