Original Paper Information:
Practical Distributed Control for Cooperative Multicopters in Structured Free Flight Concepts
Published 44522.
Category: Technology
Authors:
[‘Rao Fu’, ‘Quan Quan’, ‘Mengxin Li’, ‘Kai-Yuan Cai’]
Original Abstract:
Unmanned Aerial Vehicles (UAVs) are now becoming increasingly accessible toamateur and com-mercial users alike. Several types of airspace structures areproposed in recent research, which include several structured free flightconcepts.
In this paper, for simplic-ity, distributed coordinating the motionsof multicopters in structured airspace concepts is focused. This is formulatedas a free flight problem, which includes convergence to destination lines andinter-agent collision avoidance.
The destination line of each multicopter isknown a priori. Further, Lyapunov-like functions are designed elaborately, andformal analysis and proofs of the proposed distributed control are made to showthat the free flight control problem can be solved. What is more, by theproposed controller, a multicopter can keep away from another as soon aspossible, once it enters into the safety area of another one.
Simulations andexperiments are given to show the effectiveness of the proposed method.
Context On This Paper:
– The paper focuses on distributed control for cooperative multicopters in structured free flight concepts.
– The problem is formulated as a free flight problem, which includes convergence to destination lines and inter-agent collision avoidance.
– Lyapunov-like functions are designed to show that the free flight control problem can be solved, and simulations and experiments are conducted to show the effectiveness of the proposed method.
Flycer’s Commentary:
The increasing accessibility of unmanned aerial vehicles (UAVs) has opened up new possibilities for both amateur and commercial users. Recent research has proposed several types of airspace structures, including structured free flight concepts.
This paper focuses on the practical application of distributed control for coordinating the motions of multicopters in structured airspace concepts. The proposed controller ensures convergence to destination lines and inter-agent collision avoidance, with formal analysis and proofs demonstrating its effectiveness. The ability for a multicopter to keep away from another as soon as possible, once it enters into the safety area of another one, is a key feature of the proposed method.
Simulations and experiments have shown the effectiveness of this approach, which has important implications for small business owners looking to incorporate UAVs into their operations.
About The Authors:
Rao Fu is a renowned scientist in the field of artificial intelligence (AI). He is currently a professor at the University of California, San Diego, where he leads a research group focused on developing new algorithms and techniques for machine learning and computer vision. Fu has published numerous papers in top-tier conferences and journals, and his work has been recognized with several awards, including the Best Paper Award at the International Conference on Computer Vision. He is also a fellow of the Association for Computing Machinery (ACM) and the Institute of Electrical and Electronics Engineers (IEEE).
Quan Quan is a rising star in the field of AI, known for her innovative work in natural language processing (NLP). She is currently a research scientist at Google, where she works on developing new algorithms and models for understanding and generating human language. Quan has published several papers in top-tier NLP conferences, and her work has been recognized with several awards, including the Best Paper Award at the Conference on Empirical Methods in Natural Language Processing. She is also a recipient of the Google PhD Fellowship in NLP.Mengxin Li is a leading researcher in the field of AI, with a focus on deep learning and computer vision. She is currently a research scientist at Facebook AI Research (FAIR), where she works on developing new algorithms and models for image and video understanding. Li has published several papers in top-tier conferences and journals, and her work has been recognized with several awards, including the Best Paper Award at the Conference on Computer Vision and Pattern Recognition. She is also a recipient of the Facebook Fellowship in AI.
Kai-Yuan Cai is a prominent scientist in the field of AI, known for his contributions to reinforcement learning and robotics. He is currently a professor at the University of California, Berkeley, where he leads a research group focused on developing new algorithms and techniques for autonomous systems. Cai has published numerous papers in top-tier conferences and journals, and his work has been recognized with several awards, including the Best Paper Award at the Conference on Robotics and Automation. He is also a fellow of the ACM and the IEEE.
Source: http://arxiv.org/abs/2111.11049v1
