activity
20212024
most citedNR-RRT: Neural Risk-Aware Near-Optimal Path Planning in Uncertain Nonconvex Environments

44 citations · 63 across the 6 of their papers we have counts for

collaborators

6 papers

cs.RO2024

Online Time-Informed Kinodynamic Motion Planning of Nonlinear Systems

Fei Meng, Jianbang Liu, Haojie Shi +3

Sampling-based kinodynamic motion planners (SKMPs) are powerful in finding collision-free trajectories for high-dimensional systems under differential constraints. Time-informed se…

cs.CV2023

Discrepancy-based Active Learning for Weakly Supervised Bleeding Segmentation in Wireless Capsule Endoscopy Images

Fan Bai, Xiaohan Xing, Yutian Shen +2

Weakly supervised methods, such as class activation maps (CAM) based, have been applied to achieve bleeding segmentation with low annotation efforts in Wireless Capsule Endoscopy (…

cs.RO2022★ 44 cited

NR-RRT: Neural Risk-Aware Near-Optimal Path Planning in Uncertain Nonconvex Environments

Fei Meng, Liangliang Chen, Han Ma +2

Balancing the trade-off between safety and efficiency is of significant importance for path planning under uncertainty. Many risk-aware path planners have been developed to explici…

cs.RO2022★ 1 cited

BiAIT*: Symmetrical Bidirectional Optimal Path Planning with Adaptive Heuristic

Chenming Li, Han Ma, Peng Xu +2

Adaptively Informed Trees (AIT*) is an algorithm that uses the problem-specific heuristic to avoid unnecessary searches, which significantly improves its performance, especially wh…

cs.RO2021★ 17 cited

Enhance Connectivity of Promising Regions for Sampling-based Path Planning

Han Ma, Chenming Li, Jianbang Liu +2

Sampling-based path planning algorithms usually implement uniform sampling methods to search the state space. However, uniform sampling may lead to unnecessary exploration in many…

cs.RO2021★ 1 cited

Relevant Region Sampling Strategy with Adaptive Heuristic for Asymptotically Optimal Path Planning

Chenming Li, Fei Meng, Han Ma +2

Sampling-based planning algorithm is a powerful tool for solving planning problems in high-dimensional state spaces. In this article, we present a novel approach to sampling in the…