重み付き最大化による多目的ロボット計画問題のスカラー化
Scalarizing Multi-Objective Robot Planning Problems using Weighted Maximization
ロボットの動作計画で複数の目的を扱う際、従来の重み付き和ではなく重み付き最大を用いるコスト関数を提案し、連続・離散空間の計画アルゴリズムを開発して、より広いトレードオフ（パレート最適解）を発見できることを示した。
著者: Nils Wilde, Stephen L. Smith, Javier Alonso-Mora
分類: cs.RO
原文アブストラクト
When designing a motion planner for autonomous robots there are usually multiple objectives to be considered. However, a cost function that yields the desired trade-off between objectives is not easily obtainable. A common technique across many applications is to use a weighted sum of relevant objective functions and then carefully adapt the weights. However, this approach may not find all relevant trade-offs even in simple planning problems. Thus, we study an alternative method based on a weighted maximum of objectives. Such a cost function is more expressive than the weighted sum, and we show how it can be deployed in both continuous- and discrete-space motion planning problems. We propose a novel path planning algorithm for the proposed cost function and establish its correctness, and present heuristic adaptations that yield a practical runtime. In extensive simulation experiments, we demonstrate that the proposed cost function and algorithm are able to find a wider range of trade-offs between objectives (i.e., Pareto-optimal solutions) for various planning problems, showcasing its advantages in practice.
