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HB-RRT:A path planning algorithm for mobile robots using Halton sequence-based rapidly-exploring random tree

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2024年第PartE期133卷

作者： Zhong, Huageng Cong, Ming Wang, Minghao Du, Yu Liu, Dong Dalian Univ Technol Sch Mech Engn Dalian Peoples R China Dalian Univ Technol Ningbo Inst Ningbo Zhejiang Peoples R China Dalian Jiaotong Univ Sch Mech Engn Dalian Peoples R China

Path planning remains crucial for efficient robot operation. A Halton Biased Rapidly-exploring Random Tree (HB-RRT) path planning algorithm is introduced in this study. The Halton sequence, known for its uniform distribution and low discrepancy, is employed for sampling. Issues arising from the pseudo-random sequence in the standard RRT algorithm, leading to uneven distribution of sampling points, are addressed. A mouse-inspired goal-oriented strategy and a candidate sampling pool strategy are incorporated to enhance the sampling point quality, thereby addressing the challenge of insufficient memory during node expansion. Path optimization is further achieved through a multi-level planning approach, which aims to minimize redundancy. A subsequent smoothing of the path is conducted using a cubic B-spline method. Comparisons with the RRT, Bionic Target Bias-RRT, and Informed-RRT* algorithms, through both numerical simulations and real -world testing, confirm the superiority of the HB-RRT algorithm in terms of planning time, path length, and overall path quality.

关键词： Path planning sampling-based algorithms Rapidly-exploring random tree (RRT) Optimal path planning Halton sequence Mobile robotics

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Smooth-RRT*: An Improved Motion Planner for Underwater Robot 27

Smooth-RRT*: An Improved Motion Planner for Underwater Robot

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27th Asia-Pacific Conference on Communications (APCC) - Creating Innovative Communication Technologies for Post-Pandemic Era

作者： Wang, Kehao Li, Shuaifu Wang, Yang Xi, Jing Wuhan Univ Technol Sch Informat Engn Wuhan Peoples R China Wuhan Univ Technol Natl Engn Res Ctr Water Transport Safety Wuhan Peoples R China PLA Army Armaments Dept Mil Representat Off 1 Mil Representat Bur 1 Beijing Peoples R China

ISBN: (数字)9781665499279

ISBN: (纸本)9781665499279

In underwater search and rescue, it is very important for underwater robot to reach the rescue position quickly. Planning path in advance is very important to save rescue time and energy consumption. Therefore, it is meaningful to find a better and shorter path as soon as possible. As a common method of path planning, RRT* has the disadvantages of high cost and slow convergence. To solve these flaws, an improved motion planner for underwater robots is proposed in this paper. In this study, the simulation experiments were divided into twodimensional conditions and three-dimensional conditions, where used point cloud of real underwater scene to find a better initial solution. based on RRT*, this paper finds the ancestor node farthest from the sampling point and without collision in the random tree as parent node, adds intermediate nodes in the path according to the step size, and uses trigonometric inequality many times throughout the process, so as to obtain an optimized path. Through a large number of simulation experiments, the results show that the cost of path is less and the convergence speed is faster than RRT* and Q-RRT*.

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

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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年第PartB期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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CCPF-RRT*: An improved path planning algorithm with consideration of congestion

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第1期228卷

作者： Liang, Yan-ming Zhao, Hai -yang Xian Univ Technol Sch Automat & Informat Engn Xian 710048 Peoples R China

Path planning is essential for robots to efficiently execute jobs in challenging settings. In this paper, we propose a new algorithm, called potential function-based sampling heuristic optimal path planning considering congestion (CCPF-RRT*), extending the standard Rapidly-exploring Random Tree star (RRT*) to address the issues of slow convergence, high path cost, and how to plan suitable paths in the presence of congested regions in the envi-ronment. First, the CCPF-RRT* algorithm adds the artificial potential field to RRT*, which can drastically cut down on the number of iterations and speed up convergence. Second, a movement cost function that takes congestion intensity and path length into account when evaluating a connection between two path nodes is created. This function is then used to direct the growth of new nodes and the updating of parent nodes so that the algorithm takes congestion intensity and path length into account when generating paths. Third, in order to reduce the path cost, the algorithm establishes parent nodes for random nodes by combining the benefits of the F-RRT* algorithm. The movement cost function is added to the process of establishing parent nodes to optimize the path cost in order to prevent the creation of parent nodes that force the path to traverse through crowded areas. In experimental simulation results, the algorithm outperforms RRT*, Q-RRT*, PQ-RRT*, and F-RRT* not only in terms of initial solution and quick convergence speed, but is also able to design an ideal path with the lowest movement cost in a crowded environment using the movement cost function.

关键词： Path planning Rapidly-exploring Random Tree star (RRT*) sampling-based algorithms Considering congestion

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

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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

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PiP-X: Online feedback motion planning/replanning in dynamic environments using invariant funnels

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INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH 2023年

作者： Jaffar, Mohamed Khalid M. Otte, Michael Univ Maryland Dept Aerosp Engn College Pk MD USA Univ Maryland Mot & Teaming Lab 8228 Paint Branch Dr College Pk MD 20742 USA

Computing kinodynamically feasible motion plans and repairing them on-the-fly as the environment changes is a challenging, yet relevant problem in robot navigation. We propose an online single-query sampling-based motion re-planning algorithm using finite-time invariant sets, commonly referred to as "funnels". We combine concepts from nonlinear systems analysis, sampling-based techniques, and graph-search methods to create a single framework that enables feedback motion re-planning for any general nonlinear dynamical system in dynamic workspaces. A volumetric network of funnels is constructed in the configuration space using sampling-based methods and invariant set theory;and an optimal sequencing of funnels from robot configuration to a desired goal region is then determined by computing the shortest-path subgraph (tree) in the network. Analyzing and formally quantifying the stability of trajectories using Lyapunov level-sets ensures kinodynamic feasibility and guaranteed set-invariance of the solution paths. Though not required, our method is capable of using a pre-computed library of motion primitives to speedup online computation of controllable motion plans that are volumetric in nature. We introduce a novel directed-graph data structure to represent the funnel-network and its inter-sequencibility;helping us leverage discrete graph-based incremental search to quickly rewire feasible and controllable motion plans on-the-fly in response to changes in the environment. We validate our approach on a simulated cart-pole, car-like robot, and 6DOF quadrotor platform in a variety of scenarios within a maze and a random forest environment. Using Monte Carlo methods, we evaluate the performance in terms of algorithm success, length of traversed trajectory, and runtime.

关键词： Feedback motion planning online re-planning sampling-based algorithms incremental graph-search nonlinear systems invariant set theory motion primitives

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A Non-uniform sampling Approach for Fast and Efficient Path Planning

A Non-uniform Sampling Approach for Fast and Efficient Path ...

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OCEANS Conference

作者： Wilson, James P. Shen, Zongyuan Gupta, Shalabh Univ Connecticut Dept Elect & Comp Engn Storrs CT 06269 USA

ISBN: (纸本)9780692935590

In this paper, we develop a non-uniform sampling approach for fast and efficient path planning of autonomous vehicles. The approach uses a novel non-uniform partitioning scheme that divides the area into obstacle-free convex cells. The partitioning results in large cells in obstacle-free areas and small cells in obstacle-dense areas. Subsequently, the boundaries of these cells are used for sampling;thus significantly reducing the burden of uniform sampling. When compared with a standard uniform sampler, this smart sampler significantly 1) reduces the size of the sampling space while providing completeness and optimality guarantee, 2) provides sparse sampling in obstacle-free regions and dense sampling in obstacle-rich regions to facilitate faster exploration, and 3) eliminates the need for expensive collision-checking with obstacles due to the convexity of the cells. This sampling framework is incorporated into the RRT* path planner. The results show that RRT* with the non-uniform sampler gives a significantly better convergence rate and smaller memory footprint as compared to RRT* with a uniform sampler.

关键词： non-uniform sampling autonomous vehicles sampling-based algorithms near-optimal path planning

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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 9203-9203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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Solving the motion planning problem using learning experience through case-based reasoning and machine learning algorithms

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AIN SHAMS ENGINEERING JOURNAL 2020年第1期11卷 133-142页

作者： Abdelwahed, Mustafa F. Mohamed, Amr E. Saleh, Mohamed Aly Helwan Univ Fac Engn Dept Elect Commun & Comp 1 Sherif St Cairo 11792 Egypt

This article presents two novel methodologies for solving the motion planning problem through retained experience. Both approaches employ AI's case-based reasoning (CBR) technique. Case-based reasoning is an expert system development methodology which reuses past solutions to solve new problems. The first approach uses CBR to retain K similar cases to solve the motion planning problem by merging those solutions into a set. Afterwards, it picks from this set based on a heuristic function to assemble a final solution. Regarding the second approach, it employs the retained K similar cases differently. It uses those solution to build a graph which can be queried using traditional graph search algorithms. Results prove the success of such approaches concerning solution quality and success rate compared to different experience-based algorithms. Such utilization for CBR systems develops new research directions for building systems that can solve NP problems based on retained experiences exclusively. (C) 2019 The Authors. Published by Elsevier B.V. on behalf of Faculty of Engineering, Ain Shams University. This is an open access article under the CC BY-NC-ND license (http://***/licenses/by-nc-nd/4.0/).

关键词： sampling-based algorithms Experience-based algorithms Case-based reasoning Artificial intelligence Motion planning

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PQ-RRT*: An improved path planning algorithm for mobile robots

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EXPERT SYSTEMS WITH APPLICATIONS 2020年 152卷 113425-113425页

作者： Li, Yanjie Wei, Wu Gao, Yong Wang, Dongliang Fan, Zhun South China Univ Technol Sch Automat Sci & Engn Guangzhou 510641 Guangdong Peoples R China Shantou Univ Dept Elect & Informat Engn Shantou 515063 Peoples R China Shantou Univ Key Lab Digital Signal & Image Proc Guangdong Pro Shantou 515063 Peoples R China Shantou Univ Key Lab Intelligent Mfg Technol Minist Educ Shantou 515063 Guangdong Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 43003 Peoples R China

During the last decade, sampling-based algorithms for path planning have gained considerable attention. The RRT*, a variant of RRT (rapidly-exploring random trees), is of particular concern to researchers due to its asymptotic optimality. However, the limits of the slow convergence rate of RRT* makes it inefficient for applications. For the purposes of overcoming these limitations, this paper proposes a novel algorithm, PQ-RRT*, which combines the strengths of P-RRT* (potential functions based RRT*) and Quick-RRT*. PQ-RRT* guarantees a fast convergence to an optimal solution and generates a better initial solution. The asymptotic optimality and fast convergence of the proposed algorithm are proved in this paper. Comparisons of PQ-RRT* with P-RRT* and Quick-RRT* in four benchmarks verify the effectiveness of the proposed algorithm. (C) 2020 Elsevier Ltd. All rights reserved.

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

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