日本フィジカルAI新聞

世界のフィジカルAIを、日本語で。

群制御arXiv:2202.13456

仮想フェロモンと振動音響通信による分散・非同期群ロボット制御

PheroCom: Decentralised and asynchronous swarm robotics coordination based on virtual pheromone and vibroacoustic communication

シェア:XThreadsFacebookLINEはてブBluesky

各ロボットが独立した仮想フェロモンマップを保持し、近傍ロボットとの情報交換をアリの振動音響通信に倣ったゴシップ型で行うことで、中央管理なしに群れのナビゲーションを分散・非同期に調整するモデルを提案し、シミュレーションで有効性を示した。

著者: Claudiney R. Tinoco, Gina M. B. Oliveira

分類: cs.RO, cs.AI, cs.MA, cs.NE

原文アブストラクト

Representation and control of the dynamics of stigmergic substances used by bio-inspired approaches is a challenge when applied to robotics. In order to overcome this challenge, this work proposes a model to coordinate swarms of robots based on the virtualisation and control of these substances in a local scope. The model presents a new pheromone modelling, which enables the decentralisation and asynchronicity of navigation decisions. Each robot maintains an independent virtual pheromone map, which is continuously updated with the robot's deposits and pheromone evaporation. Moreover, the individual pheromone map is also updated by aggregating information from other robots that are exploring nearby areas. Thus, individual and independent maps replace the need of a centralising agent that controls and distributes the pheromone information, which is not always practicable. Pheromone information propagation is inspired by ants' vibroacoustic communication, which, in turn, is characterised as an indirect communication through a type of gossip protocol. The proposed model was evaluated through an agent simulation software, implemented by the authors, and in the Webots platform. Experiments were carried out to validate the model in different environments, with different shapes and sizes, as well as varying the number of robots. The analysis of the results has shown that the model was able to perform the coordination of the swarm, and the robots have exhibited an expressive performance executing the surveillance task.

関連論文

  1. 1パナソニックHDら、ロボット操作AI「T-Rex」を共同開発 12タスクで平均成功率65%研究・技術
  2. 2Hugging Face、ロボット学習基盤「LeRobot v0.7.0」を公開 対話操作と大規模データ学習に対応新製品・発表
  3. 3NVIDIA、Vera CPUのエージェント処理性能を公表 x86比で速度・電力効率を比較半導体・GPU
  4. 4Agility、SPAC合併でナスダック上場資金調達
  5. 5AGIBOTのG2 6台、上海のコンビニで24時間試験 1,730タスクを実施量産・展開
PR本紙発行元 EmplifAI