Zobrazeno 1 - 10
of 140
pro vyhledávání: '"Liu, Qingchen"'
The role of a motion planner is pivotal in quadrotor applications, yet existing methods often struggle to adapt to complex environments, limiting their ability to achieve fast, safe, and robust flight. In this letter, we introduce a performance-enhan
Externí odkaz:
http://arxiv.org/abs/2403.12865
For real-world navigation, it is important to endow robots with the capabilities to navigate safely and efficiently in a complex environment with both dynamic and non-convex static obstacles. However, achieving path-finding in non-convex complex envi
Externí odkaz:
http://arxiv.org/abs/2306.10477
This paper proposes a novel distributed coverage controller for a multi-agent system with constant-speed unicycle robots (CSUR). The work is motivated by the limitation of the conventional method that does not ensure the satisfaction of hard state- a
Externí odkaz:
http://arxiv.org/abs/2304.05723
Quantifying the average communication rate (ACR) of a networked event-triggered stochastic control system (NET-SCS) with deterministic thresholds is challenging due to the non-stationary nature of the system's stochastic processes. For a NET-SCS, the
Externí odkaz:
http://arxiv.org/abs/2301.05445
Publikováno v:
In Journal of the Franklin Institute September 2024 361(13)
Publikováno v:
In Tribology International August 2024 196
Voronoi coverage control is a particular problem of importance in the area of multi-robot systems, which considers a network of multiple autonomous robots, tasked with optimally covering a large area. This is a common task for fleets of fixed-wing Un
Externí odkaz:
http://arxiv.org/abs/2107.14580
Autor:
Le, Nhan Khanh, Liu, Yang, Nguyen, Quang Minh, Liu, Qingchen, Liu, Fangzhou, Cai, Quanwei, Hirche, Sandra
Federated learning is the distributed machine learning framework that enables collaborative training across multiple parties while ensuring data privacy. Practical adaptation of XGBoost, the state-of-the-art tree boosting framework, to federated lear
Externí odkaz:
http://arxiv.org/abs/2106.10662
This paper presents an integrated perception and control approach to accomplish safe autonomous navigation in unknown environments. This is achieved by numerical optimization with constraint learning for instantaneous local control barrier functions
Externí odkaz:
http://arxiv.org/abs/2106.05341
Publikováno v:
International Journal of Robust and Nonlinear Control 2022.32
This paper presents a new formulation for model-free robust optimal regulation of continuous-time nonlinear systems. The proposed reinforcement learning based approach, referred to as incremental adaptive dynamic programming (IADP), exploits measured
Externí odkaz:
http://arxiv.org/abs/2105.01698