Distributed Event-Triggered Approach for Multi-Agent Formation Based on Cooperative Localization with Mixed Measurements

This paper concentrates on multi-agent formation control problems under mixed measurements of distance and bearing. Towards this objective, a distributed event-triggered estimation-based control framework is developed such that only at necessary time instants, the event for estimation (namely, cooperative localization among a subgroup of agents) is triggered to recover relative position information by utilizing a mixed distance and bearing measurements from different agents. Firstly, it is shown by using the stiffness theory that a subgroup of agents are capable of recovering relative position information if a sufficient number of independent distance and range measurements are available. Secondly, a distributed event-triggered mechanism is presented for achieving an affine formation control, which can be implemented in an asynchronous manner and also ensures Zeno-free behavior. Simulation studies are provided to demonstrate the effective performance of the proposed approach.

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Event-triggered and Self-triggered Formation Control of a Linear Multi-agent System

IEEJ Transactions on Electronics Information and Systems ◽

10.1541/ieejeiss.140.303 ◽

2020 ◽

Vol 140 (3) ◽

pp. 303-311

Author(s):

Ryo Toyota ◽

Kento Kotani ◽

Toru Namerikawa

Keyword(s):

Formation Control ◽

Multi Agent System ◽

Agent System ◽

Multi Agent ◽

Event Triggered

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RoRePo: Detecting the role information and relative position information for contexts in multi-turn dialogue generation

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-202641 ◽

2021 ◽

Vol 40 (5) ◽

pp. 10003-10015

Author(s):

Zibang Gan ◽

Biqing Zeng ◽

Lianglun Cheng ◽

Shuai Liu ◽

Heng Yang ◽

...

Keyword(s):

Relative Position ◽

Latent Variables ◽

Role Ambiguity ◽

Position Information ◽

New Model ◽

Proposed Model ◽

Relevant Context ◽

Position Representation ◽

Current Speaker ◽

Relative Position Information

In multi-turn dialogue generation, dialogue contexts have been shown to have an important influence on the reasoning of the next round of dialogue. A multi-turn dialogue between two people should be able to give a reasonable response according to the relevant context. However, the widely used hierarchical recurrent encoder-decoder model and the latest model that detecting the relevant contexts with self-attention are facing the same problem. Their given response doesn’t match the identity of the current speaker, which we call it role ambiguity. In this paper, we propose a new model, named RoRePo, to tackle this problem by detecting the role information and relative position information. Firstly, as a part of the decoder input, we add a role embedding to identity different speakers. Secondly, we incorporate self-attention mechanism with relative position representation to dialogue context understanding. Besides, the design of our model architecture considers the influence of latent variables in generating more diverse responses. Experimental results of our evaluations on the DailyDialog and DSTC7_AVSD datasets show that our proposed model advances in multi-turn dialogue generation.

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