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作者： Laurent Denarie INP DE TOULOUSE

学位级别：博士

The ability to design proteins with specific properties would yield great progress in pharmacology and bio-technologies. Methods to design proteins have been developed since a few decades and some relevant achievements have been made including de novo protein design. Yet, current approaches suffer some serious limitations. By not taking protein's backbone motions into account, they fail at capturing some of the properties of the candidate design and cannot guarantee that the solution will in fact be stable for the goal conformation. Besides, although multi-states design methods have been proposed, they do not guarantee that a feasible trajectory between those states exists, which means that design problem involving state transitions are out of reach of the current methods. This thesis investigates how robotics-inspired algorithms can be used to efficiently explore the conformational landscape of a protein aiming to enhance protein design methods by introducing additional backbone flexibility. This work also provides first milestones towards protein motion design.

关键词： path planning protein design structural biology robotics sampling-based algorithms computational biology

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Plánování pohybu objektu v 3D prostoru

Plánování pohybu objektu v 3D prostoru

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作者： Krčmář, Radim Brno University of Technology

V následující práci jsou předvedeny základy plánování v prostoru se zaměřením na pravděpodobnostní plánování. Oborem úzce spjatým ... 详细信息

V následující práci jsou předvedeny základy plánování v prostoru se zaměřením na pravděpodobnostní plánování. Oborem úzce spjatým je detekce kolizí, s užitím lineární algebry je vytvořen systém pro kolize objektů v libovolném počtu rozměrů. Jsou popsány základní možnosti vizualizace trojrozměrných dat. Vybrané algoritmy byly implementovány v haskellu a užity k vytažení ježka z klece.

关键词： haskell plánování pohybu pravděpodobnostní algoritmy vizualizace dat ve 3D detekce kolizí lineární algebra haskell motion planning sampling-based algorithms data visualization in 3D collision detection linear algebra Text

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Vizualizace plánování cesty

Vizualizace plánování cesty

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作者： Vozák, Petr Brno University of Technology

Tato bakalářská práce pojednává o pravděpodobnostních algoritmech pro hledání cesty holonomického (hlavně robota typu auto) a pružného robota. Obsahuje stručný ... 详细信息

Tato bakalářská práce pojednává o pravděpodobnostních algoritmech pro hledání cesty holonomického (hlavně robota typu auto) a pružného robota. Obsahuje stručný teoretický popis pravděpodobnostních algoritmů včetně diskuze nad implementačními detaily. Součástí práce jsou java applety a webové stránky věnované této problematice.

关键词： pružné objekty holonomický robot auto robotika hledání cesty pravděpodobnostní algoritmy PRM EST RRT SRT. flexible object holonomic robot car robot robotics path finding sampling-based algorithms PRM EST RRT SRT. Text

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Plánování pohybu objektu v 3D prostoru

Plánování pohybu objektu v 3D prostoru

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作者： Němec, František Brno University of Technology

Tato práce se zabývá problémem plánování pohybu objektu ve 3D prostoru. Cílem je vytvořit program, který uživateli umožní vytvořit scénu, ve které bude hled&... 详细信息

Tato práce se zabývá problémem plánování pohybu objektu ve 3D prostoru. Cílem je vytvořit program, který uživateli umožní vytvořit scénu, ve které bude hledání cesty probíhat, automaticky pak cestu vyhledá a nakonec ji bude vizualizovat. Práce je zaměřena na pravděpodobnostní algoritmy, které jsou spolu s důležitými pojmy popsány v teoretické části. V praktické části je popsán návrh a implementace aplikace. Nakonec je provedeno několik experimentů pro porovnání výkonu jednotlivých algoritmů a demonstraci funkčnosti programu.

关键词： Plánování pohybu hledání cesty 3D prostor pravděpodobnostní algoritmy PRM EST RRT R-RRT RR-RRT. Motion planning path planning 3D space probabilistic algorithms sampling-based algorithms PRM EST RRT R-RRT RR-RRT. Text

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Vizualizace hledání cesty pro robota

Vizualizace hledání cesty pro robota

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作者： Kvasnica, Miroslav Brno University of Technology

Tato bakalářská práce pojednává o pravděpodobnostních algoritmech pro hledání cesty robota. Obsahuje teoretický popis pravděpodobnostních algoritmů včetně diskuze n... 详细信息

Tato bakalářská práce pojednává o pravděpodobnostních algoritmech pro hledání cesty robota. Obsahuje teoretický popis pravděpodobnostních algoritmů včetně diskuze nad implementačními detaily, přibližuje jejich využití a popisuje jednotlivé algoritmy - PRM, EST, RRT a SRT včetně jejich dalších modifikací. Součástí práce jsou java applety znázorňující vybrané algoritmy a také webové stránky věnované této problematice.

关键词： robotika hledání cesty pravděpodobnostní metody PRM EST RRT SRT robotics path finding roadmaps sampling-based algorithms PRM EST RRT SRT Text

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Density gradient-RRT: An improved rapidly exploring random tree algorithm for UAV path planning

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EXPERT SYSTEMS WITH APPLICATIONS 2024年 252卷

作者： Huang, Tai Fan, Kuangang Sun, Wen Jiangxi Univ Sci & Technol Sch Mech & Elect Engn Hongqi St 86 Ganzhou Peoples R China Jiangxi Univ Sci & Technol Magnet Suspens Technol Key Lab Jiangxi Prov Hongqi St 86 Ganzhou 341000 Peoples R China Jiangxi Univ Sci & Technol Sch Elect Engn & Automat Hongqi St 86 Ganzhou 34100 Peoples R China

In-depth studies of algorithms for solving motion planning problems have been conducted due to the rapid popularization and development of unmanned aerial vehicles in previous decades. Among them, the classic rapidly exploring random tree (RRT) algorithm has derivative algorithms (e.g., RRT*, Q-RRT*, and F-RRT*) that focus on the optimal path cost of the initial solution. Other improved algorithms, such as RRT-connect and BG-RRT, focus on the optimal time of the initial solution. This article proposes an improved density gradient-RRT (DG-RRT) algorithm based on RRT that improves the efficiency of the guide point and reduces the time lost in the process of obtaining the initial solution through the dynamic gradient sampling strategy. Simultaneously, it reduces the path cost by reconstructing the output path. The proposed algorithm is an expansion algorithm of a random tree, and the performance of the algorithm can be further improved by combining it with other RRT optimization algorithms. DG-RRT and other algorithms are compared in different environments through simulation experiments to verify the advantages of DG-RRT. In addition, it used a set of simulation flight tests to verify the feasibility of the DG-RRT algorithm for UAV path planning.

关键词： Path planning Rapidly-exploring random tree sampling-based algorithms Dynamic density gradient sampling

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Informed scenario-based RRT* for aircraft trajectory planning under ensemble forecasting of thunderstorms

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2021年 129卷

作者： Andre, Eduardo Gonzalez-Arribas, Daniel Soler, Manuel Kamgarpour, Maryam Sanjurjo-Rivo, Manuel Univ Carlos III Dept Bioengn & Aerosp Engn Madrid Spain Univ British Columbia Elect & Comp Engn Vancouver BC Canada

Thunderstorms represent a major hazard for flights, as they compromise the safety of both the airframe and the passengers. To address trajectory planning under thunderstorms, three variants of the scenario-based rapidly exploring random trees (SB-RRTs) are proposed. During an iterative process, the so-called SB-RRT, the SB-RRT* and the Informed SB-RRT* find safe trajectories by meeting a user-defined safety threshold. Additionally, the last two techniques converge to solutions of minimum flight length. Through parallelization on graphical processing units the required computational times are reduced substantially to become compatible with near real-time operation. The proposed methods are tested considering a kinematic model of an aircraft flying between two waypoints at constant flight level and airspeed;the test scenario is based on a realistic weather forecast and assumed to be described by an ensemble of equally likely members. Lastly, the influence of the number of scenarios, safety margin and iterations on the results is analyzed. Results show that the SB-RRTs are able to find safe and, in two of the algorithms, close to-optimum solutions.

关键词： Aircraft path planning sampling-based algorithms Uncertain thunderstorm avoidance Parallel programming

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SOF-RRT*: An improved path planning algorithm using spatial offset sampling

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023年 126卷

作者： Yu, Shanen Chen, Jianke Liu, Guangyu Tong, Xiaolong Sun, Yingyi Hangzhou Dianzi Univ Sch Automat Artificial Intelligence Key Lab IoT & Informat Fus Technol Zhejiang Prov Hangzhou 310018 Peoples R China

Recently, sampling-based algorithms have demonstrated advantages in complex and high-dimensional environments. The RRT* algorithm, as the best variant of RRT, provides progressive optimality. It is shown that the RRT* algorithm has a slow convergence speed and a high initial path cost that result in the low efficiency of the algorithm, due to the low probability of adequate sampling caused by the invalid expansion of the tree and redundant sampling. To overcome these limitations, the SOF-RRT* algorithm is proposed to generate better initial solutions with higher stability and faster convergence. The SOF-RRT* algorithm is an improvement on the F-RRT* algorithm. The SOF-RRT* algorithm introduces a spatial probability weight sampling strategy into sampling, which makes the sampling probability higher in the area with a large feasible area. It reduces redundant sampling and increases the effective sampling rate. In the aspect of tree expansion, the strategy of target bias and obstacle tangential bias is introduced to improve the tree expansion efficiency, which makes tree expansion away from obstacles and bias to the target point. The proposed algorithm improves the expansion efficiency of sampling and tree. Finally, the initial path generation and convergence speed are simulated and compared, which proves that the algorithm has been greatly optimized in terms of the number of iterations, path quality, and convergence speed.

关键词： Optimal path planning Rapidly-exploring random tree (RRT) sampling-based algorithms Navigation

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A sampling-based Algorithm with the Metropolis Acceptance Criterion for Robot Motion Planning

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SENSORS 2022年第23期22卷 9203页

作者： Liu, Yiyang Zhao, Yang Yan, Shuaihua Song, Chunhe Li, Fei Chinese Acad Sci Key Lab Networked Control Syst Shenyang 110016 Peoples R China Chinese Acad Sci Shenyang Inst Automation Shenyang 110016 Peoples R China Chinese Acad Sci Inst Robot & Intelligent Mfg Shenyang 110169 Peoples R China Kunshan Intelligent Equipment Res Inst Kunshan 215300 Peoples R China Shenyang Ligong Univ Sch Automat & Elect Engn Shenyang 110159 Peoples R China Univ Chinese Acad Sci Sch Comp Sci & Technol Beijing 100049 Peoples R China Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China

Motion planning is one of the important research topics of robotics. As an improvement of Rapidly exploring Random Tree (RRT), the RRT* motion planning algorithm is widely used because of its asymptotic optimality. However, the running time of RRT* increases rapidly with the number of potential path vertices, resulting in slow convergence or even an inability to converge, which seriously reduces the performance and practical value of RRT*. To solve this issue, this paper proposes a two-phase motion planning algorithm named Metropolis RRT* (M-RRT*) based on the Metropolis acceptance criterion. First, to efficiently obtain the initial path and start the optimal path search phase earlier, an asymptotic vertex acceptance criterion is defined in the initial path estimation phase of M-RRT*. Second, to improve the convergence rate of the algorithm, a nonlinear dynamic vertex acceptance criterion is defined in the optimal path search phase, which preferentially accepts vertices that may improve the current path. The effectiveness of M-RRT* is verified by comparing it with existing algorithms through the simulation results in three test environments.

关键词： motion planning sampling-based algorithms RRT Metropolis acceptance criterion asymptotic optimality

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ATS-RRT*: an improved RRT* algorithm based on alternative paths and triangular area sampling

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ADVANCED ROBOTICS 2023年第10期37卷 605-620页

作者： Zhang, Zhi-wei Jia, Yun-wei Su, Qi-qi Chen, Xiao-tong Fu, Bang-peng Tianjin Univ Technol Tianjin Key Lab Adv Mechatron Syst Design & Intell Tianjin Peoples R China Tianjin Univ Technol Natl Demonstrat Ctr Expt Mech & Elect Engn Educ Tianjin Peoples R China Tiandy Technol Co Ltd Tianjin Peoples R China

The Rapidly Exploring Random Tree Star (RRT*) is a probabilistically complete algorithm. It is recognized as a better path planning algorithm, but its path quality and path planning speed still have room for improvement. This paper proposes an improved RRT* algorithm based on alternative paths and triangular area sampling (ATS-RRT*). The alternative paths strategy generates multiple initial paths based on whether the sample points can communicate with the target points and set the path with the smallest cost as the final initial path, which can speed up the initial path planning and improve the initial path finding rate. The triangular area sampling strategy combines every three adjacent nodes to generate some triangle areas and corresponding half-triangle areas. The path quality can be improved quickly by limiting the sampling in these triangle areas. In addition, the direct connection strategy with triangle nodes and the tabu table using in the Rewire process also speeds up the algorithm. Experiments show that the speed of finding the initial path and the success rate of finding the suboptimal path are improved by 2.3 and 1.45 times respectively compared with RRT*, Quick-RRT*, and Informed + Quick-RRT*.

关键词： RRT* path planning sampling-based algorithms direct connection strategy

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