6 papers · 1 filter
Neural-Primitive: An Efficient End-to-end Local Planner with Primitive-based Imitation Learning for Autonomous Flight
Zhitao Liu, Guangtong Xu, Zihan Wang +3
Autonomous flight in unknown cluttered environments is hindered by the computation-quality-memory trilemma of onboard trajectory generation. In this paper, we propose an efficient…
FLIP: Real-Time and Resilient Formation Planning for Large-Scale DIstributed Swarms via Point Cloud Registration
Yuan Zhou, Guangtong Xu, Zhenyu Hou +2
Traditional large-scale formation planning either oversimplify the formation representation which leads to poor performance, or they employ complete collaborative relationships, wh…
Precise Aggressive Aerial Maneuvers with Sensorimotor Policies
Tianyue Wu, Guangtong Xu, Zihan Wang +6
Precise aggressive maneuvers with lightweight onboard sensors remains a key bottleneck in fully exploiting the maneuverability of drones. Such maneuvers are critical for expanding…
Number Adaptive Formation Flight Planning via Affine Deformable Guidance in Narrow Environments
Yuan Zhou, Jialiang Hou, Guangtong Xu +1
Formation maintenance with varying number of drones in narrow environments hinders the convergence of planning to the desired configurations. To address this challenge, this paper…
Flying on Point Clouds with Reinforcement Learning
Guangtong Xu, Tianyue Wu, Zihan Wang +2
A long-cherished vision of drones is to autonomously traverse through clutter to reach every corner of the world using onboard sensing and computation. In this paper, we combine on…
Sparse-Graph-Enabled Formation Planning for Large-Scale Aerial Swarms
Yuan Zhou, Lun Quan, Chao Xu +2
The formation trajectory planning using complete graphs to model collaborative constraints becomes computationally intractable as the number of drones increases due to the curse of…