6 citations · 16 across the 18 of their papers we have counts for
6 papers · 1 filter
Dynamic-TD3: A Novel Algorithm for UAV Path Planning with Dynamic Obstacle Trajectory Prediction
Wentao Chen, Jingtang Chen, Mingjian Fu +4
Deep reinforcement learning (DRL) finds extensive application in autonomous drone navigation within complex, high-risk environments. However, its practical deployment faces a safet…
A Vision-based Irregular Obstacle Avoidance Framework via Deep Reinforcement Learning
Lingping Gao, Jianchuan Ding, Wenxi Liu +4
Deep reinforcement learning has achieved great success in laser-based collision avoidance work because the laser can sense accurate depth information without too much redundant dat…
Crowd-Driven Mapping, Localization and Planning
Tingxiang Fan, Dawei Wang, Wenxi Liu +1
Navigation in dense crowds is a well-known open problem in robotics with many challenges in mapping, localization, and planning. Traditional solutions consider dense pedestrians as…
Learning Resilient Behaviors for Navigation Under Uncertainty
Tingxiang Fan, Pinxin Long, Wenxi Liu +3
Deep reinforcement learning has great potential to acquire complex, adaptive behaviors for autonomous agents automatically. However, the underlying neural network polices have not…
Getting Robots Unfrozen and Unlost in Dense Pedestrian Crowds
Tingxiang Fan, Xinjing Cheng, Jia Pan +4
We aim to enable a mobile robot to navigate through environments with dense crowds, e.g., shopping malls, canteens, train stations, or airport terminals. In these challenging envir…
Fully Distributed Multi-Robot Collision Avoidance via Deep Reinforcement Learning for Safe and Efficient Navigation in Complex Scenarios
Tingxiang Fan, Pinxin Long, Wenxi Liu +1
In this paper, we present a decentralized sensor-level collision avoidance policy for multi-robot systems, which shows promising results in practical applications. In particular, o…