Novel Potential Guided Bidirectional RRT<sup>*</sup> With Direct Connection Strategy for Path Planning of Redundant Robot Manipulators in Joint Space

Dai, Jun; Zhang, Yi; Deng, Hua

doi:10.1109/tie.2023.3269462

Cited by 16 publications

(8 citation statements)

References 31 publications

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“…Islam et al proposed RRT*-Smart in [28], which, after generating the initial path, improves RRT*'s convergence speed by removing redundant nodes and optimizing sampling. In [29], Dai proposed a novel algorithm based on bidirectional Rapidly Exploring Random Trees and direct connection. Initially, an expansion strategy based on artificial potential fields was designed in the joint space, which was then integrated with the GB-RRT algorithm.…”

Section: Related Workmentioning

confidence: 99%

Efficient Path Planning Based on Dynamic Bridging Rapidly Exploring Random Tree

Qiu,

Li,

Tong

et al. 2024

Applied Sciences

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In the domain of mobile robotic navigation, the real-time generation of low-cost, executable reference trajectories is crucial. This paper propounds an innovative path planning strategy, termed Dynamic Bridging Rapidly Exploring Random Tree (DBR-RRT), which endeavors to enable safe and expedited path navigation. Initially, a heuristic discrimination method is engaged in the path search phase, whereby the issue of sluggish search velocity is tackled by evaluating whether sampled points reside at “bridging locations” within a free space, and by assessing the spatial–geometric relationships between proximate obstacles and auxiliary points. Subsequently, by leveraging extended speed, additional sampling points are generated in the vicinity of existing points to augment the search’s efficacy. Ultimately, the path is optimized and pruned by synthesizing the local curvature of the sampling points and the proximity to obstacles, assigning varied priorities to nodes, thus ensuring that the path’s quality and smoothness is upheld.

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Section: Related Workmentioning

confidence: 99%

Efficient Path Planning Based on Dynamic Bridging Rapidly Exploring Random Tree

Qiu,

Li,

Tong

et al. 2024

Applied Sciences

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“…A connection strategy for redundant robot manipulators in the joint space is proposed, designing a joint space expansion strategy based on artificial potential field and combining it with the GB-RRT algorithm to improve the obstacle avoidance ability; and designing a direct connection strategy to improve the expansion efficiency. Compared with bidirectional RRT and GB-RRT, it has better path planning [85]. Javier Moreno-Valenzuela proposes an adaptive gravity compensation proportional-derivative (PD) plus LIAW algorithm, which limits the angle of the robot arm to improve performance [86].…”

Section: Motion Controlmentioning

confidence: 99%

Bibliometric analysis and visualization of research and development trends in humanoid robotics

Zhang,

Wang,

Zhou

et al. 2024

AETR

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This study searched the Web of Science core database for literature related to robotic arms for humanoid robots and used bibliometrics and CiteSpace 6.1.R2 software to visually analyze the authors, institutions, and keywords of the literature published in this field from 2009 to 2023. The study summarizes the current status and trends of robotic arms for humanoid robots and incorporates information extraction to select and include 376 articles for analysis. This study provides the field's development vein by reviewing the relevant literature and follows the research progress in the area of humanoid robots in a particular vein. In addition to thoroughly examining robotic arms and motion generating techniques for robotic arms that mimic human action, this study reviews pertinent literature. A review of the literature indicates a clear trend toward the use of artificial intelligence and machine learning to create motion control schemes for humanoid robotic arms. The report then assesses current methodologies and suggests future research directions for promising studies.

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“…Moreover, when the resultant direction of all the repulsive forces from obstacles is the same as the direction of the attractive force, and the car has not yet reached the target point, it is possible to get stuck in a local optimum where the repulsive and attractive forces are equal. To solve these problems, the traditional repulsive field of the local optimal improvement method proposed by other researchers is improved in this paper by introducing a modulation factor ρ n (q, q goal ) in the repulsive field function, which produces a new repulsive field function U rep (q) as shown in Equation (12), which ensures that the repulsive and attractive forces only reduce to zero simultaneously when the car reaches the target point, thus solving the problems of local optima and elusive targets.…”

Section: Improved Apf-rrt* Algorithmmentioning

confidence: 99%

“…Additionally, some scholars have proposed the RRT-Connect algorithm [11], which generates random trees separately from the starting and target nodes, reducing the search space and improving search speed. To address the shortcomings of the RRT algorithm in ensuring asymptotic optimality, the RRT* algorithm changes the search mode by reselecting the parent node and rewiring, thus generating paths with the best or approximate best length [12].…”

Section: Introductionmentioning

confidence: 99%

Research on Path Planning and Tracking Control of Autonomous Vehicles Based on Improved RRT* and PSO-LQR

2023

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Path planning and tracking control are essential parts of autonomous vehicle research. Regarding path planning, the Rapid Exploration Random Tree Star (RRT*) algorithm has attracted much attention due to its completeness. However, the algorithm still suffers from slow convergence and high randomness. Regarding path tracking, the Linear Quadratic Regulator (LQR) algorithm is widely used in various control applications due to its efficient stability and ease of implementation. However, the relatively empirical selection of its weight matrix can affect the control effect. This study suggests a path planning and tracking control framework for autonomous vehicles based on an upgraded RRT* and Particle Swarm Optimization Linear Quadratic Regulator (PSO-LQR) to address the abovementioned issues. Firstly, according to the driving characteristics of autonomous vehicles, a variable sampling area is used to limit the generation of random sampling points, significantly reducing the number of iterations. At the same time, an improved Artificial Potential Field (APF) method was introduced into the RRT* algorithm, which improved the convergence speed of the algorithm. Utilizing path pruning based on the maximum steering angle constraint of the vehicle and the cubic B-spline algorithm to achieve path optimization, a continuous curvature path that conforms to the precise tracking of the vehicle was obtained. In addition, optimizing the weight matrix of LQR using POS improved path-tracking accuracy. Finally, this article’s improved RRT* algorithm was simulated and compared with the RRT*, target bias RRT*, and P-RRT*. At the same time, on the Simulink–Carsim joint simulation platform, the PSO-LQR is used to track the planned path at different vehicle speeds. The results show that the improved RRT* algorithm optimizes the path search speed by 34.40% and the iteration number by 33.97%, respectively, and the generated paths are curvature continuous. The tracking accuracy of the PSO-LQR was improved by about 59% compared to LQR, and its stability was higher. The position error and heading error were controlled within 0.06 m and 0.05 rad, respectively, verifying the effectiveness and feasibility of the proposed path planning and tracking control framework.

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Novel Potential Guided Bidirectional RRT^* With Direct Connection Strategy for Path Planning of Redundant Robot Manipulators in Joint Space

Cited by 16 publications

References 31 publications

Efficient Path Planning Based on Dynamic Bridging Rapidly Exploring Random Tree

Efficient Path Planning Based on Dynamic Bridging Rapidly Exploring Random Tree

Bibliometric analysis and visualization of research and development trends in humanoid robotics

Research on Path Planning and Tracking Control of Autonomous Vehicles Based on Improved RRT* and PSO-LQR

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Novel Potential Guided Bidirectional RRT* With Direct Connection Strategy for Path Planning of Redundant Robot Manipulators in Joint Space

Cited by 16 publications

References 31 publications

Efficient Path Planning Based on Dynamic Bridging Rapidly Exploring Random Tree

Efficient Path Planning Based on Dynamic Bridging Rapidly Exploring Random Tree

Bibliometric analysis and visualization of research and development trends in humanoid robotics

Research on Path Planning and Tracking Control of Autonomous Vehicles Based on Improved RRT* and PSO-LQR

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Novel Potential Guided Bidirectional RRT^* With Direct Connection Strategy for Path Planning of Redundant Robot Manipulators in Joint Space