非ホロノミックロボット向け動的制約付き動作計画ネットワーク
Dynamically Constrained Motion Planning Networks for Non-Holonomic Robots
非ホロノミックロボットのリアルタイム動作計画のため、学習と計画ネットワークを改良したDynamic MPNetを提案し、Dubins carの屋内ナビゲーションで有効性を示した。
著者: Jacob J. Johnson, Linjun Li, Fei Liu, Ahmed H. Qureshi, Michael C. Yip
分類: cs.RO, cs.SY, eess.SY
原文アブストラクト
Reliable real-time planning for robots is essential in today's rapidly expanding automated ecosystem. In such environments, traditional methods that plan by relaxing constraints become unreliable or slow-down for kinematically constrained robots. This paper describes the algorithm Dynamic Motion Planning Networks (Dynamic MPNet), an extension to Motion Planning Networks, for non-holonomic robots that address the challenge of real-time motion planning using a neural planning approach. We propose modifications to the training and planning networks that make it possible for real-time planning while improving the data efficiency of training and trained models' generalizability. We evaluate our model in simulation for planning tasks for a non-holonomic robot. We also demonstrate experimental results for an indoor navigation task using a Dubins car.
