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Decentralized Online Simultaneous Localization and Mapping for Multi-Agent Systems

Andrés Jiménez, Vicente García-Díaz, Rubén González-Crespo, Sandro Bolaños
2018 Sensors  
This article presents a design for a system capable of a decentralized implementation of SLAM that is based on the use of a system comprised of wireless agents capable of storing and distributing the map  ...  To solve it, the RA must execute the task known as Simultaneous Location and Mapping (SLAM) which locates the agent in the new environment while generating the map at the same time, geometrically or topologically  ...  Localization and Multi-Agent Mapping System Agents Description The multi-agent system is comprised of a single RA and multiple WAs.  ... 
doi:10.3390/s18082612 pmid:30096931 fatcat:i7e63rbfbzcfddz4lonqsyteka

Deep Reinforcement Learning for Decentralized Multi-Robot Exploration with Macro Actions [article]

Aaron Hao Tan, Federico Pizarro Bejarano, Goldie Nejat
2021 arXiv   pre-print
In this paper, we present the first Macro Action Decentralized Exploration Network (MADE-Net) using multi-agent deep reinforcement learning to address the challenges of communication dropouts during multi-robot  ...  Cooperative multi-robot teams need to be able to explore cluttered and unstructured environments together while dealing with communication challenges.  ...  CONCLUSION In this paper, we present a novel multi-agent DRL architecture for decentralized multi-robot exploration for cluttered and unstructured environments to address the challenge of communication  ... 
arXiv:2110.02181v1 fatcat:jcsgrvlxwfeotnashpyf6yomfy

An Online Optimization Approach for Multi-Agent Tracking of Dynamic Parameters in the Presence of Adversarial Noise [article]

Shahin Shahrampour, Ali Jadbabaie
2017 arXiv   pre-print
Finally, in a numerical experiment, we verify that our algorithm can be simply implemented for multi-agent tracking with nonlinear observations.  ...  The location of the target at each time induces a global time-varying loss function, and the global loss is a sum of local losses, each of which is associated to one agent.  ...  Technical Assumptions To solve the multi-agent online optimization (4), we propose to decentralize the Mirror Descent algorithm [14] .  ... 
arXiv:1702.06219v1 fatcat:bdz7ugmqmncldbzgwemnh4wlp4

Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep Reinforcement Learning [article]

Pinxin Long, Tingxiang Fan, Xinyi Liao, Wenxi Liu, Hao Zhang, Jia Pan
2018 arXiv   pre-print
We present a decentralized sensor-level collision avoidance policy for multi-robot systems, which directly maps raw sensor measurements to an agent's steering commands in terms of movement velocity.  ...  While other distributed multi-robot collision avoidance systems exist, they often require extracting agent-level features to plan a local collision-free action, which can be computationally prohibitive  ...  We present a decentralized sensor-level collision avoidance policy for multi-robot systems, which directly maps raw sensor measurements to an agent's steering commands in terms of movement velocity.  ... 
arXiv:1709.10082v3 fatcat:gr3rzog2fzglffmpgafqanyovy

COVINS: Visual-Inertial SLAM for Centralized Collaboration [article]

Patrik Schmuck, Thomas Ziegler, Marco Karrer, Jonathan Perraudin, Margarita Chli
2021 arXiv   pre-print
Collaborative SLAM enables a group of agents to simultaneously co-localize and jointly map an environment, thus paving the way to wide-ranging applications of multi-robot perception and multi-user AR experiences  ...  This article presents COVINS, a novel collaborative SLAM system, that enables multi-agent, scalable SLAM in large environments and for large teams of more than 10 agents.  ...  online during a mission, through co-localization and joint creation of a global map of the environment.  ... 
arXiv:2108.05756v1 fatcat:ia55vrb2bzatliq35iu76jdcru

Collective Online Learning of Gaussian Processes in Massive Multi-Agent Systems

Trong Nghia Hoang, Quang Minh Hoang, Kian Hsiang Low, Jonathan How
This paper presents a novel Collective Online Learning of Gaussian Processes (COOL-GP) framework for enabling a massive number of GP inference agents to simultaneously perform (a) efficient online updates  ...  , which is amenable to both an efficient online update via an importance sampling trick as well as multi-agent model fusion via decentralized message passing that can exploit sparse connectivity among  ...  This research was funded in part by the Army Research Laboratory and was accomplished under Cooperative Agreement Number W911NF-17-2-0181 and by ONR under the BRC N00014-17-1-2072.  ... 
doi:10.1609/aaai.v33i01.33017850 fatcat:mphngbpig5ckbe4l6ijz2l4kfu

Blockchain Design for an Embedded System

Sara Falcone, John Zhang, Agnes Cameron, Amira Abdel-Rahman
2019 Ledger  
This paper proposes a blockchain-based mapping protocol for distributed robotic systems running on embedded hardware.  ...  Options, trade-offs and considerations for implementing blockchain technology on an embedded system with wireless radio communication are explored and discussed.  ...  Robertson and W. Langford for their feedback and suggestions on this project. This work was supported by the Center for Bits and Atoms research consortia; thank you!  ... 
doi:10.5195/ledger.2019.172 fatcat:hc5hlodqongbbeuuuduzqykvrm

Distributed value functions for multi-robot exploration

Laetitia Matignon, Laurent Jeanpierre, Abdel-Illah Mouaddib
2012 2012 IEEE International Conference on Robotics and Automation  
The localization aspect is not considered and it is assumed the robots share their positions and have access to a map updated with all explored areas.  ...  A key problem is then the coordination of decentralized decision processes: each individual robot must choose appropriate exploration goals so that the team simultaneously explores different locations  ...  ACKNOWLEDGEMENTS This work has been supported by the NRA (French National Research Agency) and the DGA (Defense Procurement Agency) (ANR-09-CORD-103) and jointly developed with the members of the Robots  ... 
doi:10.1109/icra.2012.6224937 dblp:conf/icra/MatignonJM12 fatcat:tly7tefrnnaklkiuemfjrdugsi

Survey of Recent Multi-Agent Reinforcement Learning Algorithms Utilizing Centralized Training [article]

Piyush K. Sharma, Rolando Fernandez, Erin Zaroukian, Michael Dorothy, Anjon Basak, Derrik E. Asher
2021 arXiv   pre-print
Much work has been dedicated to the exploration of Multi-Agent Reinforcement Learning (MARL) paradigms implementing a centralized learning with decentralized execution (CLDE) approach to achieve human-like  ...  The goal is to explore how different implementations of information sharing mechanism in centralized learning may give rise to distinct group coordinated behaviors in multi-agent systems performing cooperative  ...  The U.S. government is authorized to reproduce and distribute reprints for government purposes notwithstanding any copyright notation herein.  ... 
arXiv:2107.14316v1 fatcat:n7qmmwwdenfbdngkmzflsqcx7y

Distributed Heuristic Multi-Agent Path Finding with Communication [article]

Ziyuan Ma, Yudong Luo, Hang Ma
2021 arXiv   pre-print
Multi-Agent Path Finding (MAPF) is essential to large-scale robotic systems.  ...  Our method treats each agent independently and trains the model from a single agent's perspective. The final trained policy is applied to each agent for decentralized execution.  ...  ACKNOWLEDGMENT This work was supported by Natural Sciences and Engineering Research Council under grant RGPIN-2020-06540.  ... 
arXiv:2106.11365v1 fatcat:oar37oedpzb77mflmnnfx7pmuu

Provably Efficient Multi-Agent Reinforcement Learning with Fully Decentralized Communication [article]

Justin Lidard, Udari Madhushani, Naomi Ehrich Leonard
2021 arXiv   pre-print
Specifically, we consider a class of online, episodic, tabular Q-learning problems under time-varying reward and transition dynamics, in which agents can communicate in a decentralized manner.We show that  ...  Distributed exploration reduces sampling complexity in multi-agent RL (MARL). We investigate the benefits to performance in MARL when exploration is fully decentralized.  ...  For continuous state and action spaces, [20] provides a decentralized multi-agent regret bound for linear-quadratic systems for unknown dynamics and a one-directional communication from the agent controlling  ... 
arXiv:2110.07392v1 fatcat:5uqgwiewdbgmlbjausnnow5e54

A Survey and Analysis of Multi-Robot Coordination

Zhi Yan, Nicolas Jouandeau, Arab Ali Cherif
2013 International Journal of Advanced Robotic Systems  
In the field of mobile robotics, the study of multi-robot systems (MRSs) has grown significantly in size and importance in recent years.  ...  This paper presents a systematic survey and analysis of the existing literature on coordination, especially in multiple mobile robot systems (MMRSs).  ...  and other uncertainties; 3) produces a highly vulnerable system, and if the central control agent malfunctions a new agent must be available or else the Decentralized architectures can be further divided  ... 
doi:10.5772/57313 fatcat:lj4hv3wxqrdmfc4mzph76q76qq

A survey on multi-robot coverage path planning for model reconstruction and mapping

Randa Almadhoun, Tarek Taha, Lakmal Seneviratne, Yahya Zweiri
2019 SN Applied Sciences  
In this paper, we surveyed the research topics related to multi-robot CPP for the purpose of mapping and model reconstructions.  ...  There has been an increasing interest in researching, developing and deploying multi-robot systems.  ...  The algorithm starts by generating an elevation map using modified visual Simultaneous Localization and Mapping (SLAM) and meshing approach.  ... 
doi:10.1007/s42452-019-0872-y fatcat:y6hkwvnapnfpnax3k7xnqkj2gi

Decentralized Reinforcement Learning for the Online Optimization of Distributed Systems [chapter]

Jim Dowling, Seif Haridi
2008 Reinforcement Learning Decentralized Reinforcement Learning for the Online Optimization of Distributed Systems  ...  The authors would like to thank Jan Sacha for an implementation of CRL in Java on which the experiments in this paper are based.  ...  Distributed reinforcement learning problem definition Goldman and Zilberstein characterize multi-agent reinforcement learning as a decentralized control problem for stochastic systems (Goldman and Zilberstein  ... 
doi:10.5772/5279 fatcat:vkmpk3jmincerc2e3i4z3cjvz4

Distributed Learning of Decentralized Control Policies for Articulated Mobile Robots [article]

Guillaume Sartoretti and William Paivine and Yunfei Shi and Yue Wu and Howie Choset
2019 arXiv   pre-print
We present results of closed-loop locomotion in unstructured terrains on a snake and a hexapod robot, using decentralized controllers learned offline and online respectively.  ...  State-of-the-art distributed algorithms for reinforcement learning rely on multiple independent agents, which simultaneously learn in parallel environments while asynchronously updating a common, shared  ...  ACKNOWLEDGMENT We acknowledge the support of the National Science Foundation on this project as part of the "Shaky Perception" grant, as well as the 2017 CMU RISS and SURF programs.  ... 
arXiv:1901.08537v2 fatcat:cf7rz75sf5f23ikqlf6y7lutji
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