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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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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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Data-Driven Reachability Analysis of Digital Twin FMI Models 11th

Data-Driven Reachability Analysis of Digital Twin FMI Models

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11th International Symposium on Leveraging Applications of Formal methods, Verification and Validation

作者： Bogomolov, Sergiy Fitzgerald, John Soudjani, Sadegh Stankaitis, Paulius Newcastle Univ Sch Comp Newcastle Upon Tyne Tyne & Wear England

ISBN: (纸本)9783031197611;9783031197628

Digital Twins are an emerging technology whichmakes it possible to couple cyber-physical assets with their virtual representation in real-time. The technology is applicable to a variety of domains and facilitates a more intelligent and dependable system design and operation. In this paper, we address the challenge of analysing Digital Twins by proposing a simulation-based reachability analysis of models based on the Functional Mock-Up Interface standard. The analysis approach uses simulations to obtain the Lipschitz constant of the model which is then used to compute reachable states of the system. The approach also provides probabilistic guarantees on the accuracy of the computed reachable sets that are based on simulations of the system from random initial states.

关键词： Digital twin Functional mock-up interface Reachability analysis sampling-based methods

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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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State-space models for ecological time-series data: Practical model-fitting

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methods IN ECOLOGY AND EVOLUTION 2023年第1期14卷 26-42页

作者： Newman, Ken King, Ruth Elvira, Victor Valpine, Perry McCrea, Rachel S. Morgan, Byron J. T. Univ Edinburgh Sch Math Edinburgh Midlothian Scotland Biomath & Stat Scotland Edinburgh Midlothian Scotland Univ Calif Berkeley Dept Environm Sci Policy & Management Berkeley CA 94720 USA Univ Kent Sch Math Stat & Actuarial Sci Canterbury Kent England

State-space models are an increasingly common and important tool in the quantitative ecologists' armoury, particularly for the analysis of time-series data. This is due to both their flexibility and intuitive structure, describing the different individual processes of a complex system, thus simplifying the model specification step. State-space models are composed of two processes (a) the system (or state) process that describes the dynamics of the true underlying state of the system over time;and (b) the observation process that links the observed data with the current true state of the system at that time. Specification of the general model structure consists of considering each distinct ecological process within the system and observation processes, which are then automatically combined within the state-space structure. There is typically a trade-off between the complexity of the model and the associated model-fitting process. Simpler model specifications permit the application of simpler model-fitting tools;whereas more complex model specifications, with nonlinear dynamics and/or non-Gaussian stochasticity often require more sophisticated model-fitting algorithms to be applied. We provide a brief overview of general state-space models before focusing on the different model-fitting tools available. In particular for different general state-space model structures we discuss established model-fitting tools that are available. We also offer practical guidance for choosing a specific fitting procedure.

关键词： hidden Markov model Kalman filter Laplace approximation likelihood-free methods Markov chain Monte Carlo sampling-based methods sequential Monte Carlo

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Development of Informed Rapidly-Exploring Random Tree Focused on Memory Efficient Path Planning 4

Development of Informed Rapidly-Exploring Random Tree Focuse...

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4th International Conference on Computational Intelligence and Intelligent Systems (CIIS)

作者： Le, Zhanhao Tang, Fanyi Wang, Pengyu Wuhan Text Univ Sch Mech Engn & Automat Wuhan 430200 Peoples R China Univ British Columbia Dept Mech Engn Vancouver BC V6T 1Z4 Canada Southwest Jiaotong Univ SWJTU LEEDS Joint Sch Chengdu 611730 Peoples R China

ISBN: (纸本)9781450385930

based on Rapidly-exploring random trees (RRTs) algorithms, this work demonstrates a new algorithm, iRRT* FN. The algorithm is a modified version of Informed RRT*, using a memory-efficient planning approach. It runs identically as Informed RRT* before the nodes in the tree reach a predetermined limit of nodes. Then the algorithm releases the memory load by pruning nodes in the tree, which are irrelevant to finding the goal. We test the algorithm on 2D models compared to RRT*. Not only do iRRT* FN has a higher convergence rate finding the optimal path, but the algorithm is also proven to have a higher memory efficiency. Especially in the real-world motion planning scenario, which has a large sample space, limiting the number of nodes in the tree can immensely improve the efficiency of RRTs.

关键词： Path planning motion planning rapidly-exploring random trees sampling-based methods

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Development of Informed Rapidly-Exploring Random Tree Focused on Memory Efficient Path Planning 2021

Development of Informed Rapidly-Exploring Random Tree Focuse...

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2021 The 4th International Conference on Computational Intelligence and Intelligent Systems

作者： Zhanhao Le Fanyi Tang Pengyu Wang School of Mechanical Engineering and Automation Wuhan Textile University China Department of Mechanical Engineering University of British Columbia Canada SWJTU-LEEDS Joint School Southwest Jiaotong University China

ISBN: (纸本)9781450385930

关键词： motion planning sampling-based methods Path planning rapidly-exploring random trees

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Optimisation of the combined application planning and execution time utilising repeated PRM replanning for point-to-point sequences

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Procedia CIRP 2021年 103卷 286-291页

作者： Ivo Dekker Karel Kellens Eric Demeester KU Leuven Department of Mechanical Engineering ACRO research unit Wetenschapspark 27 3590 Diepenbeek Belgium

Motion planning and collision avoidance are well studied topics in modern industrial robotics and they are already relatively simple to adopt using packages like ROS MoveIt or Battelle PathPlan. Using probabilistic planning algorithms, collision-free trajectories can be found in as little time as hundredths of a second. However, the obtained trajectories can vary significantly for each planning request. This can result in notably longer trajectories than necessary, increasing the total time required for planning and execution. Optimisation-based motion planners such as PRM* and RRT* can find shorter paths, at the cost of a higher planning time. Our focus is put on applications that require both short planning and short trajectory execution times using existing planning tools, this with the goal of minimising the total time required for the entire application. In this paper, an easy-to-implement approach is proposed to perform path optimisation for trajectory planning applications, without altering the used planner itself, which the authors dubbed repeated PRM . This optimisation is performed by planning, selecting and eliminating trajectories based on minimal motion time during the robot’s current movement. Testing is done in a virtual environment using ROS MoveIt with a 6-DOF Stäubli TX2-90XL, utilising the OMPL PRM motion planning algorithm, eight random pose targets and eight collision objects. Tests are performed on pose sequences of one to eight poses with 30 simulations each. The average of all total times required to perform the pose sequences with the repeated PRM approach are compared to these of sequential PRM and PRM* methods. After testing, the repeated PRM method shows an average impact on the total planning and execution time with a reduction of up to 26,35 % compared to sequential PRM depending on the length of the pose sequence, and up to 57,60 % compared to sequential PRM*, as the additional computational time required for PRM* significantly increas

关键词： Motion path planning PRM sampling-based methods Industrial robotics

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A Novel RRT*-based Algorithm for Motion Planning in Dynamic Environments 14

A Novel RRT*-Based Algorithm for Motion Planning in Dynamic ...

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IEEE International Conference on Mechatronics and Automation (ICMA)

作者： Adiyatov, Olzhas Varol, Huseyin Atakan Nazarbayev Univ Dept Robot & Mechatron Astana Kazakhstan

ISBN: (纸本)9781509067596

sampling-based motion planning has become a powerful framework for solving complex robotic motion-planning tasks. Despite the introduction of a multitude of algorithms, most of these deal with the static case involving non-moving obstacles. In this work, we are extending our memory efficient RRT*FN algorithm to dynamic scenarios. Specifically, we retain the useful parts of the tree (the data structure storing the motion plan information) after a dynamic obstacle invalidates the solution path. We then employ two greedy heuristics to repair the solution instead of running the whole motion planning process from scratch. We call this new algorithm, RRT*FN Dynamic (RRT*FND). To compare our method to the state-of the-art motion planners, RRT* and RRT*FN, we conducted an extensive set of benchmark experiments in dynamic environments using two robot models: a non-holonomic mobile robot and an industrial manipulator. The results of these experiments show that RRT*FND finds the solution path in shorter time in most of the cases and verifies the efficacy of it in dynamic settings.

关键词： Path planning motion planning sampling-based methods dynamic environments rapidly-exploring random trees

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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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