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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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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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Hybrid sampling-based Path Planning for Mobile Manipulators Performing Pick and Place Tasks in Narrow Spaces

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APPLIED SCIENCES-BASEL 2024年第22期14卷 10313页

作者： Chen, Hanlin Zang, Xizhe Zhu, Yanhe Zhao, Jie Harbin Inst Technol State Key Lab Robot & Syst Harbin 150001 Peoples R China

A mobile manipulator is capable of traversing a vast area while performing manipulation tasks in confined spaces. However, the high degree of freedom presents a challenge for path planning. In this paper, a hybrid sampling-based path planning method is proposed for mobile manipulators performing pick and place tasks in confined spaces. This method employs a random sampling approach, yet differs from the traditional RRT method. Firstly, a sampling-based configuration generation method for mobile manipulators is proposed, with the objective of generating a valid, collision-free configuration with the end-effector at the desired pose. A path for the end-effector corresponding to the goal configuration is then planned using the RRT method. Secondly, an area-restricted approach that samples in the vicinity of the previous configuration is introduced to generate the next valid configuration. Subsequently, a cost computation rule is devised to identify the optimal subsequent configuration utilizing the trajectory of the end-effector as a guiding principle. Finally, the obtained path is smoothed. Simulations demonstrate that the proposed hybrid sample-based method is an effective solution to the path planning problem for mobile manipulators performing pick and place tasks in narrow spaces.

关键词： mobile manipulator motion planning collision avoidance sampling-based method

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A Wind Power Probabilistic Model Using the Reflection method and Multi-Kernel Function Kernel Density Estimation

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ENERGIES 2022年第24期15卷 9436-9436页

作者： Choi, Juseung Eom, Hoyong Baek, Seung-Mook Kongju Natl Univ Inst IT Convergence Technol Dept Elect & Control Engn Cheonan 31080 South Korea

This paper proposes a wind power probabilistic model (WPPM) using the reflection method and multi-kernel function kernel density estimation (KDE). With the increasing penetration of renewable energy sources (RESs) into power systems, several probabilistic approaches have been introduced to assess the impact of RESs on the power system. A probabilistic approach requires a wind power scenario (WPS), and the WPS is generated from the WPPM. Previously, WPPM was generated using a parametric density estimation, and it had limitations in reflecting the characteristics of wind power data (WPD) due to a boundary bias problem. The paper proposes a WPPM generated using the KDE, which is a non-parametric method. Additionally, the paper proposes a reflection method correcting for the boundary bias problem caused by the double-bounded characteristic of the WPD and the multi-kernel function KDE minimizing the effect of tied values. Six bandwidth selectors are used to calculate the bandwidth for the KDE, and one is selected by analyzing the correlation between the normalized WPD and the calculated bandwidth. The results were validated by generating WPPMs with WPDs in six regions of the Republic of Korea, and it was confirmed that the accuracy and goodness-of-fit are improved when the proposed method is used.

关键词： bandwidth selection kernel density estimation probabilistic model sampling-based method scenario generation wind power output

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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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Real-time Local Path Planning for Intelligent Vehicle combining Tentacle Algorithm and B-spline Curve 6th

Real-time Local Path Planning for Intelligent Vehicle combin...

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6th IFAC Conference on Engine Powertrain Control, Simulation and Modeling (E-COSM)

作者： Li, Zhouren Xiong, Lu Zeng, Dequan Fu, Zhiqiang Leng, Bo Shan, Fengwu Tongji Univ Sch Automot Studies Shanghai 201804 Peoples R China Tongji Univ Clean Energy Automot Engn Ctr Shanghai 201804 Peoples R China Jiangxi Jiangling Motors Grp New Energy Vehicle Corp Nanchang Jiangxi Peoples R China

The obstacle-avoidance problem of intelligent vehicles is one of the challenges that we face in path planning In order to tackle it, this paper proposes a real-time path planning approach based on tentacle algorithm and B-spline curve. In this approach, firstly, some virtual tentacles are built to represent the pre-calculated paths of the ego vehicle at a current speed. Secondly, it selects the best tentacle path among them, which is required to provide a safe driving direction and sampling area for generating B-spline paths. When the vehicle drives along the best tentacle path, the B-spline path is generated according to the sampling area. Finally, the designed path is formed by segments of the best tentacle and a B-spline curve. Compared with other sampling-based path sets approaches, using the proposed approach needs shorter reaction time. Simulation and experiment results verify the real-time performance and effectiveness of the algorithm of local path planning Copyright (C) 2021 The Authors.

关键词： Path planning Intelligent vehicle B-spline curves sampling-based method

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An efficient direct evaluation of reliability for slopes using low-discrepancy sampling

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BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT 2022年第12期81卷 492-492页

作者： Hu, Chao Lei, Ruide Berto, Filippo Sichuan Univ Sci & Engn Sch Civil Engn Zigong 643000 Peoples R China Chongqing Inst Geol & Mineral Resources Natl & Local Joint Engn Res Ctr Shale Gas Explora Chongqing 4001120 Peoples R China Norwegian Univ Sci & Technol Dept Mech Engn N-7491 Trondheim Norway

In this paper, to improve the computational efficiency of the reliability methods in evaluating the stability of slopes based on the uncertainty of soil properties, an efficient direct simulation approach using low-discrepancy sampling is proposed. Low-discrepancy sampling is used to generate samples, and the corresponding response values of these samples are estimated using parallel computing method. The proposed approach takes into account the inherent law between the soil strength parameters and a slope's stability by using the deterministic models for the slope stability analysis, which are used to reduce the computational cost of the samples. The results of four examples show that the proposed approach can meet the accuracy requirements and can efficiently improve the computational efficiency. Compared with a conventional Monte Carlo simulation, the number of executing performance functions is reduced by 90%. The proposed approach proves its capabilities and can serve as a good tool for the slope reliability methods.

关键词： Slope reliability Low-discrepancy sampling sampling-based method Failure probability

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FUAS: An uncertainty and sensitivity analysis toolkit for fuel rod performance code

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ANNALS OF NUCLEAR ENERGY 2022年 173卷

作者： Wei, Jun Wang, Yang Song, Zi-Fan Wang, Jie Liu, Xin Deng, Yong-Jun China Nucl Power Technol Res Inst Co Ltd Shang Bu Middle Rd Shenzhen 518031 Guangdong Peoples R China

Fuel rod performance codes are used to predict the behavior of fuel rods in reactors. The uncertainty and sensitivity analysis are always needed when developing or using these codes. However, fuel rod codes typically have two characteristics that make relevant analysis challenging: First, there are many input parameters for which uncertainty needs to be considered. Second, the structure of the models is complex, non-linear and non-additive. To make the uncertainty and sensitivity analysis of a fuel rod code effectively and conveniently, an assistant toolkit, FUAS, has been developed recently by China Nuclear Power Technology Research Institute Co. Ltd. FUAS considers the fuel rod codes as black boxes and applies sampling-based approaches. Several statistical methods are available in this toolkit. A hypothetical steady-state scenario of the PWR fuel rod was simulated, and uncertainty and sensitivity analysis were performed with the sampling-based approach by coupling the fuel rod code and FUAS toolkit. (C) 2022 Elsevier Ltd. All rights reserved.

关键词： Uncertainty analysis Sensitivity analysis Fuel rod performance code sampling-based method Monte Carlo method

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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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