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Kinematic Constrained Bi-directional RRT with Efficient Branch Pruning for robot path planning
Expert Systems With Applications, 2021In this paper, we present a novel path planning algorithm called Kinematic Constrained Bi-directional Rapidly-exploring Random Tree with Efficient Branch Pruning (KB-RRT*), which is designed on the basis of Bi-directional Rapidly-exploring Random Tree ...
Jiankun Wang, Baopu Li, Max Q-H Meng
exaly +2 more sources
IEEE transactions on industrial electronics (1982. Print), 2023
To avoid the cumbersome calculation of inverse kinematics and improve the efficiency of obstacle avoidance, a novel potential guided bidirectional rapidly-exploring random tree star with the direct connection strategy for redundant robot manipulators in ...
Jun Dai, Yi Zhang, H. Deng
semanticscholar +1 more source
To avoid the cumbersome calculation of inverse kinematics and improve the efficiency of obstacle avoidance, a novel potential guided bidirectional rapidly-exploring random tree star with the direct connection strategy for redundant robot manipulators in ...
Jun Dai, Yi Zhang, H. Deng
semanticscholar +1 more source
SVF-RRT*: A Stream-Based VF-RRT* for USVs Path Planning Considering Ocean Currents
IEEE Robotics and Automation Letters, 2023In a large-scale oceanic environment with spatially variable ocean currents, it is vital for unmanned surface vehicles (USVs) to navigate with a safe and energy-efficient path.
Weilong Zhang +3 more
semanticscholar +1 more source
Path Planning Based on Improved RRT Algorithm
2023 2nd International Symposium on Control Engineering and Robotics (ISCER), 2023The traditional path-exploring Random Trees (RRT) algorithm has the defects of low search efficiency and high path curvature. Based on the basic RRT algorithm, target bias strategy and pruning optimization algorithm are adopted to reduce unnecessary ...
Yanguo Huang, Chao Jin
semanticscholar +1 more source
MK-RRT*: Multi-Robot Kinodynamic RRT Trajectory Planning
2021 International Conference on Unmanned Aircraft Systems (ICUAS), 2021This paper introduces MK-RRT*: a Multi-robot Kinodynamic RRT*-based framework for trajectory planning of multiple dynamically-modeled robots. The framework includes both tightly-coupled and loosely-coupled methods for planning. The simultaneous, tightly-coupled, method provides an asymptotically optimal solution to the multi-robot trajectory planning ...
Brennan Cain +2 more
openaire +1 more source
Proceedings of the International Symposium on Combinatorial Search, 2022
Sampling-based motion planning algorithms such as Rapidly exploring Random Trees (RRTs) have been used in robotic applications for a long time. In this paper, we propose a method that combines deep learning with RRT* method. We use a neural network to learn a sample strategy for RRT*.We evaluate Deep RRT* in a collection of 2D scenarios.
Xuzhe Dang +2 more
openaire +1 more source
Sampling-based motion planning algorithms such as Rapidly exploring Random Trees (RRTs) have been used in robotic applications for a long time. In this paper, we propose a method that combines deep learning with RRT* method. We use a neural network to learn a sample strategy for RRT*.We evaluate Deep RRT* in a collection of 2D scenarios.
Xuzhe Dang +2 more
openaire +1 more source
GMR-RRT*: Sampling-Based Path Planning Using Gaussian Mixture Regression
IEEE Transactions on Intelligent Vehicles, 2022Mobile robot autonomous path planning is an essential factor for its wide deployment in real-world applications. Conventional sampling-based algorithms have gained tremendous success in the path planning field, but they usually take much time to find the
Jiankun Wang +3 more
semanticscholar +1 more source
Neural RRT*: Learning-Based Optimal Path Planning
IEEE Transactions on Automation Science and Engineering, 2020Rapidly random-exploring tree (RRT) and its variants are very popular due to their ability to quickly and efficiently explore the state space. However, they suffer sensitivity to the initial solution and slow convergence to the optimal solution, which ...
Jiankun Wang +4 more
semanticscholar +1 more source
IEEE International Conference on Robotics and Automation, 2023
Sampling-based planning algorithms like Rapidly-exploring Random Tree (RRT) are versatile in solving path planning problems. RRT* offers asymptotic optimality but requires growing the tree uniformly over the free space, which leaves room for efficiency ...
Zhe Huang +2 more
semanticscholar +1 more source
Sampling-based planning algorithms like Rapidly-exploring Random Tree (RRT) are versatile in solving path planning problems. RRT* offers asymptotic optimality but requires growing the tree uniformly over the free space, which leaves room for efficiency ...
Zhe Huang +2 more
semanticscholar +1 more source
E-RRT*: Path Planning for Hyper-Redundant Manipulators
IEEE Robotics and Automation Letters, 2023A hyper-redundant manipulator(HRM) can flexibly accomplish tasks in narrow spaces. However, its excessive degrees of freedom pose challenges for path planning.
Hongcheng Ji +3 more
semanticscholar +1 more source