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24th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Ramasamy, Sudha Zhang, Xiaoxiao Bennulf, Mattias Danielsson, Fredrik Univ West Dept Prod Syst Trollhattan Sweden

ISBN: (纸本)9781728103037

In this paper, a path planning algorithm is designed and tested with a real robot for a Plug & Produce demonstrator. The demonstrator is divided into modules that can be connected and removed. Modules are used for various processes like tool change and storage. This paper focuses on the process of cutting-tool change for the production industry. The Plug & Produce demonstrator uses a multi-agent system where parts and resources are agents. A part agent, e.g., a cutting-tool, can request a robot to perform skills like transportation. This requires the robot to be autonomous. The aim of this paper is to automate the path planning for industrial robotics in a Plug & Produce system. This is done by implementing a sampling based RRT algorithm combined with a collision detection function in RobotStudio. With various real time scenarios, the path planning execution time is observed and presented in the paper.

关键词： Industry 4.0 Multi-agent systems Path planning Plug & Produce rapidly-exploring random tree

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Unmanned Ship Path Planning Based on RRT 1

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14th International Conference on Intelligent Computing (ICIC)

作者： Chen, Xinjia Liu, Yanxia Hong, Xiaobin Wei, Xinyong Huang, Yesheng South China Univ Technol Sch Software Engn Guangzhou Guangdong Peoples R China South China Univ Technol Sch Mech & Automot Engn Guangzhou Guangdong Peoples R China

ISBN: (数字)9783319959306

ISBN: (纸本)9783319959290;9783319959306

Path planning is a task of primary importance for unmanned ship, but current algorithms are complex and inefficient. In this paper, we propose a rapidly-exploring random tree algorithm (RRT) for path planning of unmanned ship, which can obtain an asymptotically optimal path planning in limited time. Moreover, an extension of RRT algorithm has been proposed to overcome the actual demand of multi-waypoint path planning for unmanned ship. The feasibility and effectiveness of the proposed algorithm was proved by simulation on MATLAB (TM) platform.

关键词： Unmanned ship Path planning rapidly-exploring random tree

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A Study on Path Planning Using Bi-Directional PQ-RRT* Algorithm and Trajectory Tracking Technique Using Incremental Backstepping Control

A Study on Path Planning Using Bi-Directional PQ-RRT* Algori...

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Asia-Pacific International Symposium on Aerospace Technology (APISAT)

作者： Woo, An Jung Woo, Ji Won An, Jun-Young Kim, Chang-Joo Konkuk Univ Dept Aerosp & Informat Engn Seoul South Korea

ISBN: (纸本)9789811926358;9789811926341

An autonomous flight system is essential for effective mission performance of UAVs, which are increasingly being applied in civil and military missions. Thus, in this study, an effective approach for implementing guidance and flight control systems is proposed. Based on the rapidly-exploring random tree (RRT) algorithm, the bidirectional potential quick (PQ)-RRT* is proposed to implement the path planner of the guidance system. The proposed bidirectional PQ-RRT* algorithm has a combination of three different types of improvement methods based on RRT*: potential field guided sampling (P-RRT*), modified RRT*(Q-RRT*), and bi-directional searching tree methods (Bi-RRT*). The convergence path was efficiently optimized using the line-of-sight path optimization algorithm. Then, a flyable trajectory was generated by a 7th order spline generator with waypoints from the path planner. Finally, incremental backstepping control was adopted to ensure trajectory-tracking performance within the overall operational flight envelope. To validate a series of processes, a simulation was performed to examine the practical realization. Based on the results, the bidirectional PQ-RRT* and line-of-sight path optimization algorithms were validated to provide an effective solution. In addition, the proposed flight control system exhibited excellent trajectory-tracking performance.

关键词： Path planning Trajectory tracking Incremental backstepping control Autonomous control system rapidly-exploring random tree

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Guiding Attraction based random tree Path Planning under Uncertainty: Dedicate for UAV

Guiding Attraction based Random Tree Path Planning under Unc...

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11th IEEE International Conference on Mechatronics and Automation (ICMA)

作者： Yang, Liang Qi, Juntong Jiang, Ziya Song, Dalei Han, Jianda Xiao, Jizhong Chinese Acad Sci Shenyang Inst Automat State Key Lab Robot Shenyang 110016 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China CUNY City Coll Dept Elect Engn New York NY 10031 USA

ISBN: (纸本)9781479939794

This paper proposes a path planning algorithm called guiding attraction based random tree (GART), which is built upon the famous sampling-based algorithm RRT* to generate a near optimal path in real time for unmanned aerial vehicle (UAV) navigation under uncertainty. The algorithm takes UAV heading dynamic constraint and 'obstacle safe attraction' into consideration, and uses a descriptive set method to describe the uncertainty caused by control and sensing error. The analysis shows that the computational complexity of GART is within a constant factor of RRT* and RRT. A number of detailed comparisons of the proposed algorithm with RRT* in 2D are given which verify the efficiency of our algorithm. Moreover, 3D simulation results demonstrate that GART find the near optimal path only after 2400 iterations, which means that GART outperformed RRT* by 833%.

关键词： Guiding Attraction Sampling Based Algorithm rapidly-exploring random tree UAV Path Planning Uncertainty

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A path planning method based on improved RRT*

A path planning method based on improved RRT*

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IEEE Chinese Guidance, Navigation and Control Conference (CGNCC)

作者： Liu Yang Zhang Wei-guo Shi Jing-ping Li Guang-wen Northwestern Polytech Univ Coll Automat Xian 710129 Peoples R China

ISBN: (纸本)9781479946990

An improved rapidly-exploring random tree* (RRT*) algorithm was given, which focused on the problem of UAV path planning in 3-D dynamic environment. By considering the constraint of the UAV, the frequency of collision checking in the searching stage was reduced. And this could save searching time. D* Lite algorithm was introduced to RRT* to solve the dynamic path planning problem. The information of the length and the collision checking results on the route segments was saved to construct a road map. By using the road map, D* Lite algorithm could be more efficient. The simulation results show that the improved RRT* algorithm can find the result more quickly, and when a sudden threat arise, the algorithm can find a substituted route fast.

关键词： Path planning Unmanned aerial vehicle rapidly-exploring random tree 3-D dynamic environment

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Pokročilé plánování cesty robotu (RRT)

Pokročilé plánování cesty robotu (RRT)

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作者： Knispel, Lukáš Brno University of Technology

Tato diplomová práce práce se zabývá plánováním cesty všesměrového mobilního robotu pomocí algoritmu RRT (rapidly-exploring random tree – Rychle rostoucí n... 详细信息

Tato diplomová práce práce se zabývá plánováním cesty všesměrového mobilního robotu pomocí algoritmu RRT (rapidly-exploring random tree – Rychle rostoucí náhodný strom). Teoretická část popisuje základní algoritmy plánování cesty a prezentuje bližší pohled na RRT a jeho potenciál. Praktická část práce řeší návrh a tvorbu v zásadě multiplatformní C++ aplikace v prostředí Windows 7 za použití aplikačního frameworku Qt 4.8.0, která implementuje pokročilé RRT algoritmy s parametrizovatelným řešičem a speciálním dávkovým režimem. Tento mód slouží k testování efektivnosti nastavení řešiče pro dané úlohy a je založen na post-processingu a vizualizaci výstupu měřených úloh pomocí jazyka Python. Vypočtené cesty mohou být vylepšeny pomocí zkracovacích algoritmů a výsledná trajektorie odeslána do pohonů Maxon Compact Drive všesměrové mobilní platformy pomocí CANopen. Aplikace klade důraz na moderní grafické uživatelské rozhraní se spolehlivým a výkonným 2D grafickým engine.

关键词： plánování cesty mobilní robot Rychle rostoucí náhodný strom obousměrné prohledávání nonholonomní plánování Qt framework C++ Python Maxon MCD EPOS path planning mobile robot rapidly-exploring random tree bidirectional search nonholonomic plan

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Plánování cesty v reálném čase

Plánování cesty v reálném čase

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作者： Bartozel, Zdeněk Brno University of Technology

Práce se zabývá plánováním cesty a pohybu holonomního robota v dynamickém prostředí. Cílem práce je implementace několika algoritmů založených na rapidly-ex... 详细信息

Práce se zabývá plánováním cesty a pohybu holonomního robota v dynamickém prostředí. Cílem práce je implementace několika algoritmů založených na rapidly-explored random tree algoritmu a jejich porovnání v navrženém dynamickém prostředí.

关键词： Realtime plánování cesty rapidly-exploring random tree RRT holonomní plánování Real-time motion planing rapidly-exploring random tree RRT holonomic planing Text

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Plánování cesty robotu (RRT)

Plánování cesty robotu (RRT)

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作者： Knispel, Lukáš Brno University of Technology

Tato bakalářská práce se zabývá plánováním cesty všesměrového mobilního robotu pomocí algoritmu RRT (rapidly-exploring random tree – Rychle rostoucí ná... 详细信息

Tato bakalářská práce se zabývá plánováním cesty všesměrového mobilního robotu pomocí algoritmu RRT (rapidly-exploring random tree – Rychle rostoucí náhodný strom). V teoretické části dále popisuje základní algoritmy plánování cesty a prezentuje bližsí pohled na RRT a jeho potenciál. Praktická část práce řeší návrh a tvorbu C++ linux aplikace v prostředí Ubuntu 9.10 za použití aplikačního frameworku Qt 4.6, která implementuje pokročilý RRT algoritmus s parametrizovatelným řešičem a dávkovým režimem za účelem testování efektivnosti nastavení řešiče pro dané ú***ánování cesty, mobilní robot, Rychle rostoucí náhodný strom, Qt, C++.

关键词： Plánování cesty mobilní robot Rychle rostoucí náhodný strom Qt C++ Path planning mobile robot rapidly-exploring random tree Qt C++ Text

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Driving risk-aversive motion planning in off-road environment

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第1期216卷

作者： Tian, Hongqing Li, Boqi Huang, Heye Han, Ling Tsinghua Univ State Key Lab Automot Safety & Energy Beijing Peoples R China Univ Michigan Mech Engn Ann Arbor MI USA Changchun Univ Technol Sch Mechatron Engn Changchun Peoples R China

Incremental sampling-based motion-planning algorithms, such as the rapidly-exploring random tree (RRT), are popular in motion planning because of their ability to proffer rapid solutions. Driving safety is the ability of a vehicle to prevent overturning and collision. However, the RRT algorithms neither involve any safety mechanism, nor consider any terrain measurement information of the environment, there is no assurance on the driving safety of the solutions. Therefore, obtaining an efficient trajectory while considering driving safety is a dilemma for intelligent vehicles in unstructured off-road environments. This study presents a potential-field based RRT* motion-planning algorithm for vehicle risk aversion. The algorithm constructs a configuration space using the potential field, which identifies risk range around obstacles and off-road terrains. A random-exploring tree grows through sampling inside the potential-field space with the consideration of nonholonomic constraints of the vehicles. A cost function that trades off driving safety and efficiency is used as a priority-sequence mechanism to get an optimized planning solution. A heuristic sampling method is employed to obtain a fast solution to the initial trajectory, and the trajectory can be optimized by decreasing the cost in the following sampling process thereafter. We tested the algorithm on scenarios to verify its effectiveness. Simulation results, under different scenarios, demonstrate that the proposed algorithm improves vehicles' motion planning performance.

关键词： rapidly-exploring random tree Driving safety Potential field Motion planning Off-road environment

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Potential-Field-RRT: A Path-Planning Algorithm for UAVs Based on Potential-Field-Oriented Greedy Strategy to Extend random tree

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DRONES 2023年第5期7卷 331-331页

作者： Huang, Tai Fan, Kuangang Sun, Wen Li, Weichao Guo, Haoqi Jiangxi Univ Sci & Technol Sch Mech & Elect Engn Hongqi St 86 Ganzhou 341000 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 341000 Peoples R China Natl Rare Earth Funct Mat Innovat Ctr Huilong St 6 Ganzhou 341000 Peoples R China

This paper proposes a random tree algorithm based on a potential field oriented greedy strategy for the path planning of unmanned aerial vehicles (UAVs). Potential-field-RRT (PF-RRT) discards the defect of traditional artificial potential field (APF) algorithms that are prone to fall into local errors, and introduces potential fields as an aid to the expansion process of random trees. It reasonably triggers a greedy strategy based on the principle of field strength descending gradient optimization, accelerating the process of random tree expansion to a better region and reducing path search time. Compared with other optimization algorithms that improve the sampling method to reduce the search time of the random tree, PF-RRT takes full advantage of the potential field without limiting the arbitrariness of random tree expansion. Secondly, the path construction process is based on the principle of triangle inequality for the root node of the new node to improve the quality of the path in one iteration. Simulation experiments of the algorithm comparison show that the algorithm has the advantages of fast acquisition of high-quality initial path solutions and fast optimal convergence in the path search process. Compared with the original algorithm, obtaining the initial solution using PF-RRT can reduce the time loss by 20% to 70% and improve the path quality by about 25%. In addition, the feasibility of PF-RRT for UAV path planning is demonstrated by actual flight test experiments at the end of the experiment.

关键词： path planning rapidly-exploring random tree potential field greedy strategy root node iteration unmanned aerial vehicles

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