負荷を考慮した情報収集経路計画と物理的サンプリング
LIPP: Load-Aware Informative Path Planning with Physical Sampling
サンプル収集による質量増加が移動コストに影響する状況を考慮した情報収集経路計画問題を新たに定義し、エネルギー予算制約下で訪問位置・順序・サンプル数を最適化するMIQPとして定式化した。
詳しい要約
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2. 先行研究と比べてどこがすごい？
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著者: Hojune Kim, Guangyao Shi, Gaurav S. Sukhatme
分類: cs.RO
原文アブストラクト
In classical Informative Path Planning (C-IPP), robots are typically modeled as mobile sensors that acquire digital measurements such as images or radiation levels. In this model, since making a measurement leaves the robot's physical state unchanged, the cost of traversing an edge remains static regardless of when it is traversed. This is a natural assumption for many missions, but does not extend to settings involving physical sample collection, where each collected sample adds mass and increases the energy cost of all subsequent motion. As a result, IPP formulations that ignore this coupling between information gain and load-dependent traversal cost can produce plans that are distance-efficient but energy-suboptimal, collecting fewer samples and less data than the energy budget would permit. In this paper, we first introduce Load-aware Informative Path Planning (LIPP), a strict generalization of C-IPP that explicitly models this coupling, with C-IPP recovered as the special case of zero sample mass. We then formulate LIPP as a Mixed-Integer Quadratic Program (MIQP) that jointly optimizes visitation location, order, and per-location sampling count under an energy budget. We further derive theoretical bounds on the path-length increase of LIPP relative to C-IPP, characterizing the trade-off for improved energy efficiency. Finally, through extensive simulations across 2,000 diverse mission scenarios, we demonstrate that LIPP progressively achieves higher uncertainty reduction per unit energy as sample mass increases.
