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20212026
most citedLearning-based Fast Path Planning in Complex Environments

1 citations · 3 across the 6 of their papers we have counts for

collaborators

6 papers

cs.RO2026

CrossTracer: Cross-Embodiment Navigation via VLA Model Reasoning and Trace Residuals Adapting

Yao Wang, Siyuan Wang, Zhirui Sun +4

Vision-language-action (VLA) models provide strong semantic priors for robot navigation, but they often ignore embodiment-specific mobility constraints. A path that is semantically…

cs.RO2024

Socially Adaptive Path Planning Based on Generative Adversarial Network

Yao Wang, Yuqi Kong, Wenzheng Chi +1

The natural interaction between robots and pedestrians in the process of autonomous navigation is crucial for the intelligent development of mobile robots, which requires robots to…

cs.RO2023★ 1 cited

GVD-Exploration: An Efficient Autonomous Robot Exploration Framework Based on Fast Generalized Voronoi Diagram Extraction

Dingfeng Chen, Anxing Xiao, Meiyuan Zou +3

Rapidly-exploring Random Trees (RRTs) are a popular technique for autonomous exploration of mobile robots. However, the random sampling used by RRTs can result in inefficient and i…

cs.RO2023★ 1 cited

Graph Neural Network Based Method for Path Planning Problem

Xingrong Diao, Wenzheng Chi, Jiankun Wang

Sampling-based path planning is a widely used method in robotics, particularly in high-dimensional state space. Among the whole process of the path planning, collision detection is…

cs.CV2022

3D Object Aided Self-Supervised Monocular Depth Estimation

Songlin Wei, Guodong Chen, Wenzheng Chi +2

Monocular depth estimation has been actively studied in fields such as robot vision, autonomous driving, and 3D scene understanding. Given a sequence of color images, unsupervised…

cs.RO2021★ 1 cited

Learning-based Fast Path Planning in Complex Environments

Jianbang Liu, Baopu Li, Tingguang Li +3

In this paper, we present a novel path planning algorithm to achieve fast path planning in complex environments. Most existing path planning algorithms are difficult to quickly fin…