检索结果-内蒙古大学图书馆

3rd Modeling, Estimation and Control Conference (MECC)

作者： Patel, Darshit Eskandarian, Azim Virginia Tech Dept Mech Engn Blacksburg VA 24061 USA

ISBN: (纸本)9781713872344

rapidly exploring random trees (RRT) is one of the most widely used algorithms for motion planning in the field of robotics. To reduce the exploration time, RRT-Connect was introduced where two trees are simultaneously formed and eventually connected. Probabilistic RRT used the concept of position probability map to introduce goal biasing for faster convergence. In this paper, we propose a modified method to combine the pRRT and RRT-Connect techniques and obtain a feasible trajectory around the obstacles quickly. Instead of forming a single tree from the start point to the destination point, intermediate goal points are selected around the obstacles. Multiple trees are formed to connect the start, destination, and intermediate goal points. These partial trees are eventually connected to form an overall safe path around the obstacles. The obtained path is tracked using an MPC Stanley controller which results in a trajectory with control commands at each time step. The trajectories generated by the proposed methods are more optimal and in accordance with human intuition. The algorithm is compared with the standard RRT and pRRT for studying its relative performance. Copyright (c) 2023 The Authors. This is an open access article under the CC BY -NC -ND license (hdps://***/licenscs/b-nolld/4.0/)

关键词： Motion Planning Model Predictive Control rapidly exploring random trees Autonomous Driving Sampling-Based Algorithms

来源：评论

学校读者我要写书评

暂无评论

A Path Planning Method with a Bidirectional Potential Field Probabilistic Step Size RRT for a Dual Manipulator

引用

SENSORS 2023年第11期23卷 5172-5172页

作者： Liu, Youyu Tao, Wanbao Li, Shunfang Li, Yi Wang, Qijie Anhui Polytech Univ Anhui Key Lab Detect Technol & Energy Saving Devic Wuhu 241000 Peoples R China Wuhu Inst Technol Res Off Wuhu 241000 Peoples R China Anhui Polytech Univ Mech Engn Dept Wuhu 241000 Peoples R China

The search efficiency of a rapidly exploring random tree (RRT) can be improved by introducing a high-probability goal bias strategy. In the case of multiple complex obstacles, the high-probability goal bias strategy with a fixed step size will fall into a local optimum, which reduces search efficiency. Herein, a bidirectional potential field probabilistic step size rapidly exploring random tree (BPFPS-RRT) was proposed for the path planning of a dual manipulator by introducing a search strategy of a step size with a target angle and random value. The artificial potential field method was introduced, combining the search features with the bidirectional goal bias and the concept of greedy path optimization. According to simulations, taking the main manipulator as an example, compared with goal bias RRT, variable step size RRT, and goal bias bidirectional RRT, the proposed algorithm reduces the search time by 23.53%, 15.45%, and 43.78% and decreases the path length by 19.35%, 18.83%, and 21.38%, respectively. Moreover, taking the slave manipulator as another example, the proposed algorithm reduces the search time by 6.71%, 1.49%, and 46.88% and decreases the path length by 19.88%, 19.39%, and 20.83%, respectively. The proposed algorithm can be adopted to effectively achieve path planning for the dual manipulator.

关键词： dual manipulator rapidly exploring random trees angle selection artificial potential field method bidirectional potential field probabilistic step size path optimization

来源：评论

学校读者我要写书评

暂无评论

Visual-RRT: Integrating IBVS as a steering method in an RRT planner☆

引用

ROBOTICS AND AUTONOMOUS SYSTEMS 2023年 169卷

作者： Reyes, Ramses Becerra, Israel Murrieta-Cid, Rafael Hutchinson, Seth CIMAT Ctr Invest Matemat Guanajuato 36023 Mexico Consejo Nacl Ciencia & Technol CONACyT Mexico City 03940 Mexico Georgia Inst Technol Inst Robot & Intelligent Machines Atlanta GA USA

In this paper, we present a new approach to robot motion planning that anticipates the use of visionbased feedback control during task execution. We accomplish this by incorporating an image-based visual servo (IBVS) controller directly into the steering function used by a rapidly exploring random Tree (RRT) planner. Our approach requires a number of extensions to traditional RRT-style planning. First, we derive a new sampling strategy that augments the usual state information by including image features that will be used by the IBVS control law. These augmented samples are then used by our new IBVS steering function, which simulates an IBVS control law to generate local trajectories that extend the current tree. These trajectories must be validated to ensure that they are collision-free and that all image features remain unoccluded and within the camera field of view throughout the local trajectory. We also provide a formal proof showing that the proposed approach is probabilistically complete. We have applied our approach to the problem of planning trajectories for three different systems: a robotic arm, an unmanned aerial vehicle (UAV) and a car-like robot, which are equipped with an IBVS control law. We explore performance trade-offs in the control design via simulation studies and demonstrate real-world effectiveness via experiments in which a small-scale car-like robot uses IBVS to navigate a track that includes a number of obstacles and potential occlusions. By exploring performance trade-offs, we mean that several elements, such as the metric used to identify nearest neighbors in the RRT and the steering method used to generate nodes, are tested and compared. (c) 2023 Elsevier B.V. All rights reserved.

关键词： Feedback-based motion planning Image-based visual servo control Nonholonomic constraints rapidly exploring random trees

来源：评论

学校读者我要写书评

暂无评论

Probabilistic RRT Connect with intermediate goal selection for online planning of autonomous vehicles

引用

IFAC-PapersOnLine 2023年第3期56卷 373-378页

作者： Darshit Patel Azim Eskandarian Department of Mechanical Engineering Virginia Tech Blacksburg VA 24061 USA

rapidly exploring random trees (RRT) is one of the most widely used algorithms for motion planning in the field of robotics. To reduce the exploration time, RRT-Connect was introduced where two trees are simultaneously formed and eventually connected. Probabilistic RRT used the concept of position probability map to introduce goal biasing for faster convergence. In this paper, we propose a modified method to combine the pRRT and RRT-Connect techniques and obtain a feasible trajectory around the obstacles quickly. Instead of forming a single tree from the start point to the destination point, intermediate goal points are selected around the obstacles. Multiple trees are formed to connect the start, destination, and intermediate goal points. These partial trees are eventually connected to form an overall safe path around the obstacles. The obtained path is tracked using an MPC + Stanley controller which results in a trajectory with control commands at each time step. The trajectories generated by the proposed methods are more optimal and in accordance with human intuition. The algorithm is compared with the standard RRT and pRRT for studying its relative performance.

关键词： Motion Planning Model Predictive Control rapidly exploring random trees Autonomous Driving Sampling-Based Algorithms

来源：评论

学校读者我要写书评

暂无评论

Development of path planning algorithm for biped robot using combined multi-point RRT and visibility graph

引用

International Journal of Information Technology (Singapore) 2021年第4期13卷 1513-1519页

作者： Pradhan, Sarthak Mandava, Ravi Kumar Vundavilli, Pandu R. School of Mechanical Sciences IIT Bhubaneswar Bhubaneswar 752050 India Department of Mechanical Engineering MANIT Bhopal Bhopal 462003 India

Path planning plays an important role while moving the robot from one location to another. The present paper is concerned with solving the path planning problem of the biped robot in a static environment. Here, an attempt is made to combine the benefits of established rapidly exploring random trees (RRT) algorithm and visibility graph algorithm. Various approaches, such as normal RRT that starts the search from start and goal points, multi-node point RRT approach in which a random node is generated somewhere in between the start and goal points, and a tree is started from each of these points as the root, has been developed. Further, visibility graph has also been applied to smooth or optimize the path obtained in the previous step. The developed path planning algorithms are initially tested in computer simulations. For experimental purposes, a global camera attached at the top of the terrain is used to map the environment. Further, a humanoid with 18 degrees of freedom is used to execute the path decided by the algorithm. Required corrections are made at regular intervals if the robot deviates from its path. © 2021, Bharati Vidyapeeth's Institute of Computer Applications and Management.

关键词： Biped robot Path planning rapidly exploring random trees Visibility graphs

来源：评论

学校读者我要写书评

暂无评论

Discovering and Synthesizing Humanoid Climbing Movements

引用

ACM TRANSACTIONS ON GRAPHICS 2017年第4期36卷 1-11页

作者： Naderi, Kourosh Rajamaki, Joose Hamalainen, Perttu Aalto Univ Dept Comp Sci Espoo Finland

This paper addresses the problem of oflline path and movement planning for wall climbing humanoid agents. We focus on simulating bouldering, i.e. climbing short routes with diverse moves, although we also demonstrate our system on a longer wall. Our approach combines a graph-based high-level path planner with low-level sampling-based optimization of climbing moves. Although the planning problem is complex, our system produces plausible solutions to bouldering problems (short climbing routes) in less than a minute. We further utilize a k-shortest paths approach, which enables the system to discover alternative paths - in climbing, alternative strategies often exist, and what might be optimal for one climber could be impossible for others due to individual differences in strength, flexibility, and reach. We envision our system could be used, e.g. in learning a climbing strategy, or as a test and evaluation tool for climbing route designers. To the best of our knowledge, this is the first paper to solve and simulate rich humanoid wall climbing, where more than one limb can move at the same time, and limbs can also hang free for balance or use wall friction in addition to predefined holds.

关键词： Climbing Motion Synthesis rapidly exploring random trees K Shortest-Paths on Graph Dynamic Graph A* Prune

来源：评论

学校读者我要写书评

暂无评论

A Review of Global Path Planning Methods for Occupancy Grid Maps Regardless of Obstacle Density

引用

JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS 2016年第1-4期84卷 829-858页

作者： Tsardoulias, E. G. Iliakopoulou, A. Kargakos, A. Petrou, L. Aristotle Univ Thessaloniki Sch Elect & Comp Engn Dept Elect & Comp Engn Fac Engn Thessaloniki 54124 Greece

Path planning constitutes one of the most crucial abilities an autonomous robot should possess, apart from Simultaneous Localization and Mapping algorithms (SLAM) and navigation modules. Path planning is the capability to construct safe and collision free paths from a point of interest to another. Many different approaches exist, which are tightly dependent on the map representation method (metric or feature-based). In this work four path planning algorithmic families are described, that can be applied on metric Occupancy Grid Maps (OGMs): Probabilistic RoadMaps (PRMs), Visibility Graphs (VGs), rapidly exploring random trees (RRTs) and Space Skeletonization. The contribution of this work includes the definition of metrics for path planning benchmarks, actual benchmarks of the most common global path planning algorithms and an educated algorithm parameterization based on a global obstacle density coefficient.

关键词： Path planning Probabilistic RoadMaps Visibility graph Generalized Voronoi graph rapidly exploring random trees Occupancy grid maps

来源：评论

学校读者我要写书评

暂无评论

Analysis of a motion planning problem for sweet-pepper harvesting in a dense obstacle environment

引用

BIOSYSTEMS ENGINEERING 2016年 146卷 85-97页

作者： Bac, C. Wouter Roorda, Tim Reshef, Roi Berman, Sigal Hemming, Jochen van Henten, Eldert J. Univ Wageningen & Res Ctr Wageningen UR Greenhouse Hort Droevendaalsesteeg 1 NL-6708 PB Wageningen Netherlands Univ Wageningen & Res Ctr Farm Technol Grp Droevendaalsesteeg 1 NL-6708 PB Wageningen Netherlands Ben Gurion Univ Negev Dept Ind Engn & Management POB 653 IL-84105 Beer Sheva Israel

To reach a fruit in an obstacle-dense crop environment, robotic fruit harvesting requires a collision-free motion of the manipulator and end-effector. A novel two-part analysis was conducted of a sweet-pepper harvesting robot based on data of fruit (N = 158) and stem locations collected from a greenhouse. The first part of the analysis compared two methods of selecting the azimuth angle of the end-effector. The new 'constrained-azimuth' method avoided risky paths and achieved a motion planning success similar to the 'full-azimuth' method. In the second part, a sensitivity analysis was conducted for five parameters specifying the crop (stem spacing and fruit location), the robot (end-effector dimensions and robot position) and the planning algorithm, to evaluate their effect on successfully finding a collision-free goal configuration and path. Reducing end-effector dimensions and widening stem spacing are promising research directions because they significantly improved goal configuration success, from 63% to 84%. However, the fruit location at the stem is the strongest influencing parameter and therefore provides an incentive to train or breed plants that develop more fruit at the front side of the plant stem. The two analyses may serve as useful tools to study motion planning problems in a dense obstacle environment. (C) 2015 IAgrE. Published by Elsevier Ltd. All rights reserved.

关键词： Motion planning Agricultural robot Sensitivity analysis rapidly exploring random trees Grasp pose

来源：评论

学校读者我要写书评

暂无评论

High-level motion planning for CPG-driven modular robots

引用

ROBOTICS AND AUTONOMOUS SYSTEMS 2015年 68卷 116-128页

作者： Vonasek, Vojtech Saska, Martin Winkler, Lutz Preucil, Libor Czech Tech Univ Fac Elect Engn CR-16635 Prague 6 Czech Republic Karlsruhe Inst Technol Inst Proc Control & Robot D-76131 Karlsruhe Germany

Modular robots may become candidates for search and rescue operations or even for future space missions, as they can change their structure to adapt to terrain conditions and to better fulfill a given task. A core problem in such missions is the ability to visit distant places in rough terrain. Traditionally, the motion of modular robots is modeled using locomotion generators that can provide various gaits, e.g. crawling or walking. However, pure locomotion generation cannot ensure that desired places in a complex environment with obstacles will in fact be reached. These cases require several locomotion generators providing motion primitives that are switched using a planning process that takes the obstacles into account. In this paper, we present a novel motion planning method for modular robots equipped with elementary motion primitives. The utilization of primitives significantly reduces the complexity of the motion planning which enables plans to be created for robots of arbitrary shapes. The primitives used here do not need to cope with environmental changes, which can therefore be realized using simple locomotion generators that are scalable, i.e., the primitives can provide motion for robots with many modules. As the motion primitives are realized using locomotion generators, no reconfiguration is required and the proposed approach can thus be used even for modular robots without self-reconfiguration capabilities. The performance of the proposed algorithm has been experimentally verified in various environments, in physical simulations and also in hardware experiments. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Motion planning Modular robots rapidly exploring random trees Central pattern generator

来源：评论

学校读者我要写书评

暂无评论

A Planning Method to Obtain Good Quality Paths for Autonomous Cars 4

A Planning Method to Obtain Good Quality Paths for Autonomou...

引用

Eastern European Regional Conference on the Engineering of Computer-Based Systems ECBS-EERC

作者： Kiss, Domokos Csorvasi, Gabor Nagy, Akos Budapest Univ Technol & Econ Dept Automat & Appl Informat Budapest Hungary

ISBN: (纸本)9781467379670

Path planning among obstacles for nonholonomic systems is a widely researched area nowadays, but it is still one of the most challenging problems in autonomous navigation. We have recently presented a rapidly exploring random tree based global planner (RTR) and a steering method (C*CS) for car-like vehicles, which uses circular and straight movements. With the aid of these two methods it is possible to obtain a feasible path, even in narrow spaces and in situations requiring non-trivial maneuvers. This paper presents an improved solution for environments, where not all obstacles are known at the beginning and these are discovered during the motion of the robot. We also introduce an extension to the presented algorithm to achieve paths of better quality, i.e. more similar to a reasonable path generated by a human driver.

关键词： Nonholonomic motion planning rapidly exploring random trees changing environments narrow spaces

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：