Learning to Cooperate via an Attention-Based Communication Neural Network in Decentralized Multi-Robot Exploration

Geng, Mingyang; Xu, Kele; Zhou, Xing; Ding, Bo; Wang, Huaimin; Zhang, Lei

doi:10.3390/e21030294

Cited by 22 publications

(10 citation statements)

References 24 publications

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“…Without a centralized controller, decentralized agents cannot learn effective and decentralized cooperation policies in a complex environment. Geng et al proposed a new attention-based communication neural network (CommAttn) [28]. CommAttn can use the display communication method to automatically learn and explore the cooperation policies in the problem, by modeling the interaction between agents and introducing the attention mechanism.…”

Section: Selective and Targeted Communication Through The Use Of Attention Mechanismmentioning

confidence: 99%

Cooperative Multiagent Attentional Communication for Large-Scale Task Space

Zou

et al. 2022

Wireless Communications and Mobile Computing

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With the rapid development of mobile robots, they have begun to be widely used in industrial manufacturing, logistics scheduling, intelligent medical, and other fields. For large-scale task space, the communication between multiagents is the key to affect cooperation productivity, and agents can coordinate more effectively with the help of dynamic communication. However, the traditional communication mechanism uses simple message aggregation and broadcast and, in some cases, lacks the distinction of the importance of information. Multiagent deep reinforcement learning (MDRL) is valid to solve the problem of informational coordination strategies. However, how different messages affect each agent’s decision-making process remains a challenging task for large-scale task. To solve this problem, we propose IMANet (Import Message Attention Network). It divides the decision-making process into two substages: communication and action, where communication is considered to be part of the environment. First, an attention mechanism based on query vectors is introduced. The correlation between the query vector agent’s own information and the current state information of other agents is estimated, and then, the results are used to distinguish the importance of information from other agents. Second, the LSTM network is used as the unit controller for each agent, and individual rewards are used to guide the agent training after communication. Finally, IMANet is evaluated on tasks on challenging multi-agent platforms, Predator and Prey (PP), and traffic junction. The results show that IMANet can improve the efficiency of learning and training, especially when applied to large-scale task space, with a success rate 12% higher than CommNet in baseline experiments.

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Section: Selective and Targeted Communication Through The Use Of Attention Mechanismmentioning

confidence: 99%

Cooperative Multiagent Attentional Communication for Large-Scale Task Space

Zou

et al. 2022

Wireless Communications and Mobile Computing

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“…4), which was designed to communicate to the readers that AI models increased in popularity among NDM. In general, the ten most commonly used applications are artificial neural network (ANN) (Chau et al 2005), support vector (Jiao et al 2016), fuzzy logic (FL) (Rodríguez et al 2011), regress algorithm (RA) (Ragettli et al 2017), genetic algorithm (GA) (Zhou et al 2019), random forest (RF) (Wang et al 2015), robotics (Geng et al 2019), bays (Cheng et al 2016), extreme learning machine (ELM) (Li et al 2017) and decision tree (DT) (Choi et al 2018). Among the retrieved articles, the proportion of the ten most commonly used applications reached 72.26%.…”

Section: Statistical Analysis Of Included Articlesmentioning

confidence: 99%

“…Chatterjee et al (2005) described rescue robot features in the context of search and rescue effort coordination. Geng et al (2019) presented a novel approach, the attentionbased communication neural network, to simulate the cooperation strategies automatically in multi-robot exploration problems.…”

Section: Natural Disastermentioning

confidence: 99%

Can we detect trends in natural disaster management with artificial intelligence? A review of modeling practices

et al. 2020

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There has been an unsettling rise in the intensity and frequency of natural disasters due to climate change and anthropogenic activities. Artificial intelligence (AI) models have shown remarkable success and superiority to handle huge and nonlinear data owing to their higher accuracy and efficiency, making them perfect tools for disaster monitoring and management. Accordingly, natural disaster management (NDM) with the usage of AI models has received increasing attention in recent years, but there has been no systematic review so far. This paper presents a systematic review on how AI models are applied in different NDM stages based on 278 studies retrieved from Elsevier Science, Springer LINK and Web of Science. The review: (1) enables increased visibility into various disaster types in different NDM stages from the methodological and content perspective, (2) obtains many general results including the practicality and gaps of extant studies and (3) provides several recommendations to develop innovative AI models and improve the quality of modeling. Overall, a comprehensive assessment and evaluation for the reviewed studies are performed, which tracked all stages of NDM research with the applications of AI models.

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“…Meanwhile, Faster R-CNN was also applied to medical anatomy [29] and crop identification [30]. However, industrial robots such as electric welding robots, picking robots [31], grasping robots [32], handling robots [33] and multi-robots [34] all use point-to-point basic vision technology.…”

Section: Introductionmentioning

confidence: 99%

Object Detection Method for Grasping Robot Based on Improved YOLOv5

Song

Bai

et al. 2021

Micromachines

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In the industrial field, the anthropomorphism of grasping robots is the trend of future development, however, the basic vision technology adopted by the grasping robot at this stage has problems such as inaccurate positioning and low recognition efficiency. Based on this practical problem, in order to achieve more accurate positioning and recognition of objects, an object detection method for grasping robot based on improved YOLOv5 was proposed in this paper. Firstly, the robot object detection platform was designed, and the wooden block image data set is being proposed. Secondly, the Eye-In-Hand calibration method was used to obtain the relative three-dimensional pose of the object. Then the network pruning method was used to optimize the YOLOv5 model from the two dimensions of network depth and network width. Finally, the hyper parameter optimization was carried out. The simulation results show that the improved YOLOv5 network proposed in this paper has better object detection performance. The specific performance is that the recognition precision, recall, mAP value and F1 score are 99.35%, 99.38%, 99.43% and 99.41% respectively. Compared with the original YOLOv5s, YOLOv5m and YOLOv5l models, the mAP of the YOLOv5_ours model has increased by 1.12%, 1.2% and 1.27%, respectively, and the scale of the model has been reduced by 10.71%, 70.93% and 86.84%, respectively. The object detection experiment has verified the feasibility of the method proposed in this paper.

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Learning to Cooperate via an Attention-Based Communication Neural Network in Decentralized Multi-Robot Exploration

Cited by 22 publications

References 24 publications

Cooperative Multiagent Attentional Communication for Large-Scale Task Space

Cooperative Multiagent Attentional Communication for Large-Scale Task Space

Can we detect trends in natural disaster management with artificial intelligence? A review of modeling practices

Object Detection Method for Grasping Robot Based on Improved YOLOv5

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