知能システム論１（１２） 移動ロボットのナビゲーショ ン ２００７．６．１９

講義内容 １．はじめに ２．ベクトルの基礎 ３．運動学 (Kinematics) ４．動力学（ Dynamics) ５．ロボットの腕の制御 (Control) 力制御 ６．軌道計算 (Trajectory) ７．移動ロボット (Mobile Robot) ナビゲー ション

Indoor Navigation of Multiple Mobile Robots in a Dynamic Environment Using iGPS Presented at the 2002 IEEE International Conference on Robotics and Automation

Contents Objective Approach to multi-robot navigation Application of multi-robot navigation in a dynamic environment Conclusions

Objective - Delivery Service Robots in Office -

Approach to Multi-robot Navigation Car navigation using GPS System configuration Localization and map construction

Localization: iGPS (indoor GPS) [2] Y.Hada and K.Takase: ”Multiple Mobile Robot Navigation Using The Indoor Global Positioning System (iGPS)”, Proc. of IROS2001, pp , IR LED Unit

Map construction 32.2[m] 14.3[m] corridor room X Y desks We created a map using both building plan and a layout of furniture Robot

Previous Work Multi-robot navigation in a static environment [2] Y.Hada and K.Takase: ” Multiple Mobile Robot Navigation Using The Indoor Global Positioning System (iGPS) ”, Proc. of IROS2001, pp , 2001.

Multi-robot Navigation in a Dynamic Environment We use the external robot control system for globally rational navigation We need to introduce some methods to update environmental map by recognizing moving object –Using iGPS –Using onboard sensor

Classification of obstacles Labelable obstacles to which artificial marks can be attached –Chairs Unlabelable obstacles which is difficult to bear artificial marks –People Unmovable obstacles –Walls, Pillars, Desks, Shelves

Recognition of labelable Obstacles Using iGPS Mark-based vision [Y.Hada, K.Takase:1997]Mark-based vision –Object recognition can be replaced with simpler mark recognition We introduce mark-based vision into iGPS to recognize labelable obstacles.

IR LED Unit Length:134mm Width :36mm LED Start bits Object ID number bit End bit x y

Object Model – pillar-like object Object ID=“ １ ” … Chair v1 v2 v3 v4 y x

Procedure of Object Recognition v1 v2 v3 v4 y x Xw Yw World Coordinate System V2 V3 V4 V1

Detection of unlablelable obstacles using an onboard sensor Moving objects (people) are detected by onboard sensor We assume that people temporarily impede a robot’s movement 30[degrees] 60[cm] Direction of robot ’ s movement Laser range finder Omnidirectional Mobile robot

Experimental setup

Experiment - Delivery Task -

Movable obstacle avoidance

Unload stowage

Detection of moving obstacle

Summary of Research The multi-robot navigation system based on iGPS was proposed. The experiment of delivery task was carried out in the time-varying environment. The system could successfully navigate multiple robots reliably and robustly, suggesting the practical usefulness.

Concluding Remarks Importance of robots for social application History of R/D on robot technology reviewed Social robots not realized by conventional R/D Intelligent Environment Supported Robot is proposed aiming at breakthrough in robotics Mobile robot system for delivery in a building developed, demonstrating the feasibility and effectiveness of the proposed approach

Car navigation using GPS Planning route using map Localization method Return GPS satellite

External section System Configuration External sensing devices –Facilitate localization remarkably –Useful for indoor navigation [3] R. Gutsche and F.M.Wahl: “ A New Navigation Concept for Mobile vehicles ”, Proc. Of IEEE IROS ’ 92, pp , return Internal section Off-carrier sensors On-carrier sensors

Mobile robot with IR LED Unit return

Experiment Stairs Bench with LED unit Static obstacles (2 tables, 3 benches) Goal of robot A Goal of robot B Robot A Robot B Narrow space

Future work Introduce more effective sensor-based navigation scheme Use “passive” marks like stealth barcode instead of IR LED unit.

Problem

全方向移動 ロボット オムニホイル を用いた設計 キネマティクス return

オムニホイル 中心にモータ軸を 固定 バレル部分が自由 に回転 ６０度ずらして２ つ組み合わせる モータの回転軸方向に自由に移動 １２０度ずらし３組配置、滑らかな全方向移動 return

全方向移動機構のキネマティク ス Ｖ１ Ｖ２ Ｖ３ Ｖｘ Ｖｙ ｒ 対称型三輪機構 return

物体の実時間認識 物体にマークを貼付し、物体認識を マークの認識に置き換える。 物体モデルとリンクすることで、その 物体が占める空間を認識する。

マークの認識 相関演算によるマークの同定 ( トラッキングビジョン）

ベストマッチする場所とデストーション distortion1 << distortion2

情景中のマークとテンプレートとの distortion 値

物体形状モデルの作成

物体の認識 ( 輪郭の記述）

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