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Feedback RRT* algorithm for UAV path planning in a hostile environment

COMPUTERS & INDUSTRIAL ENGINEERING 2022年 174卷

作者： Guo, Jun Xia, Wei Hu, Xiaoxuan Ma, Huawei HeFei Univ Technol Sch Management Hefei 230009 Anhui Peoples R China Changshu Inst Technol Business Sch Changshu 215500 Jiangsu Peoples R China

Unmanned aerial vehicle (UAV) path planning in a hostile environment (UPPHE) considers the features and information of defense systems to plan a risk-constrained shortest path for a UAV from the initial position to target. When the characteristics of defense systems cannot be accurately expressed manually, it will be brutal to effectively or efficiently implement the classical planning methods because of the lack of risk information to constrain and guide planning. We present a data-driven approach for UPPHE, the feedback rapidly-exploring random tree star algorithm (FRRT*), with a data-driven risk network and feedback module. FRRT* can use in-formation extracted from situation data to constrain the growth of the random tree and make biased adjustments to improve planning efficiency. Simulation experiments verify the effectiveness of the risk network in guiding planning, and the improved efficiency brought by the feedback module.

关键词： Unmanned aerial vehicle rapidly-exploring random tree Path planning Neural network

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Motion planning with dynamics awareness for long reach manipulation in aerial robotic systems with two arms

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INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS 2018年第3期15卷

作者： Caballero, Alvaro Bejar, Manuel Rodriguez-Castano, Angel Ollero, Anibal Univ Seville Robot Vis & Control Grp GRVC Camino Descubrimientos S-N Seville 41092 Spain Univ Pablo de Olavide Robot Vis & Control Grp GRVC Seville Spain

Human activities in maintenance of industrial plants pose elevated risks as well as significant costs due to the required shutdowns of the facility. An aerial robotic system with two arms for long reach manipulation in cluttered environments is presented to alleviate these constraints. The system consists of a multirotor with a long bar extension that incorporates a lightweight dual arm in the tip. This configuration allows aerial manipulation tasks even in hard-to-reach places. The objective of this work is the development of planning strategies to move the aerial robotic system with two arms for long reach manipulation in a safe and efficient way for both navigation and manipulation tasks. The motion planning problem is addressed considering jointly the aerial platform and the dual arm in order to achieve wider operating conditions. Since there exists a strong dynamical coupling between the multirotor and the dual arm, safety in obstacle avoidance will be assured by introducing dynamics awareness in the operation of the planner. On the other hand, the limited maneuverability of the system emphasizes the importance of energy and time efficiency in the generated trajectories. Accordingly, an adapted version of the optimal rapidly-exploring random tree algorithm has been employed to guarantee their optimality. The resulting motion planning strategy has been evaluated through simulation in two realistic industrial scenarios, a riveting application and a chimney repairing task. To this end, the dynamics of the aerial robotic system with two arms for long reach manipulation has been properly modeled, and a distributed control scheme has been derived to complete the test bed. The satisfactory results of the simulations are presented as a first validation of the proposed approach.

关键词： Unmanned aerial vehicle aerial manipulation motion planning rapidly-exploring random tree planning with dynamics awareness

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3D Dynamic Motion Planning for Robot-assisted Cannula Flexible Needle Insertion into Soft Tissue

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INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS 2016年第3期13卷 121-121页

作者： Zhao, Yan-Jiang Wu, Wen-Qiang Zhang, Yong-De Wang, Rui-Xue Peng, Jing-Chun Yu, Yan Harbin Univ Sci & Technol Intelligent Machine Inst Harbin Peoples R China Thomas Jefferson Univ Dept Radiat Oncol Philadelphia PA 19107 USA

In robot-assisted needle-based medical procedures, insertion motion planning is a crucial aspect. 3D dynamic motion planning for a cannula flexible needle is challenging with regard to the nonholonomic motion of the needle tip, the presence of anatomic obstacles or sensitive organs in the needle path, as well as uncertainties due to the dynamic environment caused by the movements and deformations of the organs. The kinematics of the cannula flexible needle is calculated in this paper. Based on a rapid and robust static motion planning algorithm, referred to as greedy heuristic and reachability-guided rapidly-exploring random trees, a 3D dynamic motion planner is developed by using replanning. Aiming at the large detour problem, the convergence problem and the accuracy problem that replanning encounters, three novel strategies are proposed and integrated into the conventional replanning algorithm. Comparisons are made between algorithms with and without the strategies to verify their validity. Simulations showed that the proposed algorithm can overcome the above-noted problems to realize real-time replanning in a 3D dynamic environment, which is appropriate for intraoperative planning.

关键词： Dynamic Motion Planning rapidly-exploring random tree Cannula Flexible Needle Robot-assisted Surgery

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Optimal randomized path planning for redundant manipulators based on Memory-Goal-Biasing

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INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS 2018年第4期15卷

作者： Han, Dong Nie, Hong Chen, Jinbao Chen, Meng Nanjing Univ Aeronaut & Astronaut State Key Lab Mech & Control Mech Struct Nanjing 210016 Jiangsu Peoples R China Shanghai Aerosp Syst Engn Res Inst Shanghai Peoples R China

Planning path rapidly and optimally is one of the key technologies for industrial manipulators. A novel method based on Memory-Goal-Biasing-rapidly-exploring random tree is proposed to solve high-dimensional manipulation planning more rapidly and optimally. The tree extension of Memory-Goal-Biasing-rapidly-exploring random tree can be divided into random extension and goal extension. In the goal extension, the nodes extended to the goal are recorded in a memory, and then the node closest to the goal is selected in the search tree excepting the nodes in the memory for overcoming the local minimum. In order to check collisions efficiently, the manipulator is simplified into several key points, and the obstacle area is appropriately enlarged for safety. Taking the redundant manipulator of Baxter robot as an example, the proposed algorithm is verified through Movelt ! software. The results show that Memory-Goal-Biasing-rapidly-exploring random tree only takes a few seconds for the path planning of the redundant manipulator in some complex environments, and within an acceptable time, its optimization performance is better than that of traditional optimal method in terms of the obtained path costs and the corresponding standard deviation.

关键词： rapidly-exploring random tree manipulation planning local minimum redundant manipulator Movelt

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A sampling-based multi-tree fusion algorithm for frontier detection

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INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS 2019年第4期16卷

作者： Qiao, Wenchuan Fang, Zheng Si, Bailu Northeastern Univ Fac Robot Sci & Engn Shenyang 110819 Liaoning Peoples R China Chinese Acad Sci Shenyang Inst Automat State Key Lab Robot Shenyang 110016 Liaoning Peoples R China Chinese Acad Sci Inst Robot & Intelligent Mfg Shenyang 110169 Liaoning Peoples R China

Autonomous exploration is a key step toward real robotic autonomy. Among various approaches for autonomous exploration, frontier-based methods are most commonly used. One efficient method of frontier detection exploits the idea of the rapidly-exploring random tree and uses tree edges to search for frontiers. However, this method usually needs to consume a lot of memory resources and searches for frontiers slowly in the environments where random trees are not easy to grow (unfavorable environments). In this article, a sampling-based multi-tree fusion algorithm for frontier detection is proposed. Firstly, the random tree's growing and storage rules are changed so that the disadvantage of its slow growing under unfavorable environments is overcome. Secondly, a block structure is proposed to judge whether tree nodes in a block play a decisive role in frontier detection, so that a large number of redundant tree nodes can be deleted. Finally, two random trees with different growing rules are fused to speed up frontier detection. Experimental results in both simulated and real environments demonstrate that our algorithm for frontier detection consumes fewer memory resources and shows better performances in unfavorable environments.

关键词： Exploration frontier-based rapidly-exploring random tree

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A fast and accurate compound collision detector for RRT motion planning

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ROBOTICS AND AUTONOMOUS SYSTEMS 2023年第1期167卷

作者： Wu, Shangliang Liu, Guangyu Zhang, Yanxin Xue, Anke Hangzhou Dianzi Univ Key Lab IOT & Informat Fus Technol Zhejiang Prov Hangzhou Zhejiang Peoples R China Univ Auckland Dept Exercise Sci Auckland New Zealand

The application of artificial intelligence tools has led to newly developed collision detectors which have better computational efficiency than the kinematics-and-geometry based collision detectors (KCD) to improve robot motion planning strategies. However, new detectors are not very accurate in some cases. To improve the accuracy, a trade-off between efficiency and accuracy is required. We propose a novel compound collision detector (CCD) for collision queries that modifies the planners of rapidly-exploring random tree (RRT) to improve the classical probabilistic collision detector (PCD). It is composed of an exact collision detector (ECD), an inference collision detector (ICD) and a strategy to determine ECD or ICD based on some conditions. In our CCD, we use a sphere-ellipsoidal pseudo distance (SEPD) in the determination strategy to alleviate the problem of highly-frequent outputs of false-positive in narrow passages of PCD, and a node based bounding method (NBB) to increase the speed of data storage and loading for the sub-algorithm ICD. Experiments on a Kinova Jaco assistive robotic arm are taken to evaluate the performance of our CCD, which show an improved accuracy with a small reduction of speed in comparison with PCD. So, it is a promising tool in robot motion planning.& COPY;2023 Elsevier B.V. All rights reserved.

关键词： Collision detection rapidly-exploring random tree Motion planning Manipulator Machine learning

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Study of a chassis path planning algorithm for a forest harvester

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INTERNATIONAL JOURNAL OF FOREST ENGINEERING 2023年第3期34卷 439-451页

作者： Tan, Zhuoxian Liu, Jinhao Sun, Biao Qin, Haoxian Ma, Yuewei Beijing Forestry Univ Sch Technol Beijing Peoples R China Beijing Forestry Univ Sch Technol 35 Qinghua East Rd Beijing 100083 Peoples R China

In this study, a global path planning method is proposed for the motion of the chassis of a forest harvester to satisfy the requirements of obstacle avoidance when the harvester is working in the forest. First, a distributed plane map is generated in accordance with the characteristics of forest environment. Subsequently, a Ms_BiRRT algorithm is proposed based on the principle of the random search tree algorithm for multi-stage target-guided global path planning, and the path is further optimized. The effectiveness of the Ms_BiRRT algorithm proposed in this study is examined through experimental simulation. Three types of map environments are designed based on the characteristics of the forest environment. The average planning time of the proposed algorithm is extended by 90.69% and 47.37%, the path length is shortened by 10.40% and 8.23%, the total number of nodes is reduced by 81.52% and 54.65%, and the node utilization rate increases from 18.47% and 44.12% to 85.46%, respectively, compared with classical RRT algorithm and two-way RRT algorithm. The path planning algorithm proposed in this study is capable of quickly reaching the target point in different environments, improving the convergence speed of the algorithm, and increasing the efficiency and stability of the forest harvester in the working process based on the initial path guidance and target guidance methods.

关键词： Harvesting machinery intelligent forestry global planning rapidly-exploring random tree

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Vehicle's RRT path planning algorithm under dynamics constraint

Vehicle's RRT path planning algorithm under dynamics constra...

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2016 China International Conference on Inertial Technology and Navigation

作者： SHANG Song-tian ZHANG Kai LEI Jin-zhou QIU Zu-qi School of Automation Beijing Institute of Technology

This article based on the need of autonomous driving vehicle's path planning algorithm,study and research the existing problems,like not satisfied the vehicle non-holonomic constrain and optimizing the vehicle path *** three parts:First,design and implement the traditional RRT(rapidly-exploring random tree) path planning algorithm,build two different complexities simulation experiment background in Mat lab,and also complete the global path planning in two different environment with ***,design and update the RRT algorithm on account of non-holonomic constrain and B-spline *** get the global paths in simulation experiment,which satisfy the non-holonomic constrain as a ***,apply the improve ideological to RRT* path planning algorithm,and take advantage of RRT* algorithm's optimizing of path planning,besides,test the improved RRT* algorithm in simulation experiment to get the perfect global path planning algorithm under non-holonomic constrain,verified the method's availability as well.

关键词： path planning rapidly-exploring random tree non-holonomic global path planning B-spline

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A Probability Adaptive Sampling-based Algorithm for Obstacle Avoidance Motion Planning Problems

A Probability Adaptive Sampling-based Algorithm for Obstacle...

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第三十八届中国控制会议

作者： Kai Mi Jun Zheng Yunkuan Wang Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences

In recent years,with the increasingly complex robot application environments,the research of robotic autonomous obstacle avoidance motion planning is one of the key technologies to improve its *** this paper,we present an improved algorithm based on rapidly-exploring random tree(RRT)algorithm,which is called Probability Adaptive RRT(PARRT).We analyze the defects of the original algorithm in detail from the perspective of Voronoi *** some complicated occasions,especially when the robot is surrounded by obstacles,the efficiency of the original algorithm will be greatly *** this problem,we proposed an expansion probability adaptive *** dynamically adjusting the extending probability of nodes near obstacles,the invalid node expansion is reduced effectively and the planning speed is greatly ***,we experiment in both two-dimensional space and the joint space of *** planning results show the improved effect of the proposed algorithm.

关键词： rapidly-exploring random tree Voronoi diagram motion planning extending probability probability adaptive

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Research on path planning of intelligent shopping cart in large shopping mall

Research on path planning of intelligent shopping cart in la...

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第30届中国控制与决策会议

作者： Yi Cheng Yun Wang Key Laboratory of Advanced Electrical Engineering and Energy Technology Tianjin Polytechnic University Tianjin Polytechnic University School of Electronic Engineering and Automation

The recovery and arrangement of shopping carts in large shopping malls need to invest a lot of manpower and financial resources, and its recovery efficiency is low. In order to achieve the purpose of self-driving and collision free recovery of shopping cart near work, an improved algorithm is proposed. The basic RRT algorithm takes the starting point of the intelligent shopping cart as the root node and adds the gravity component of the artificial potential field, and sets the node selection range, which overcomes the randomness of the random tree growth. The simulation experiments show that the improved algorithm has the characteristics of high obstacle avoidance, short search time and strong real-time.

关键词： Path planning rapidly-exploring random tree Intelligent shopping cart

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