不確実性下における複数ロボットの分散制御のための計画
Planning for Decentralized Control of Multiple Robots Under Uncertainty
環境・センサモデルとコスト関数が与えられた一般のマルチロボット系に対し、Dec-POMDPとマクロ行動を用いて分散制御ポリシーを合成する枠組みを提案し、倉庫タスクの3変種でタスク割当・直接通信・シグナリングを適宜用いた協調行動を単一プランナで生成できることを示した。
著者: Christopher Amato, George D. Konidaris, Gabriel Cruz, Christopher A. Maynor, Jonathan P. How, Leslie P. Kaelbling
分類: cs.RO, cs.AI, cs.MA
原文アブストラクト
We describe a probabilistic framework for synthesizing control policies for general multi-robot systems, given environment and sensor models and a cost function. Decentralized, partially observable Markov decision processes (Dec-POMDPs) are a general model of decision processes where a team of agents must cooperate to optimize some objective (specified by a shared reward or cost function) in the presence of uncertainty, but where communication limitations mean that the agents cannot share their state, so execution must proceed in a decentralized fashion. While Dec-POMDPs are typically intractable to solve for real-world problems, recent research on the use of macro-actions in Dec-POMDPs has significantly increased the size of problem that can be practically solved as a Dec-POMDP. We describe this general model, and show how, in contrast to most existing methods that are specialized to a particular problem class, it can synthesize control policies that use whatever opportunities for coordination are present in the problem, while balancing off uncertainty in outcomes, sensor information, and information about other agents. We use three variations on a warehouse task to show that a single planner of this type can generate cooperative behavior using task allocation, direct communication, and signaling, as appropriate.
