能動粒子の最適ナビゲーションと機械学習の融合
Optimal active particle navigation meets machine learning
昆虫や微生物、将来のコロイドロボットなどの能動エージェントが複雑環境で目標に最適に到達するためのナビゲーション問題について、マイクロからマクロスケールまでの最近の発展を概観し、機械学習ベースの手法に焦点を当てて今後の課題を展望したレビュー論文。
著者: Mahdi Nasiri, Hartmut Löwen, Benno Liebchen
分類: cond-mat.soft, cond-mat.stat-mech, cs.LG, cs.RO
原文アブストラクト
The question of how "smart" active agents, like insects, microorganisms, or future colloidal robots need to steer to optimally reach or discover a target, such as an odor source, food, or a cancer cell in a complex environment has recently attracted great interest. Here, we provide an overview of recent developments, regarding such optimal navigation problems, from the micro- to the macroscale, and give a perspective by discussing some of the challenges which are ahead of us. Besides exemplifying an elementary approach to optimal navigation problems, the article focuses on works utilizing machine learning-based methods. Such learning-based approaches can uncover highly efficient navigation strategies even for problems that involve e.g. chaotic, high-dimensional, or unknown environments and are hardly solvable based on conventional analytical or simulation methods.