paper-with-me

홈 › Papers

Multi-Agent Coordination Fluid Flow Modeling and Experimental Evaluation

2023-01-14 · Harshvardhan Uppaluru, Mohammad Ghuran, Hossein Rastgoftar

Reliability is a critical aspect of multi-agent system coordination as it ensures that the system functions correctly and consistently. If one agent in the system fails or behaves unexpectedly, it can negatively impact the performance and effectiveness of the entire system. Therefore, it is important to design and implement multi-agent systems with a high level of reliability to ensure that they can operate safely and move smoothly in the presence of unforeseen agent failure or lack of communication with some agent teams moving in a shared motion space. This paper presents a novel fluid flow navigation model that, in an ideal fluid flow, divides agents into cooperative (non-singular) and noncooperative (singular) agents, with cooperative agents sliding along streamlines safely enclosing noncooperative agents in a shared motion space. A series of flight experiments utilizing crazyflie quadcopters will experimentally validate the suggested model.

📄 PDF Abstract BibTeX arXiv:2301.05833

Code (0)

등록된 구현이 없습니다.

Similar Papers 제목 키워드 기반

FISC: A Fluid-Inspired Framework for Decentralized and Scalable Swarm Control

2026-01-31 · Mohini Priya Kolluri, Ammar Waheed, Zohaib Hasnain arxiv

Achieving scalable coordination in large robotic swarms is often constrained by reliance on inter-agent communication, which introduces latency, bandwidth limitations, and vulnerability to failure. To address this gap, a…

Can a Laplace PDE Define Air Corridors through Low-Altitude Airspace?

2023-04-05 · Aeris El Asslouj, Ella Atkins, Hossein Rastgoftar

This paper develops a high-density air corridor traffic flow model for Uncrewed Aircraft System (UAS) operation in urban low altitude airspace. To maximize throughput with safe separation guarantees, we define an airspac…

Parameter-Conditioned Sequential Generative Modeling of Fluid Flows

2019-12-14 · Jeremy Morton, Freddie D. Witherden, Mykel J. Kochenderfer

The computational cost associated with simulating fluid flows can make it infeasible to run many simulations across multiple flow conditions. Building upon concepts from generative modeling, we introduce a new method for…

Importance of equivariant and invariant symmetries for fluid flow modeling

2023-05-03 · Varun Shankar, Shivam Barwey, Zico Kolter, Romit Maulik 외

Graph neural networks (GNNs) have shown promise in learning unstructured mesh-based simulations of physical systems, including fluid dynamics. In tandem, geometric deep learning principles have informed the development o…

CoFlow: Coordinated Few-Step Flow for Offline Multi-Agent Decision Making

2026-05-02 · Guowei Zou, Haitao Wang, Beiwen Zhang, Boning Zhang 외 arxiv

Generative models have emerged as a promising paradigm for offline multi-agent reinforcement learning (MARL), but existing approaches require many iterative sampling steps. Recent few-step acceleration methods either dis…

Multi-agent Reinforcement LearningDecision Making