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Intellectualization of life is a general tendency due to the proliferation of technology and science. Based on this concept, this paper presents multi-group localization algorithms and detection algorithms for multi-group service robot system (MGSR). Shopping cart problem is considered as an exemplary multi-group service robot system. The MGSR is designed to provide users with co-service by multiple carts and allows multiple users operation simultaneously. In MGSR, a cart carrying personal belongings of the user follows the user automatically and provides real-time position information to the user. To fulfill estimating the location of MGSR, hybrid external localization algorithm based on combination of QR location information and ZigBee location estimate is proposed. To detect and track a cart by another cart with LRF, we define cart features in LRF data and employ a support vector data description method. Recognition of user---cart groups in MGSR is realized by ZigBee blind nodes on the cart. We verified the feasibility of the proposed algorithms for MGSR through three experiment trials.

In this paper, kinematic modeling and singularity analysis are performed for an omnidirectional mobile robot. At the beginning, generalized formulas of the mobility and the first-order kinematics for a planar multiarticulated omnidirectional mobile robot are proposed. To verify the proposed formulas, a mobile robot that consists of three wheel mechanisms each of which has one redundant joint as compared to the operational degrees is introduced. Initially, the kinematic modeling and dynamic analysis, and odometry calculation for this robot are conducted. Next, singularity analysis of the proposed robot is performed. Then, using such a kinematic redundancy of each chain, motion planning algorithms that exploit the kinematic redundancy of the wheel chain are suggested. For navigation, a localization algorithm of the mobile robot based on odometry is presented, and specifically, two-leveled obstacle avoidance scheme, which simultaneously considers both large and small obstacles, is proposed. The usefulness of the proposed algorithms is verified through experimentation.

This paper presents a human detection algorithm and an obstacle avoidance algorithm for a marathoner service robot (MSR) that provides a service to a marathoner while training. To be used as a MSR, the mobile robot should have the abilities to follow a running human and avoid dynamically moving obstacles in an unstructured outdoor environment. To detect a human by a laser range finder (LRF), we defined features of the human body in LRF data and employed a support vector data description method. In order to avoid moving obstacles while tracking a running person, we defined a weighted radius for each obstacle using the relative velocity between the robot and an obstacle. For smoothly bypassing obstacles without collision, a dynamic obstacle avoidance algorithm for the MSR is implemented, which directly employed a real-time position vector between the robot and the shortest path around the obstacle. We verified the feasibility of these proposed algorithms through experimentation in different outdoor environments.

An automatic lighting system using 3-DOF robotic lamps and a laser scanner is proposed in this paper. The 3-DOF robotic lamp, which is designed with a spherical parallel mechanism with a unique forward kinematic solution, has a tilting motion to track a person and zoom-in and zoom-out motions to control the light intensity. In order to minimize the dynamic load, three actuators are installed at the base frame, and a counterbalancing design is considered. The positions of people are detected by a laser scanner, and the Kalman filter and a data association algorithm are applied in order to track the positions of people accurately. Therefore, multiple robotic lamps can track and illuminate each person continuously. We demonstrate experimentally that three robotic lamps mounted on the ceiling illuminate three people independently and control the intensity of the light according to the distance between a person and the robotic lamp.

This paper presents task-oriented navigation algorithms used for an outdoor environment. The goals of the navigation are recognizing colored border lines on both sides of a path, avoiding obstacles on the path, and navigating the given path. To recognize the colored border lines with one camera, we apply a support vector data description method, which employs six color features extracted from two color models. To avoid collision with obstacles on the path, we fuse the data of the lines measured by a camera and the obstacles measured by a laser range finder. These algorithms were applied to autonomous navigation of about 100 m long curved track. We demonstrate that a four-wheel skid-steering mobile robot successfully finishes the mission.

This work deals with motion planning algorithms of an omni-directional mobile robot with active caster wheels. A typical problem occurred in the motion control of such omni-directional mobile robot, which has been identified through experimental experiences, is skidding of the mobile wheel. It sometimes results in uncertain rotation of the steering wheel. To cope with this problem, a motion planning algorithm which resolves the skidding problem and uncertain motions of the steering wheel is mainly investigated. For navigation of the mobile robot, the posture of the omni-directional mobile robot is initially calculated using the odometry information. Then, the accuracy of the mobile robot's odometry is measured through comparison of the odometry information and the external sensor measurement. Finally, for successful navigation of the mobile robot, a motion planning algorithm that employs kinematic redundancy resolution method is proposed. Through simulations and experimentation, the feasibility of proposed algorithms was verified.

This paper deals with a navigation algorithm for swarm robot systems in which multiple mobile robots work together. The motion of each mobile robot is modeled in such a way to have more inputs than the number of outputs. The null-space projection method of this model is employed to resolve the motion of the swarm robot system while avoiding obstacles. The feasibility of the proposed navigation algorithm is verified through a simulation study using several swarm robot models.

This work deals with navigation of an omni-directional mobile robot with active caster wheels. Initially, the posture of the omni-directional mobile robot is calculated by using the odometry information. Next, the position accuracy of the mobile robot is measured through comparison of the odometry information and the external sensor measurement. Finally, for successful navigation of the mobile robot, a motion planning algorithm that employs kinematic redundancy resolution method is proposed. Through experiments for multiple obstacles and multiple moving obstacles, the feasibility of the proposed navigation algorithm was verified.

In this paper, intelligent human tracking algorithms for a human moving at a high speed are proposed and applied to a marathoner service robot. Then an application using omni-directional mobile platform for the elderly or the disabled is introduced. To use various motions of omni-directional mobile robots, kinematic modeling and singularity analysis are performed.

This paper presents a novel application of a perception sensor Kinect to a mobile shopping cart (MSC). We attempt to exercise daily-used instructions as commands by using skeleton tracking of the Kinect sensor. A pair of differential-driven wheels together with dynamical system is mounted on the chassis of the shopping cart, which reshapes the shopping cart as a mobile robot. The commercialized Kinect sensor is employed to detect the gestures of the human body. The mobile shopping cart equipped with Kinect could execute different gesture command, for example, go ahead, draw back, set stop and so on. The response time is within 0.5 second. To do the right reflexes, we define a set of trajectory determined by the gesture of the target customer. The experiment result shows the validity of proposed method.

This paper presents tracking algorithms of a mobile robot that tracks a human in front. The mobile robot, which is a differential-driven wheel type, is equipped with a laser range finder to perform human tracking in front. Initially, we recommend the torso part for the robust tracking of the human body in outdoor environment by a laser range finder. To track a human in front, we define a virtual target determined by the velocity of the target human. For a more efficient movement of the mobile robot, a desired heading direction of the robot considering motion vectors of the robot and the human is proposed.

This paper presents tracking algorithms of a mobile robot that follows a human body moving at high speed in unstructured outdoor environment. The mobile robot, which is a differential-driven wheel type, is equipped with one laser range finder to perform high-speed human tracking and obstacle avoidance. Initially, the waist part of the human body is found the best part of the human body for robust tracking by a laser range finder. Next an obstacle avoidance algorithm considering the relative velocity between the robot and obstacles is proposed. It is shown that the proposed human tracking algorithms based on the Kalman filter performs well even in the case of the target occlusions and temporary losses. These tracking algorithms have been applied to a marathoner following robot that carries the marathoner's personal belongings in many different outdoor environments.

This work presents the kinematic modeling and motion planning algorithm for an omni-directional mobile robot with kinematic redundancy. This robot consists of three wheel mechanisms each of which has one redundant joint as compared to the operational degrees. Initially, the kinematic modeling of this robot is conducted. Next, using such a kinematic redundancy of each chain, several motion planning algorithms are suggested. A localization algorithm of the mobile robot based on odometry is presented and specifically, two-leveled obstacle avoidance scheme, which simultaneously considers both large and small obstacles, is presented. The usefulness of the proposed algorithms is verified through simulation.

This work deals with navigation of an omni-directional mobile robot with active caster wheels. Initially, the posture of the omni-directional mobile robot is calculated by using the odometry information. Next, the position accuracy of the mobile robot is measured through comparison of the odometry information and the external sensor measurement. Finally, for successful navigation of the mobile robot, a motion planning algorithm that employs kinematic redundancy resolution method is proposed. Through experiments for multiple obstacles and multiple moving obstacles, the feasibility of the proposed navigation algorithm was verified.