別紙４（課程博士）
Date of Submission:

平成 ２8 年
Department

Student ID Number

機械工学専攻Applicant’s name

氏名

学籍番号

第 139103 号

Supervisors

指導教員

Tresna Dewi

内山

１月 12 日直樹

章 忠三宅哲夫

Abstract

論文内容の要旨
Title of Thesis
博士学位論文名(博士)

Collision Avoidance and Object Following Control for a Mobile Robot in Human
Living Environment（人間生活環境における移動ロボットの衝突回避と物体追従制御）(Approx. 800 words)(要旨

1,200 字程度)

With recent technological developments, mobile robots are expected to occupy the same environments as humans
and provide fully autonomous or semi-autonomous assistance. Robot navigation is the safe movement of a robot
toward its goal and is accomplished by knowledge of the environment. Obstacle avoidance is one of the main
requirements of robot navigation. During the past few years, the potential field method for obstacle avoidance
and target setting has gained popularity among roboticists. In this approach, the obstacles and targets exert
repulsive and attractive forces on the robot, respectively.
As our most powerful sense, vision provides us with an enormous amount of information and enables intelligent
interaction with our dynamic environment. Vision-based robotics systems have recently increased in popularity,and several new approaches have been proposed. Despite the huge efforts invested in creating effective low-costservice mobile robots, vision-based motion control and path planning remain theoretically challenging. The basic
principle involves iterating two structures from motions captured by a stereo camera. As the distance to a
detected object and motion can be predicted and measured by depth mapping, the stereo camera can besubstituted by a single red–green–blue depth (RGB-D) camera. A popular RGB-D camera is the Kinect sensor, amotion sensing device developed by Microsoft Corporation.
Vision-based robot navigation might enable the production of service robots that can track and follow moving
targets. Tracking mobile robots generally require expensive sensors such as range finders, tend to disregard
obstacle avoidance, and are developed as single robots. Moreover, their algorithms are complex.
In this study, we combined the potential field method with visual sensing to create a moving-object-following
robot; that is, a robot that tracks and follows an object attached to a human, wheelchair, or another robot (leaderrobot). The attached object considered was a blue circular mark. The robot was mounted with a Kinect sensor,
and the position of the blue circle and depth mapping for the distance calculation were detected by an RGB
image detection system. The designed mobile robot system can track and follow a moving object in environments
wherein humans coexist; using only the information acquired by vision sensors and IR proximity sensors. The
designed reference controller generates a reference trajectory for obstacle avoidance and the robot is moved alongits reference trajectory by a simple proportional integral (PI) controller. This proposed method enables collision
avoidance, is robust to brief occlusion, and to some extent, is also applicable to swarm robots. The simple
algorithm makes the proposed method easily modifiable to robotic applications other than mobile robots.
First, we derive reference models from the dynamic modeling of the two-wheeled differential drive mobile robot

with non-holonomic constraints. The collision avoidance scheme is achieved by the model reference control
based on the potential field path planner. A system integration method, by which the robot moves in a dynamicenvironment, is also presented. The effectiveness of the proposed controller is demonstrated by discussing theresults of simulations and experiments. The next step is to present a new collision avoidance approach for
four-wheeled human-operated mobile robots. Because the proposed method considers the non-holonomic
constraint of a mobile robot, it provides practical collision avoidance control. In a verification experiment,
entirely unskilled operators maneuvered the robot to its destination without collisions, demonstrating theeffectiveness of the proposed approach.
The proposed method also presents the collision avoidance control technique applicable for swarm robots moving
in a rehabilitation environment containing static and moving obstacles. A leader–follower formation is adopted,
in which the leader of the swarm robot team follows the rehabilitee, while the other robots follow the leader. The
effectiveness of the proposed system is demonstrated by presenting the results of a simulation study in which
several static and dynamic obstacles were placed in a human living environment.
The final step is combining the potential field method with visual sensing to create a moving-object-following
robot; that is, a robot that tracks and follows an object attached to a human, wheelchair, or another robot (leaderrobot). In this study, the attached object was a blue circular mark. The robot was mounted with a Kinect sensor,
and the position of the blue circle and the depth mapping for the distance calculation were detected by RGB
imaging. The effectiveness of the proposed method is discussed in the context of human-following,
wheelchair-following, and leader robot-following systems and by presenting an experiment that extended into thehallway outside the laboratory testbed. Experiments were conducted in several environmental settings.
In order to prove that proposed method easily modifiable to robotic applications other than mobile robots, we
discuss the possibility of achieving human and object following without specific shape detection. We compare theproposed system design with the work undertaken by several references. The effectiveness of the proposed
method was experimentally evaluated in several course settings.
This thesis proposes a moving-object-following mobile robot functioning as a mobile service robot in a humanliving environment. The algorithms and hardware are simplified as much as possible in this design, enabling theproposed robot to assist in a wide variety of dull, repetitive tasks in human environments, such as transporting thedisabled. Despite the application of inexpensive Kinect and proximity sensors, the system performed as expectedin this investigation, in which the effectiveness of the proposed method was confirmed in different experimental
scenarios. The possibility of creating swarms of service robots by the proposed method is also discussed in thisthesis.