This article presents a reliable and robust rapidly exploring random tree (R2-RRT*) algorithm to tackle challenges in mission planning of off-road autonomous ground vehicles (AGVs) under uncertain terrain environment. Two types of mobility reliability metrics, namely state mobility reliability (SMR) and mission mobility reliability (MMR), are first defined to quantify the mobility reliability of an AGV and to incorporate mobility reliability into mission planning. SMR measures the probability that a vehicle can pass through a specific location on a map of interest, whereas MMR quantifies mobility reliability of a mission path with the consideration of dependence of soil properties and slope over space. Based on the defined SMR and MMR metrics, two reliability-based robust mission planning models are developed to identify optimal paths that have robust travel time and satisfy specific reliability requirements. Moreover, a reliability-based path smoothing algorithm is developed to address the suboptimality of R2-RRT*. Results of a case study demonstrate the efficacy of the proposed models and algorithms. Note to Practitioners —This article was motivated to explicitly account for the uncertain terrain environment in mission planning of off-road autonomous ground vehicles (AGVs). Existing approaches, e.g., RRT and its variants, in general, oversimplify the uncertainty sources and overlook the reliability of vehicle mobility. These simplifications could lead to failure (i.e., immobility) of off-road AGVs on the obtained paths. This article suggests a reliability-based mission planning model by incorporating the proposed SMR and MMR metrics into mission planning considering the spatial-dependent uncertainty sources. To identify a reliable and robust path, we extend RRT* to R2-RRT* that achieves a tradeoff between mission cost and reliability. The proposed reliability-based mission planning model, however, is not limited to RRT* and can also be integrated with other path planning algorithms. It can be also applied to other unmanned vehicles and robots, such as the motion planning of unmanned aerial vehicles (UAVs) in adverse weather conditions.

The presence of various uncertainty sources in metal-based additive manufacturing (AM) process prevents producing AM products with consistently high quality. Using electron beam melting (EBM) of Ti-6Al-4V as an example, this paper presents a data-driven framework for process parameters optimization using physics-informed computer simulation models. The goal is to identify a robust manufacturing condition that allows us to constantly obtain equiaxed materials microstructures under uncertainty. To overcome the computational challenge in the robust design optimization under uncertainty, a two-level data-driven surrogate model is constructed based on the simulation data of a validated high-fidelity multiphysics AM simulation model. The robust design result, indicating a combination of low preheating temperature, low beam power, and intermediate scanning speed, was acquired enabling the repetitive production of equiaxed structure products as demonstrated by physics-based simulations. Global sensitivity analysis at the optimal design point indicates that among the studied six noise factors, specific heat capacity and grain growth activation energy have the largest impact on the microstructure variation. Through this exemplar process optimization, the current study also demonstrates the promising potential of the presented approach in facilitating other complicate AM process optimizations, such as robust designs in terms of porosity control or direct mechanical property control.

Uncertainty quantification (UQ) in metal additive manufacturing (AM) has attracted tremendous interest in order to dramatically improve product reliability. Model-based UQ, which relies on the validity of a computational model, has been widely explored as a potential substitute for the time-consuming and expensive UQ solely based on experiments. However, its adoption in the practical AM process requires overcoming two main challenges: (1) the inaccurate knowledge of uncertainty sources and (2) the intrinsic uncertainty associated with the computational model. Here, we propose a data-driven framework to tackle these two challenges by combining high throughput physical/surrogate model simulations and the AM-Bench experimental data from the National Institute of Standards and Technology (NIST). We first construct a surrogate model, based on high throughput physical simulations, for predicting the three-dimensional (3D) melt pool geometry and its uncertainty with respect to AM parameters and uncertainty sources. We then employ a sequential Bayesian calibration method to perform experimental parameter calibration and model correction to significantly improve the validity of the 3D melt pool surrogate model. The application of the calibrated melt pool model to UQ of the porosity level, an important quality factor, of AM parts, demonstrates its potential use in AM quality control. The proposed UQ framework can be generally applicable to different AM processes, representing a significant advance toward physics-based quality control of AM products.

Surrogate models play a vital role in overcoming the computational challenge in designing and analyzing nonlinear dynamic systems, especially in the presence of uncertainty. This paper presents a comparative study of different surrogate modeling techniques for nonlinear dynamic systems. Four surrogate modeling methods, namely, Gaussian process (GP) regression, a long short-term memory (LSTM) network, a convolutional neural network (CNN) with LSTM (CNN-LSTM), and a CNN with bidirectional LSTM (CNN-BLSTM), are studied and compared. All these model types can predict the future behavior of dynamic systems over long periods based on training data from relatively short periods. The multi-dimensional inputs of surrogate models are organized in a nonlinear autoregressive exogenous model (NARX) scheme to enable recursive prediction over long periods, where current predictions replace inputs from the previous time window. Three numerical examples, including one mathematical example and two nonlinear engineering analysis models, are used to compare the performance of the four surrogate modeling techniques. The results show that the GP-NARX surrogate model tends to have more stable performance than the other three deep learning (DL)-based methods for the three particular examples studied. The tuning effort of GP-NARX is also much lower than its deep learning-based counterparts.