極限未知環境における学習・制御された自律ロボット探査
Learned and Controlled Autonomous Robotic Exploration in an Extreme, Unknown Environment
モデルベース遺伝的アルゴリズムを用いて、未知の極限地形でのテラメカニクスモデルと制御方策をオンライン学習し、解釈可能なモデルを生成する手法を提案した論文。
著者: Frances Zhu, D. Sawyer Elliott, ZhiDi Yang, Haoyuan Zheng
分類: cs.RO
原文アブストラクト
Exploring and traversing extreme terrain with surface robots is difficult, but highly desirable for many applications, including exploration of planetary surfaces, search and rescue, among others. For these applications, to ensure the robot can predictably locomote, the interaction between the terrain and vehicle, terramechanics, must be incorporated into the model of the robot's locomotion. Modeling terramechanic effects is difficult and may be impossible in situations where the terrain is not known a priori. For these reasons, learning a terramechanics model online is desirable to increase the predictability of the robot's motion. A problem with previous implementations of learning algorithms is that the terramechanics model and corresponding generated control policies are not easily interpretable or extensible. If the models were of interpretable form, designers could use the learned models to inform vehicle and/or control design changes to refine the robot architecture for future applications. This paper explores a new method for learning a terramechanics model and a control policy using a model-based genetic algorithm. The proposed method yields an interpretable model, which can be analyzed using preexisting analysis methods. The paper provides simulation results that show for a practical application, the genetic algorithm performance is approximately equal to the performance of a state-of-the-art neural network approach, which does not provide an easily interpretable model.
