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作者： Helton, J. C Johnson, J. D Sallaberry, C. J Storlie, C. B Sandia National Labs. Albuquerque NM. Department of Energy Washington DC.

sampling-based methods for uncertainty and sensitivity analysis are reviewed. The following topics are considered: (1) Definition of probability distributions to characterize epistemic uncertainty in analysis inputs, (2) Generation of samples from uncertain analysis inputs, (3) Propagation of sampled inputs through an analysis, (4) Presentation of uncertainty analysis results, and (5) Determination of sensitivity analysis results. Special attention is given to the determination of sensitivity analysis results, with brief descriptions and illustrations given for the following procedures/techniques: examination of scatterplots, correlation analysis, regression analysis, partial correlation analysis, rank transformations, statistical tests for patterns based on gridding, entropy tests for patterns based on gridding, nonparametric regression analysis, squared rank differences/rank correlation coefficient test, two dimensional Kolmogorov-Smirnov test, tests for patterns based on distance measures, top down coefficient of concordance, and variance decomposition.

关键词： Sensitivity analysis Uncertainty Surveys sampling Data covariances Probability Regression analysis Scatterplots sampling-based methods

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Comparison of Approaches for UAV Dynamics Consideration in sampling based Path Planning methods 23

Comparison of Approaches for UAV Dynamics Consideration in S...

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23rd IEEE International Symposium on Measurement and Control in Robotics (ISMCR)

作者： Stanko, Jaromir Rodina, Jozef Hubinsky, Peter Slovak Univ Technol Bratislava Inst Robot & Cybernet Bratislava Slovakia

ISBN: (数字)9781665404792

ISBN: (纸本)9781665404792

Deployment of autonomous UAVs in an indoor environment puts higher requirements for path planning algorithms due to the more complex nature of the environment (narrow crossings, absence of GNSS, smaller manoeuvring space, etc. ...). Due to the manoeuvring of UAV between obstacles, it is required to consider its dynamics in the path planning process. Basic versions of sampling-based path planning methods such as RRT (rapidly exploring random trees) or PRM (probabilistic roadmaps) don't consider the dynamics of the object we are planning a path for. In the past several approaches for path planning with dynamic constraints were developed. The first approach, randomized kino-dynamic planning, was presented for the first time. The main goal of this approach is to incrementally search state space as configuration space and find control input, that results in a collision-free trajectory. The second approach, presented in, utilizes differential flatness of UAV's (quadrotor) equations of motions. Thus, it is possible to precisely follow the path previously generated by the path planning algorithm. The main task of this paper is to compare the mentioned approaches for path planning with kino-dynamic constraints and decide which one of them is more suitable for the path planning module of autonomous UAV that is currently being developed at the Institute of Robotics at the Slovak University of Technology.

关键词： autonomous UAV quadrotor path planning sampling-based methods RRT polynomial trajectory kino-dynamic constraints

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Path planning strategy for unmanned aerial manipulators in pick-and-place applications

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UNMANNED SYSTEMS 2025年第0期

作者： Housseyn, Zamoum Yasser, Bouzid Mohamed, Guiatni Ecole Mil Polytech Guidance & Nav Lab Algiers Algeria Ecole Mil Polytech Control Complex Syst & Simulators Lab Algiers Algeria

This paper introduces an innovative path planning strategy tailored to Unmanned Aerial Manipulation (UAM) systems for pick-and-place applications. It offers a solution for effectively coordinating the aerial platform with the manipulator robot to navigate around obstacles. This is achieved by establishing two distinct paths, one for the aerial platform and another for the manipulator robot, ensuring seamless operation and preventing collisions within the entire system. The core of this approach relies heavily on the novel Random Geometric Model (RGM) to position the aerial platform close to the target object, thus confirming that the object is within reach of its end-effector. It employs sampling-based methods and geometric models to efficiently explore the configuration space and generate collision-free path. The fundamental concept underlying this approach is to randomly generate points within the workspace of the UAM as destination objectives. These points act as guiding references for the sampling-based algorithms, simplifying the creation of connectivity graphs between the starting position and the intended destinations for the aerial manipulator. The effectiveness of this strategy is demonstrated across various scenarios, highlighting its superior performance in terms of efficiency, computation time, and path length when compared to existing path planning techniques like RRT* and Informed RRT*.

关键词： Path planning sampling-based methods pick-and-place random geometric model UAMs UAV manipulator

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Review of improved Monte Carlo methods in uncertainty-based design optimization for aerospace vehicles

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PROGRESS IN AEROSPACE SCIENCES 2016年第0期86卷 20-27页

作者： Hu, Xingzhi Chen, Xiaoqian Parks, Geoffrey T. Yao, Wen Natl Univ Def Technol Coll Aerosp Sci & Engn Changsha 410073 Hunan Peoples R China Univ Cambridge Dept Engn Cambridge CB2 1PZ England

Ever-increasing demands of uncertainty-based design, analysis, and optimization in aerospace vehicles motivate the development of Monte Carlo methods with wide adaptability and high accuracy. This paper presents a comprehensive review of typical improved Monte Carlo methods and summarizes their characteristics to aid the uncertainty-based multidisciplinary design optimization (UMDO). Among them, Bayesian inference aims to tackle the problems with the availability of prior information like measurement data. Importance sampling (IS) settles the inconvenient sampling and difficult propagation through the incorporation of an intermediate importance distribution or sequential distributions. Optimized Latin hypercube sampling (OLHS) is a stratified sampling approach to achieving better space-filling and non collapsing characteristics. Meta-modeling approximation based on Monte Carlo saves the computational cost by using cheap meta-models for the output response. All the reviewed methods are illustrated by corresponding aerospace applications, which are compared to show their techniques and usefulness in UMDO, thus providing a beneficial reference for future theoretical and applied research. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Uncertainty-based design optimization Uncertainty analysis Improved Monte Carlo sampling-based methods Aerospace vehicle

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Evaluation and recommendation of sensitivity analysis methods for application to Stochastic Human Exposure and Dose Simulation models

JOURNAL OF EXPOSURE SCIENCE AND ENVIRONMENTAL EPIDEMIOLOGY

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JOURNAL OF EXPOSURE SCIENCE AND ENVIRONMENTAL EPIDEMIOLOGY 2006年第6期16卷 491-506页

作者： Mokhtari, Amirhossein Frey, H. Christopher Zheng, Junyu N Carolina State Univ Dept Civil Construct & Environm Engn Raleigh NC 27695 USA

Sensitivity analyses of exposure or risk models can help identify the most significant factors to aid in risk management or to prioritize additional research to reduce uncertainty in the estimates. However, sensitivity analysis is challenged by non-linearity, interactions between inputs, and multiple days or time scales. Selected sensitivity analysis methods are evaluated with respect to their applicability to human exposure models with such features using a testbed. The testbed is a simplified version of a US Environmental Protection Agency's Stochastic Human Exposure and Dose Simulation (SHEDS) model. The methods evaluated include the Pearson and Spearman correlation, sample and rank regression, analysis of variance, Fourier amplitude sensitivity test (FAST), and Sobol's method. The first five methods are known as "sampling-based" techniques, wheras the latter two methods are known as "variance-based" techniques. The main objective of the test cases was to identify the main and total contributions of individual inputs to the output variance. Sobol's method and FAST directly quantified these measures of sensitivity. Results show that sensitivity of an input typically changed when evaluated under different time scales (e.g., daily versus monthly). All methods provided similar insights regarding less important inputs, however, Sobol's method and FAST provided more robust insights with respect to sensitivity of important inputs compared to the sampling-based techniques. Thus, the sampling-based methods can be used in a screening step to identify unimportant inputs, followed by application of more computationally intensive refined methods to a smaller set of inputs. The implications of time variation in sensitivity results for risk management are briefly discussed.

关键词： SHEDS models sensitivity analysis variance-based methods sampling-based methods risk assessment

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Scalable Multicore Motion Planning Using Lock-Free Concurrency

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IEEE TRANSACTIONS ON ROBOTICS 2014年第5期30卷 1123-1136页

作者： Ichnowski, Jeffrey Alterovitz, Ron Univ N Carolina Dept Comp Sci Chapel Hill NC 27599 USA

We present Parallel Rapidly Exploring Random Tree (PRRT) and Parallel RRT* (PRRT*), which are sampling-based methods for feasible and optimal motion planning designed for modern multicore CPUs. We parallelize RRT and RRT* such that all threads concurrently build a single-motion planning tree. Parallelization in this manner requires data structures, such as the nearest neighbor search tree and the motion planning tree, to be safely shared across multiple threads. Rather than relying on the traditional locks which can result in slowdowns due to lock contention, we introduce algorithms that are based on lock-free concurrency using atomic operations. We further improve scalability by using partition-based sampling (which shrinks each core's working dataset to improve cache efficiency) and parallel work-saving (in reducing the number of rewiring steps performed in PRRT*). Because PRRT and PRRT* are CPU-based, they can be directly integrated with existing libraries. In scenarios such as the Alpha Puzzle and Cubicles scenario and the Aldebaran Nao performing a two-handed task, we demonstrate that PRRT and PRRT* scale well as core counts increase, and in some cases they exhibit superlinear speedup.

关键词： Concurrent algorithms motion and path planning sampling-based methods

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NRR: a nonholonomic random replanner for navigation of car-like robots in unknown environments

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ROBOTICA 2014年第7期32卷 1101-1123页

作者： Masehian, Ellips Kakahaji, Hossein Tarbiat Modares Univ Fac Engn Tehran Iran Islamic Azad Univ Tehran Branch Sama Tech & Vocat Training Coll Tehran Iran

In this paper, a new sensor-based approach called nonholonomic random replanner (NRR) is presented for motion planning of car-like mobile robots. The robot is incrementally directed toward its destination using a nonholonomic rapidly exploring random tree (RRT) algorithm. At each iteration, the robot's perceived map of the environment is updated using sensor readings and is used for local motion planning. If the goal was not visible to the robot, an approximate path toward the goal is calculated and the robot traces it to an extent within its sensor range. The robot updates its motion to goal through replanning. This procedure is repeated until the goal lies within the scope of the robot, after which it finds a more precise path by sampling in a tighter Goal Region for the nonholonomic RRT. Three main replanning strategies are proposed to decide when to perform a visibility scan and when to replan a new path. Those are named Basic, Deliberative and Greedy strategies, which yield different paths. The NRR was also modified for motion planning of Dubin's car-like robots. The proposed algorithm is probabilistically complete and its effectiveness and efficiency were tested by running several simulations and the resulting runtimes and path lengths were compared to the basic RRT method.

关键词： Sensor-based motion planning Car-like robots Nonholonomic constraints Replanning strategy sampling-based methods

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High-Frequency Replanning Under Uncertainty Using Parallel sampling-based Motion Planning

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IEEE TRANSACTIONS ON ROBOTICS 2015年第1期31卷 104-116页

作者： Sun, Wen Patil, Sachin Alterovitz, Ron Carnegie Mellon Univ Inst Robot Pittsburgh PA 15213 USA Univ Calif Berkeley Berkeley CA 94709 USA Univ N Carolina Dept Comp Sci Chapel Hill NC 27599 USA

As sampling-based motion planners become faster, they can be reexecuted more frequently by a robot during task execution to react to uncertainty in robot motion, obstacle motion, sensing noise, and uncertainty in the robot's kinematic model. We investigate and analyze high-frequency replanning (HFR) where, during each period, fast sampling-based motion planners are executed in parallel as the robot simultaneously executes the first action of the best motion plan from the previous period. We consider discrete-time systems with stochastic nonlinear (but linearizable) dynamics and observation models with noise drawn from zero mean Gaussian distributions. The objective is to maximize the probability of success (i.e., avoid collision with obstacles and reach the goal) or to minimize path length subject to a lower bound on the probability of success. We show that, as parallel computation power increases, HFR offers asymptotic optimality for these objectives during each period for goal-oriented problems. We then demonstrate the effectiveness of HFR for holonomic and non-holonomic robots including car-like vehicles and steerable medical needles.

关键词： Motion and path planning motion planning under uncertainty sampling-based methods

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A new methodology is outlined and demonstrated on the improvement of uncertainty and sensitivity analysis based on the random sampling method

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STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 2022年第6期36卷 1703-1719页

作者： Pecha, Petr Karny, Miroslav Czech Acad Sci Dept Adapt Syst Inst Informat Theory & Automat Vvi Pod Vodarenskou Vezi 4 Prague 18200 8 Czech Republic

In several hours of a calm meteorological situation, a relatively significant level of radioactivity may accumulate around the source. When the calm situation expires, a wind-induced convective movement of the air immediately begins. Random realisations of the input atmospheric dispersion model parameters for this CALM scenario are generated using Latin Hypercube sampling scheme. The resultant complex random radiological trajectories, passing through both calm and convective stages of the release scenario, represent the necessary prerequisite for the prospective uncertainty analysis (UA) and the sensitivity analysis (SA). The novel approximation-based (AB) solution replaces the non-Gaussian sum of individual puffs at the end of the calm period with one Gaussian "super-puff" distribution. This substantially accelerates generation of a sufficiently large number of random realisations for the radiological trajectories, thus facilitating the subsequent UA and SA. Both of these procedures exploit a common mapping between the pairs of calculated output fields on the one hand and the realisation vectors of the associated random input parameters on the other hand. This paper presents the necessary technical background, as well as the idea of the AB solution and its use. Examples of 2-D random trajectories of deposited Cs-137 are presented in a graphical form. Global sensitivity analysis based on random sampling methods is outlined and improved feasibility o f the originally long-running computation is demonstrated.

关键词： Sensitivity study sampling-based methods Random input parameters Calm atmosphere Radioactivity dissemination Radioactive hot spots

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Motion planning for haptic guidance

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JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS 2008年第3期53卷 223-245页

作者： Rosell, Jan Vazquez, Carlos Perez, Alexander Iniguez, Pedro Inst Ind & Control Engn IOC UPC Barcelona Spain Dept Elect Elect & Automat Engn URV Tarragona Spain

Haptic devices allow a user to feel either reaction forces from virtual interactions or reaction forces reflected from a remote site during a bilateral teleoperation task. Also, guiding forces can be exerted to train the user in the performance of a virtual task or to assist him/her to safely teleoperate a robot. The generation of guiding forces relies on the existence of a motion plan that provides the direction to be followed to reach the goal from any free configuration of the configuration space (C-space). This paper proposes a method to obtain such a plan that interleaves a sampling-based exploration of C-space with an efficient computation of harmonic functions. A deterministic sampling sequence (with a bias based on harmonic function values) is used to obtain a hierarchical cell decomposition model of C-space. A harmonic function is iteratively computed over the partially known model using a novel approach. The harmonic function is the navigation function used as motion plan. The approach has been implemented in a planner (called the Kautham planner) that, given an initial and a goal configuration, provides: (a) a channel of cells connecting the cell that contains the initial configuration with the cell that contains the goal configuration;(b) two harmonic functions over the whole C-space, one that guides motions towards the channel and another that guides motions within the channel towards the goal;and (c) a path computed over a roadmap built with the free samples of the channel. The harmonic functions and the solution path are then used to generate the guiding forces for the haptic device. The planning approach is illustrated with examples on 2D and 3D workspaces.

关键词： haptic guidance feedback motion planning sampling-based methods deterministic sampling harmonic functions

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