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A sampling-based method with virtual reality technology to provide minimum dose path navigation for occupational workers in nuclear facilities

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PROGRESS IN NUCLEAR ENERGY 2017年 100卷 22-32页

作者： Chao, Nan Liu, Yong-kuo Xia, Hong Xie, Chun-li Ayodeji, Abiodun Yang, Huan Bai, Lu Harbin Engn Univ Fundamental Sci Nucl Safety & Simulat Technol Lab Harbin 150001 Heilongjiang Peoples R China Northeast Forestry Univ Traff Coll Harbin 150040 Heilongjiang Peoples R China Atom Energy Commiss Nucl Power Plant Dev Directorate Abuja Nigeria

This paper proposes a sampling-based method (SBM) for planning minimum dose path navigation for workers in nuclear facilities. To simulate and avoid obstacles, they were approximated by a combination of basic geometries and detected with the aid of virtual reality. To obtain an optimized walking path with minimum dose without considering the pre-designed road networks, a sampling-based method that utilizes the rapidly exploring random tree star (RIZT*) algorithm, applicable in both continuous and discrete radiation fields, was proposed and implemented. The performance of this method was evaluated by simulating walk paths and estimating their radiation dose rates in a hypothetical nuclear facility, and the simulation results were compared with those derived from Dijsktra's algorithm under various radioactive environments. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Minimum dose path-planning sampling-based method Virtual reality Radiation safety

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Online trajectory planning of lithium-battery powered unmanned aerial vehicles considering electromagnetic interference

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ENGINEERING OPTIMIZATION 2025年

作者： Ge, Jiahao Xiang, Jinwu Li, Daochun Beihang Univ Sch Aeronaut Sci & Technol Beijing Peoples R China

Electromagnetic interference (EMI) of unmanned aerial vehicles (UAVs) is the main cause of magnetic compass error and flight drift. This research focuses on an online trajectory planning method considering EMI of lithium-battery powered fixed-wing UAVs. First, through analysis of EMI sources during flight, the EMI mechanism modelling and experimental verification of the main source (lithium battery) are performed, and a method for realizing self-elimination of EMI through battery inclination optimization is proposed. Secondly, under a kinodynamic RRT* framework, a specific cost function of minimizing EMI coupled with a UAV motion model is designed. Moreover, a multicriteria heuristic sampling method is proposed to improve the node quality and guarantee the planning efficiency. The proposed algorithm can not only plan a feasible trajectory online, but also effectively reduce EMI along all the generated trajectory. Comparison of the results with the hp-adaptive Radau pseudo-spectral method verifies the superiority of the method and the feasibility of online application.

关键词： Electromagnetic interference reduction electric-powered UAV mechanism modelling online trajectory planning sampling-based method

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Translational and rotational motion planning for spacecraft close proximity using sampling-based methods

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART G-JOURNAL OF AEROSPACE ENGINEERING 2019年第10期233卷 3680-3699页

作者： Zhou, Ding Yu, Zhenhua Zhang, Yanquan Li, Shunli Harbin Inst Technol Sch Astronaut Harbin 150001 Heilongjiang Peoples R China

For autonomous spacecraft close proximity under environments containing multiple obstacles and complicated constraints, incrementally rapid planning approaches stemming from sampling-based methods are investigated in this paper. Exploring planners are separately developed for the impulsive maneuvered translation and the piecewise constant controlled rotation, which, however, is constrained by the pointing limits coupling with relative positions during the proximity. Using a cost-informed parent-connecting strategy originating from dynamic programming as well as a sweeping growth fashion balanced between tree-based and graph-based methods, an asymptotically optimal unidirectional exploration method is proposed to search energy-efficient translational trajectory without collision. As for the rotation planning, the pointing constraints are taken as virtual obstacles in the state-space augmented with time horizon planned by the translation and, accordingly, a bidirectional exploration method is developed to generate constraint-satisfied slew paths with fast convergence rate. Numerical experiments indicate that the proposed sampling-based methods can rapidly return asymptotic optimal translation trajectory and rotation path satisfying collision avoidance and sensor field-of-view constraints.

关键词： Close proximity motion planning sampling-based method translation and rotation spacecraft

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Partial sample average approximation method for chance constrained problems

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OPTIMIZATION LETTERS 2019年第4期13卷 657-672页

作者： Cheng, Jianqiang Gicquel, Celine Lisser, Abdel Univ Arizona Dept Syst & Ind Engn Tucson AZ 85721 USA Univ Paris Sud XI LRI Bat 650 F-91405 Orsay France

In this paper, we present a new scheme of a sampling-based method to solve chance constrained programs. The main advantage of our approach is that the approximation problem contains only continuous variables whilst the standard sample average approximation (SAA) formulation contains binary variables. Although our approach generates new chance constraints, we show that such constraints are tractable under certain conditions. Moreover, we prove that the proposed approach has the same convergence properties as the SAA approach. Finally, numerical experiments show that the proposed approach outperforms the SAA approach on a set of tested instances.

关键词： Stochastic programming Chance constraints sampling-based method

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Impact of the homogenization level, nodal or pin-by-pin, on the uncertainty quantification with core simulators

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PROGRESS IN NUCLEAR ENERGY 2018年 104卷 218-228页

作者： Castro, E. Sanchez-Cervera, S. Garcia-Herranz, N. Cuervo, D. Univ Politecn Madrid Dept Ingn Energet C Jose Gutierrez Abascal 2 E-28006 Madrid Spain

This paper assesses the impact of the homogenization level (nodal or pin-by-pin) on the uncertainties predicted by core simulators, highlighting the need of calculations at pin level for a reliable estimate of uncertainties in hot channel factors. In order to perform the analysis, two methodologies for nuclear data uncertainty quantification in stand-alone neutronics calculations have been implemented in the core simulator COBAYA. The first one is based on first-order perturbation theory and allows sensitivity/uncertainty analysis in the multiplication factor. The second one is based on random sampling and allows the uncertainty propagation in all responses computed by the code. Both methodologies are used in conjunction with SCALE system, which provides the capabilities to propagate the nuclear data uncertainties into the few-group constants required for the diffusion calculations. These methodologies are validated using a simplified pin-cell and a fuel assembly, and then applied to a full 3D core in the context of the OECD/NEA UAM benchmark (Uncertainty Analysis in Best-Estimate Modeling for Design, Operation and Safety Analysis of LWRs).

关键词： Uncertainty quantification Nodal and pin-by-pin Core simulator Perturbation theory-based method sampling-based method

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sampling-based approach for design optimization in the presence of interval variables

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STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION 2014年第2期49卷 253-266页

作者： Yoo, David Lee, Ikjin Univ Connecticut Dept Mech Engn Storrs CT 06269 USA

This paper proposes a methodology for sampling-based design optimization in the presence of interval variables. Assuming that an accurate surrogate model is available, the proposed method first searches the worst combination of interval variables for constraints when only interval variables are present or for probabilistic constraints when both interval and random variables are present. Due to the fact that the worst combination of interval variables for probability of failure does not always coincide with that for a performance function, the proposed method directly uses the probability of failure to obtain the worst combination of interval variables when both interval and random variables are present. To calculate sensitivities of the constraints and probabilistic constraints with respect to interval variables by the sampling-based method, behavior of interval variables at the worst case is defined by the Dirac delta function. Then, Monte Carlo simulation is applied to calculate the constraints and probabilistic constraints with the worst combination of interval variables, and their sensitivities. A merit of using an MCS-based approach in the X-space is that it does not require gradients of performance functions and transformation from X-space to U-space for reliability analysis, thus there is no approximation or restriction in calculating sensitivities of constraints or probabilistic constraints. Numerical results indicate that the proposed method can search the worst case probability of failure with both efficiency and accuracy and that it can perform design optimization with mixture of random and interval variables by utilizing the worst case probability of failure search.

关键词： Interval variables sampling-based method Dirac delta function Monte Carlo simulation Surrogate model

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Overcoming Exploration: Deep Reinforcement Learning for Continuous Control in Cluttered Environments From Temporal Logic Specifications

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IEEE ROBOTICS AND AUTOMATION LETTERS 2023年第4期8卷 2158-2165页

作者： Cai, Mingyu Aasi, Erfan Belta, Calin Vasile, Cristian-Ioan Lehigh Univ Mech Engn Bethlehem PA 18015 USA Boston Univ Mech Engn Dept Boston MA 02215 USA

Model-free continuous control for robot navigation tasks using Deep Reinforcement Learning (DRL) that relies on noisy policies for exploration is sensitive to the density of rewards. In practice, robots are usually deployed in cluttered environments, containing many obstacles and narrow passageways. Designing dense effective rewards is challenging, resulting in exploration issues during training. Such a problem becomes even more serious when tasks are described using temporal logic specifications. This work presents a deep policy gradient algorithm for controlling a robot with unknown dynamics operating in a cluttered environment when the task is specified as a Linear Temporal Logic (LTL) formula. To overcome the environmental challenge of exploration during training, we propose a novel path planning-guided reward scheme by integrating sampling-based methods to effectively complete goal-reaching missions. To facilitate LTL satisfaction, our approach decomposes the LTL mission into sub-goal-reaching tasks that are solved in a distributed manner. Our framework is shown to significantly improve performance (effectiveness, efficiency) and exploration of robots tasked with complex missions in large-scale cluttered environments.

关键词： Formal methods in robotics and automation deep reinforcement learning sampling-based method

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Application and Comparison of Uncertainty Quantification methods for Railway Vehicle Dynamics with Random Mechanical Parameters

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MECHANIKA 2019年第6期25卷 455-462页

作者： Zhang, Dawei Xu, Peijuan Bigoni, Daniele Changan Univ Sch Automobile Xian 710064 Shaanxi Peoples R China Changan Univ Sch Highway Affiliat Xian 710064 Shaanxi Peoples R China MIT Dept Aeronaut & Astronaut Cambridge MA 02139 USA

This paper aims to investigate uncertainties in railway vehicle suspension components and the implement of uncertainty quantification methods in railway vehicle dynamics The sampling-based method represented by Latin Hypercube sampling (LHS) and generalized polynomial chaos approaches including the stochastic Galerkin and Collocation methods (SGM and SCM) are employed to analyze the propagation of uncertainties from the parameters input in a vehicle-track mathematical model to the results of running dynamics In order to illustrate the performance qualities of SGM, SCM and LHS, a stochastic wheel model with uncertainties of the stiffness and damping is firstly formulated to study the vertical displacement of wheel. Numerical results show that SCM, which can be easily implemented by means of the existing deterministic model, has explicit advantages over SGM and LHS in terms of the efficiency and accuracy. Furthermore, a simplified stochastic bogie model with three random suspension parameters is also established by means of SCM and LHS to analyze the critical speed, which is affected obviously by the parametric uncertainties Finally, a stochastic vertical vehicle-track coupled model with parametric uncertainties is built comprehensively on the basis of SCM, by which the dynamic response of vehicles under the track irregularity is explored in terms of the Sperling index. It concludes that the uncertainties of parameters have a significant influence on Sperling index from the view of the running quality.

关键词： uncertainty quantification railway vehicle dynamics sampling-based method polynomial chaos

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DL-RRT* algorithm for least dose path Re-planning in dynamic radioactive environments

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NUCLEAR ENGINEERING AND TECHNOLOGY 2019年第3期51卷 825-836页

作者： Chao, Nan Liu, Yong-kuo Xia, Hong Peng, Min-jun Ayodeji, Abiodun Harbin Engn Univ Fundamental Sci Nucl Safety & Simulat Technol Lab Harbin 150001 Heilongjiang Peoples R China Nigeria Atom Energy Commiss Nucl Power Plant Dev Directorate Abuja Nigeria

One of the most challenging safety precautions for workers in dynamic, radioactive environments is avoiding radiation sources and sustaining low exposure. This paper presents a sampling-based algorithm, DL-RRT*, for minimum dose walk-path re-planning in radioactive environments, expedient for occupational workers in nuclear facilities to avoid unnecessary radiation exposure. The method combines the principle of random tree star (RRT*) and D* Lite, and uses the expansion strength of grid search strategy from D* Lite to quickly find a high-quality initial path to accelerate convergence rate in RRT*. The algorithm inherits probabilistic completeness and asymptotic optimality from RRT* to refine the existing paths continually by sampling the search-graph obtained from the grid search process. It can not only be applied to continuous cost spaces, but also make full use of the last planning information to avoid global re-planning, so as to improve the efficiency of path planning in frequently changing environments. The effectiveness and superiority of the proposed method was verified by simulating radiation field under varying obstacles and radioactive environments, and the results were compared with RRT* algorithm output. (C) 2018 Korean Nuclear Society, Published by Elsevier Korea LLC.

关键词： Minimum dose path planning sampling-based method Dynamic environments Nuclear facilities

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A nonparametric Bayesian prediction interval for a finite population mean

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JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION 2016年第16期86卷 3141-3157页

作者： Nandram, Balgobin Yin, Jiani Worcester Polytech Inst Dept Math Sci Worcester MA 01609 USA

Given a sample from a finite population, we provide a nonparametric Bayesian prediction interval for a finite population mean when a standard normal assumption may be tenuous. We will do so using a Dirichlet process (DP), a nonparametric Bayesian procedure which is currently receiving much attention. An asymptotic Bayesian prediction interval is well known but it does not incorporate all the features of the DP. We show how to compute the exact prediction interval under the full Bayesian DP model. However, under the DP, when the population size is much larger than the sample size, the computational task becomes expensive. Therefore, for simplicity one might still want to consider useful and accurate approximations to the prediction interval. For this purpose, we provide a Bayesian procedure which approximates the distribution using the exchangeability property (correlation) of the DP together with normality. We compare the exact interval and our approximate interval with three standard intervals, namely the design-based interval under simple random sampling, an empirical Bayes interval and a moment-based interval which uses the mean and variance under the DP. However, these latter three intervals do not fully utilize the posterior distribution of the finite population mean under the DP. Using several numerical examples and a simulation study we show that our approximate Bayesian interval is a good competitor to the exact Bayesian interval for different combinations of sample sizes and population sizes.

关键词： Correlation exchangeability nonparametric procedure normality Polya posterior sampling-based method

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