Learning Connectivity for Data Distribution in Robot Teams

Tolstaya, Ekaterina; Landon, Butler,; Mox, Daniel; Paulos, James; Kumar, Vijay; Ribeiro, Alejandro

doi:10.1109/iros51168.2021.9636873

Cited by 6 publications

(8 citation statements)

References 18 publications

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“…This could be mitigated by accounting for the time delays between messages. Further study is needed on the impact of message delays and packet loss [16].…”

Section: Discussionmentioning

confidence: 99%

“…This formulation has been used to solve for robot localization and control with Gaussian Belief Propagation [7]. Graphical representations have also been used to learn factors for robot control via graph neural networks [6], [16]. This technique requires expert trajectory demonstrations from a centralized controller for training.…”

Section: A Multi-robot Coordinationmentioning

confidence: 99%

See 1 more Smart Citation

Parts-Based Articulated Object Localization in Clutter Using Belief Propagation

Pavlasek

Lewis

Desingh

et al. 2020

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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Decentralized coordination for multi-robot systems involves planning in challenging, high-dimensional spaces. The planning problem is particularly challenging in the presence of obstacles and different sources of uncertainty such as inaccurate dynamic models and sensor noise. In this paper, we introduce Stein Variational Belief Propagation (SVBP), a novel algorithm for performing inference over nonparametric marginal distributions of nodes in a graph. We apply SVBP to multi-robot coordination by modelling a robot swarm as a graphical model and performing inference for each robot. We demonstrate our algorithm on a simulated multi-robot perception task, and on a multi-robot planning task within a Model-Predictive Control (MPC) framework, on both simulated and real-world mobile robots. Our experiments show that SVBP represents multi-modal distributions better than sampling-based or Gaussian baselines, resulting in improved performance on perception and planning tasks. Furthermore, we show that SVBP's ability to represent diverse trajectories for decentralized multi-robot planning makes it less prone to deadlock scenarios than leading baselines.

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“…This could be mitigated by accounting for the time delays between messages. Further study is needed on the impact of message delays and packet loss [16].…”

Section: Discussionmentioning

confidence: 99%

Section: A Multi-robot Coordinationmentioning

confidence: 99%

Parts-Based Articulated Object Localization in Clutter Using Belief Propagation

Pavlasek

Lewis

Desingh

et al. 2020

2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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“…As for RANETs, Tolstaya et al propose a graph neural network based method for data distribution in a robot ad hoc network [55]. The robots extract network topology information from packets transmitting on the network and feed it to a graph neural network, that output when and where to communicate the most recent state information.…”

Section: ) Ranets Routing and Protocolsmentioning

confidence: 99%

Multi-Robot Systems and Cooperative Object Transport: Communications, Platforms, and Challenges

Lin

et al. 2023

IEEE Open J. Comput. Soc.

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Multi-robot systems gain considerable attention due to lower cost, better robustness, and higher scalability as compared with single-robot systems. Cooperative object transport, as a well-known use case of multi-robot systems, shows great potential in real-world applications. The design and implementation of a multi-robot system involve many technologies, specifically, communication, coordination, task allocation methods, experimental platforms, and simulators. However, most of recent multi-robot system studies focus on coordination and task allocation problems, with little focus on communications among multiple robots. In this review, we focus on the communication, validation platform, and simulator of multi-robot systems, and discuss one of the important applications, cooperative object transport. First, we study the multi-robot system fundamentals and comprehensively review the multi-robot system communication technologies. Then, the multi-robot system validating platform, testbed, simulator, and middleware used in academia and industry are investigated. Finally, we discuss recent advances in cooperative object transport, and challenges and possible future research directions for multi-robot systems.

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“…[ 13 ] However, robots can obtain more data that cannot be observed in the local field of view from others by transferring information, so communication‐based methods are often better than noncommunication methods. [ 14 ]…”

Section: Introductionmentioning

confidence: 99%

“…[13] However, robots can obtain more data that cannot be observed in the local field of view from others by transferring information, so communication-based methods are often better than noncommunication methods. [14] Among the communication-based methods, many studies use broadcast-based transmission of information. [15][16][17][18] Although the broadcast style is easy to implement and deploy in the actual scene, it often generates a large amount of redundant information that leads to unnecessary consumption of network and data processing.…”

Section: Introductionmentioning

confidence: 99%

Dynamic Motion Planning Model for Multirobot Using Graph Neural Network and Historical Information

Qiu

2023

Advanced Intelligent Systems

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In order to effectively improve the path‐finding capability of a multirobot system in a decentralized control approach, a dynamic motion planning model based on graph neural networks and historical information (GNNHIM) is proposed. Due to the limited sensing range of the robot's onboard sensors, GNNHIM uses convolutional neural networks to extract features from the heterogeneous environmental information collected and analyzes the motion trends of other robots in conjunction with the historical path information stored locally. After finishing feature fusion, each robot exchanges its feature vectors with other robots still within sensing range to obtain the next action by the graph neural network model processing. Herein, imitation learning is used to train robots to choose behavioral strategies to maximize team benefit. The experimental results show that GNNHIM can make path planning for multirobot systems more efficient and reliable, enabling all robots to reach their own goal within a limited time in 95.1% of the experimental dataset. Moreover, GNNHIM still has great generalization when applied to unseen scenarios and larger scale robot systems. GNNHIM in unseen scenarios has improved the success rate by 6%‐34% and reduced the detour percentage by 3.8% on average compared to the other scheduling model.

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Learning Connectivity for Data Distribution in Robot Teams

Cited by 6 publications

References 18 publications

Parts-Based Articulated Object Localization in Clutter Using Belief Propagation

Parts-Based Articulated Object Localization in Clutter Using Belief Propagation

Multi-Robot Systems and Cooperative Object Transport: Communications, Platforms, and Challenges

Dynamic Motion Planning Model for Multirobot Using Graph Neural Network and Historical Information

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