Masao SUGI (杉 正夫)

Offer Organization: Japan Society for the Promotion of Science, System Name: Grants-in-Aid for Scientific Research, Category: Grant-in-Aid for Scientific Research (C), Fund Type: -, Overall Grant Amount: - (direct: 3600000, indirect: 450000)
In former researches in mufti-robotics, in spite of its importance, there have been no propositions of task allocation scheme toward the problem involving "task constraints" that impose some restriction on the orders of task execution. In other studies involving task constraints such as job shop scheduling, task constraints are given by the designer. We address the problem that robots should calculate task constraints by themselves and should execute task allocation considering their constraints. As an example of such problem, we adopted rearrangement task of multiple movable objects. In this research, following subjects should be solved.
1. Task allocation should be executed considering task constraints.
2. Task constraints should be calculated as early as possible.
Toward these subjects, we have obtained following results.
1. By means of classification of task constraints, we have obtained calculation methods of task constraints and task allocation methods considering these constraints.
2. We have promoted efficiency of calculation procedure of task constraints. First, we have analyzed calculation procedure from viewpoints when calculation should be done and how long it should be taken. Next, we have listed all possible procedure. Finally, we have tested these procedures in simulated environments. As a result, following method turns out to be the most effective: In every time of task allocation, robots should calculate some constraints that costs comparatively low calculation costs. And robots should calculate other constraints that cost comparatively high calculation costs only when robots fail to planned tasks.
In future works, more flexible framework that divides given task into several small tasks is needed. For example, some objects should be temporarily set to adequate intermediate goals.

Offer Organization: 日本学術振興会, System Name: 科学研究費助成事業, Category: 若手研究(B), Fund Type: -, Overall Grant Amount: - (direct: 3600000, indirect: -)
本申請では,セル生産方式に自走式の部品箱を導入し,組立の各工程において必要な部品や工具を作業者に素早く手渡すことにより,部品取り出しの時間的ロスや部品取り間違いを減らして作業を効率化させることを提案している.またこのような支援を行うことにより,作業者の個性によらず安定した生産を実現することを目指している.
本年度は,昨年度までに開発した自走式トレイを用いたシステムを実際に構築した.システムは6台の自走式トレイから成り,組立に必要な部品や工具などを作業者に素早く手渡す.
自走式トレイ群と人間作業者の間には,何らかのインタフェースが必要である.インタフェースとしては,例えば音声によるもの,指差しジェスチャに基づくものなど様々なものが考えられるが,本研究では効率を重視し,足踏みスイッチによる方法を採用した.作業者は現在行っている組立工程が完了すると,足踏みスイッチを踏む.すると次の工程に必要な部品(を載せた部品トレイ)が作業者の手元に寄ってくる.
6台の自走式トレイと足踏みスイッチによるインタフェースを統合したシステムを実装し,組立作業のデモンストレーションと簡単なシステム評価を行った.子供用のジグソーパズル(6〜12ピース)を組立てる製品に見立て,提案手法により部品供給を行う場合と,部品供給を行わず,自走式トレイを作業者の前に静止して並べた場合(従来のセル生産方式に対応)のそれぞれで繰り返し組立作業を行い,組立に要した時間の平均値を求めた.その結果,組立所要時間の平均値はほぼ同じであったが,提案手法によって組立を行った場合の方が所要時間の分散が小さくなることが示された.部品供給を行わない場合は作業者の集中力低下に伴い作業効率が大幅に低下するが,部品供給を行う場合は作業者の集中力によらず安定して作業が行えることが確認できた.

Offer Organization: Japan Society for the Promotion of Science, System Name: Grants-in-Aid for Scientific Research, Category: Grant-in-Aid for Scientific Research (B), Fund Type: -, Overall Grant Amount: - (direct: 11600000, indirect: -)
In this research, we have developed a novel set of methodology that makes autonomous robots act in the real-world for long time. For technology exchange, also, we have participated in the robot soccer world cup (RoboCup), where researchers have make robots work in the real-world successfully.
The methods developed are as follows: self-localization that is robust against unexpected conditions, a decision making method that cover any condition in a task, and another decision making method that deals with uncertainty of recognition.
Our novel self-localization method, which is a version of a particle filter, utilizes resetting methods. A resetting method is used when a particle filter loses the pose of a robot. We enhance the robustness of a particle filter for self-localization by combining two major resetting methods, which have antipodal characteristics.
As a planning and decision making method, we have proposed a compression method for decision making data, which is huge for install on memory of robots. Vector quantization is used for the compression.
The particle filter and the decision making rule is combined so that robots can deal with the problem of uncertainty in decision making. In this method, an expected value of appropriateness is calculated for each action toward the probability distribution represented by the particle filter. The most appropriate action is then chosen from the values. In experiments, robots can switch their behavior when the level of uncertainty is changed. For example, a robot keeps away from a wall of an environment when the distance between the robot and the wall is uncertain.