Research on path planning based on improved RRT-Connect algorithm

Yang, Hongtao; Li, Huanyu; Liu, Keping; Yu, Wei; Li, Xiaoyuan

doi:10.1109/ccdc52312.2021.9602203

Cited by 10 publications

(11 citation statements)

References 4 publications

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“…Therefore, we propose a tree expansion method with a dynamic step size. The dynamic step method proposed in the current literature (Wang et al, 2022a;Zhang et al, 2022a;Yang et al, 2021) only dynamically changes the step size in the expansion of the tree depending on whether the current node collides with an obstacle or not. If no collision occurs, the step size is expanded, and if a collision occurs, the new node is re-found and expanded with a small step size.…”

Section: Dynamic Variable Step Samplingmentioning

confidence: 99%

“…Since the generated paths have large turning points, the curvature of the planned paths should not vary too much to ensure the stability of the vehicle during the driving process. Up to the present, most papers still use unidirectional pruning (Wang et al, 2021;Yang et al, 2021) to optimize the generated paths. To enhance the pruning speed, we proposed an improved pruning strategy based on the bidirectional pruning of Zhang et al (2021).…”

Section: Bidirectional Pruningmentioning

confidence: 99%

“…, 2022a; Zhang et al. , 2022a; Yang et al. , 2021) only dynamically changes the step size in the expansion of the tree depending on whether the current node collides with an obstacle or not.…”

Section: Qgd-rrt Algorithm Frameworkmentioning

confidence: 99%

“…Up to the present, most papers still use unidirectional pruning (Wang et al. , 2021; Yang et al. , 2021) to optimize the generated paths.…”

Section: Path Post-processingmentioning

confidence: 99%

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Research on path planning of autonomous vehicle based on RRT algorithm of Q-learning and obstacle distribution

Shang

Liu

Qin

et al. 2023

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PurposeThe goal of this research is to develop a dynamic step path planning algorithm based on the rapidly exploring random tree (RRT) algorithm that combines Q-learning with the Gaussian distribution of obstacles. A route for autonomous vehicles may be swiftly created using this algorithm.Design/methodology/approachThe path planning issue is divided into three key steps by the authors. First, the tree expansion is sped up by the dynamic step size using a combination of Q-learning and the Gaussian distribution of obstacles. The invalid nodes are then removed from the initially created pathways using bidirectional pruning. B-splines are then employed to smooth the predicted pathways.FindingsThe algorithm is validated using simulations on straight and curved highways, respectively. The results show that the approach can provide a smooth, safe route that complies with vehicle motion laws.Originality/valueAn improved RRT algorithm based on Q-learning and obstacle Gaussian distribution (QGD-RRT) is proposed for the path planning of self-driving vehicles. Unlike previous methods, the authors use Q-learning to steer the tree's development direction. After that, the step size is dynamically altered following the density of the obstacle distribution to produce the initial path rapidly and cut down on planning time even further. In the aim to provide a smooth and secure path that complies with the vehicle kinematic and dynamical restrictions, the path is lastly optimized using an enhanced bidirectional pruning technique.

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Section: Dynamic Variable Step Samplingmentioning

confidence: 99%

Section: Bidirectional Pruningmentioning

confidence: 99%

Section: Qgd-rrt Algorithm Frameworkmentioning

confidence: 99%

“…Up to the present, most papers still use unidirectional pruning (Wang et al. , 2021; Yang et al. , 2021) to optimize the generated paths.…”

Section: Path Post-processingmentioning

confidence: 99%

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Research on path planning of autonomous vehicle based on RRT algorithm of Q-learning and obstacle distribution

Shang

Liu

Qin

et al. 2023

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“…Classical shortest path search algorithms include Dijkstra algorithm, A * algorithm, Floyd algorithm, and so on [13]. However, due to real-time changes in the evacuation environment, searching for new evacuation paths is a time-consuming process that can take too long to obtain the new evacuation paths.…”

Section: Introductionmentioning

confidence: 99%

Optimal Emergency Evacuation Route Planning Model Based on Fire Prediction Data

Deng

Zhang

et al. 2022

Mathematics

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For the emergency evacuation of cruise ships in case of sudden fire, this research proposes a dynamic route optimization method based on the improved A* algorithm for real-time information, in order to obtain the real-time optimal evacuation route. Initially, a basic network topology diagram is established according to the internal structure of the cruise ship. Before the occurrence of the accident, the A* algorithm can be applied to obtain an a priori evacuation network consisting of all the optimal routes from each node to the exit. At the time of the accident, the dynamic diffusion of fire can be simulated using Fire Dynamics Simulator (FDS) based on the preliminary information of the fire, so as to estimate the impact of the fire domain on each node of the network. Then, according to the fire dynamic diffusion data, the evacuation route planning is carried out by the improved A* algorithm applying the breadth-first search strategy, so as to determine the optimal route from the current node to the safety exit and to reduce the possibility of casualties due to the uncertainty of the fire during the evacuation. This model allows for both people’s safety and evacuation time to dynamically avoid fire-affected nodes and helps people to reach the safe area as soon as possible. Finally, the evacuation model is established according to the open-source cruise ship structure, and the evacuation process of people under the dynamic spread of cruise ship fire is simulated. The results show that the route planning method proposed in this research works out well in evacuating mass people, which can effectively reduce the evacuation time and improve the safety of the evacuation process.

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