Autonomous mobile robots based on the Web have been already used in public places such as museums. There are many kinds of problems to be solved because of the limitation of Web and the dynamically changing environment. We present a methodology for intelligent mobile robot that demonstrates a certain degree of autonomy in navigation applications. In this paper, we focus on a mobile robot navigator equipped with neuro-fuzzy controller which perceives the environment, make decisions, and take actions. The neuro-fuzzy controller equipped with collision avoidance behavior and target trace behavior enables the mobile robot to navigate in dynamic environment from the start location to goal location. Most telerobotics system workable on the Web have used standard Internet techniques such as HTTP, CGI and Scripting languages. However, for mobile robot navigations, these tools have significant limitations. In our study, C# and ASP.NET are used for both the client and the server side programs because of their interactivity and quick responsibility. Two kinds of simulations are performed to verify our proposed method. Our approach is verified through computer simulations of collision avoidance and target trace.

In this paper, we propose a modified ORB-SLAM (Oriented FAST and Rotated BRIEF Simultaneous Localization And Mapping) for precise indoor navigation of a mobile robot. The exact posture and position estimation by the ORB-SLAM is not possible all the times for the indoor navigation of a mobile robot when there are not enough features in the environment. To overcome this shortcoming, additional IMU (Inertial Measurement Unit) and encoder sensors were installed and utilized to calibrate the ORB-SLAM. By fusing the global information acquired by the SLAM and the dynamic local location information of the IMU and the encoder sensors, the mobile robot can be obtained the precise navigation information in the indoor environment with few feature points. The superiority of the modified ORB-SLAM was verified to compared with the conventional algorithm by the real experiments of a mobile robot navigation in a corridor environment.

Getting on and off an elevator is one of the most important parts for multi-floor navigation of a mobile robot. In this study, we proposed the method for the pose recognition of elevator doors, safe path planning, and motion estimation of a robot using RGB-D sensors in order to safely get on and off the elevator. The accurate pose of the elevator doors is recognized using a particle filter algorithm. After the elevator door is open, the robot builds an occupancy grid map including the internal environments of the elevator to generate a safe path. The safe path prevents collision with obstacles in the elevator. While the robot gets on and off the elevator, the robot uses the optical flow algorithm of the floor image to detect the state that the robot cannot move due to an elevator door sill. The experimental results in various experiments show that the proposed method enables the robot to get on and off the elevator safely.

Unlike normal wheels, the Mecanum wheel enables omni-directional movement regardless of the orientation of a mobile robot. In this paper, a robust trajectory tracking control method is developed based on the dynamic model of the Mecanum wheel mobile robot in order that the mobile robot can move along the given path in the environment with disturbance. The method is designed using the impedance control to make the mobile robot to track the path, and the integral sliding mode control for robustness to disturbance. The good performance of the proposed method is verified using the MATLAB /Simulink simulation and also through the experiment on an actual Mecanum wheel mobile robot. In both the simulation and the experimentation, we make the mobile robot move along a reference trajectory while maintaining the robot's orientation at a constant angle to see the characteristics of the Mecanum wheel.

For a mobile robot that travels along a terrain consisting of various geology, information on tire force and friction coefficient between ground and wheel is an important factor. In order to estimate the lateral force between ground and wheel, a lot of information about the model and the surrounding environment of the vehicle is required in conventional method. Therefore, in this paper, we are going to estimate lateral force through simple model (Minimal Argument Lateral Slip Curve, MALSC) using only minimum data with high estimation accuracy and to improve estimation reliability of the friction coefficient by using the estimated lateral force data. Simulation is carried out to analyze the correlation between the longitudinal and transverse friction coefficients and slip angles to design the simplified lateral force estimation model by analysing simulation data and to apply it to the actual field environment. In order to verify the validity of the equation, estimation results are compared with the conventional method through simulation. Also, the results of the lateral force and friction coefficient estimation are compared from both the conventional method and the proposed model through the actual robot running experiments.

Most of outdoor mobile robots have a suspension on each wheel in order to relieve the shock by ground obstacles and to improve the driving stability. Typically, in the actual operations, the suspensions have been used under a given set of conditions as all the damping and spring coefficients of the suspensions are fixed. However, it is necessary to readjust the coefficients of the suspensions according to surface conditions that may cause the unstable shaking of a robot body at high speed driving. Therefore, this paper is focused on the mobility analysis of an outdoor robot when the coefficients of suspensions (in particular, damping coefficients) are changed while driving on an uneven road surface. In this paper, a semi-active suspension with twelve damping coefficient levels was used and a small sized vehicle with the suspensions was employed to analyze the mobility dependent on a change of the damping coefficient. And the mobility was evaluated through driving experiments on a bumped slope.

A rectangle-based relative localization method is proposed for a mobile robot based on a novel geometric formulation. In an artificial environment where a mobile robot navigates, rectangular shapes are ubiquitous. When a scene rectangle is captured using a camera attached to a mobile robot, localization can be performed and described in the relative coordinates of the scene rectangle. Especially, our method works with a single image for a scene rectangle whose aspect ratio is not known. Moreover, a camera calibration is unnecessary with an assumption of the pinhole camera model. The proposed method is largely based on the theory of coupled line cameras (CLC), which provides a basis for efficient computation with analytic solutions and intuitive geometric interpretation. We introduce the fundamentals of CLC and describe the proposed method with some experimental results in simulation environment.

This paper proposes a novel method for detection of hand raising poses from images acquired from a single camera attached to a mobile robot that navigates unknown dynamic environments. Due to unconstrained illumination, a high level of variance in human appearances and unpredictable backgrounds, detecting hand raising gestures from an image acquired from a camera attached to a mobile robot is very challenging. The proposed method first detects faces to determine the region of interest (ROI), and in this ROI, we detect hands by using a HOG-based hand detector. By using the color distribution of the face region, we evaluate each candidate in the detected hand region. To deal with cases of failure in face detection, we also use a HOG-based hand raising pose detector. Unlike other hand raising pose detector systems, we evaluate our algorithm with images acquired from the camera and images obtained from the Internet that contain unknown backgrounds and unconstrained illumination. The level of variance in hand raising poses in these images is very high. Our experiment results show that the proposed method robustly detects hand raising poses in complex backgrounds and unknown lighting conditions.

This paper proposed a method of cooperative control of three mobile robots for carrying an object placed on a floor together. Each robot moves to the object independently from its location to a pre-designated location for grasping the object stably. After grasping the common object, the coordination among the robots has been achieved by a master-slave mode. That is, a trajectory planning has been done for the master robot and the distances form the master robot to the two slave robots have been kept constant during the carrying operation. The localization for mobile robots has been implemented using the encoder data and inverse kinematics since the whole system does not have the slippage as much as a single mobile robot. Before the carrying operation, the lifting operations are implemented using the manipulators attached on the top of the mobile robots cooperatively. The real cooperative lifting and carrying operations are implanted to show the feasibility of the master-slave mode control based on the kinematics using the mobile manipulators developed for this research.

In this paper, a new mobile robot, so called a rollerbot, is presented, which has single body and rugby-ball shaped roller wheel. A rollerbot has single point contact on ground and low energy consumption in motion because of the reduced friction. By changing center of mass using a balancing weight, a rollerbot is able to get steering force. The vertical position of mass center of the rollerbot in this paper is designed to lie inside radius of the roller wheel, so that to have stable equilibrium position. Thus, the posture and the steering control of the rollerbot can be easily done by changing the center of mass. Kinematics of the rollerbot is derived by transformation of differential motion in this paper.

The robot mechanisms that were previously researched had only been conducted for the purpose of overcoming the obstacles stably at low speed driving and enhancing the stability against high speed circuitous driving, and yet, the mechanism satisfying two purposes. However, in order to stably drive with high speed on rough terrain, there is a need for satisfying both of these purposes, as well as testing the efficiency of the mechanisms at high speed driving. There, this paper simulated some of the passive mechanisms and focused on checking the performances of passive mechanisms through simulations and analyzing each mechanism on the basis of an evaluation index. The simulation was conducted by Adams (The Multi-body Dynamics Simulation Solution) and used various types of passive mechanisms which were introduced in the robotics field. As a result, the study confirmed that passive mechanisms have a number of situations that affect the driving stability on each direction of roll and pitch. Further study is needed about active mechanism.

This paper focuses on development of a testbed for analysis of robot-terrain interaction on rough terrain and also, through one wheel driving experiments using this testbed, prediction of maximum velocity and acceleration of UGV. Firstly, from the review regarding previous researches for terrain modeling, the main variables for measurement are determined. A testbed is developed to measure main variables related to robot-terrain interaction. Experiments are performed on three kinds of rough terrains (grass, gravel, and sand) and traction-slip curves are obtained using the data of the drawbar pull and slip ratio. Traction-slip curves are used to predict driving performance of UGV on rough terrain. Maximum velocity and acceleration of UGVs are predicted by the simple kinematics and dynamics model of two kinds of 4-wheel mobile robots. And also, driving efficiency of UGVs is predicted to reduce energy consumption while traversing rough terrains.

This paper presents interaction force control between a balancing robot and a human operator. The balancing robot has two wheels to generate movements on the plane. Since the balancing robot is based on position control, the robot tries to maintain a desired angle to be zero when an external force is applied. This leads to the instability of the system. Thus a hybrid force control method is employed to react the external force from the operator to guide the balancing robot to the desired position by a human operator. Therefore, when an operator applies a force to the robot, desired balancing angles should be modified to maintain stable balance. To maintain stable balance under an external force, suitable desired balancing angles are determined along with force magnitudes applied by the operator through experimental studies. Experimental studies confirm the functionality of the proposed method.

This paper proposes how to improve the performance of CSS-based indoor localization system. CSS based localization utilizes signal flight time between anchors and tag to estimate distance. From the distances, the 3-dimensional position is calculated through trilateration. However the error in distance caused from multi-path effect transfers to the position error especially in indoor environment. This paper handles a problem of reducing error in raw distance information. And, we propose the new localization method by pattern matching instead of the conventional localization method based on trilateration that is affected heavily on multi-path error. The pattern matching method estimates the position by using the fact that the measured data of near positions possesses a high similarity. In order to gain better performance of localization, we use EKF(Extended Kalman Filter) to fuse the result of CSS based localization and robot model.

This paper presents a new approach for mobile robot heading detection using MEMS Gyro north finding method in the indoor environment. Based on this, the robot heading angle measurement scheme is proposed; improved north finding theory and algorithm are also explained. Several approaches are applied to confirm system’s precision and effectiveness. In order to find out the heading angle, a single axis MEMS gyroscope to sense the angle between the robot heading direction and the north is used. To reach enough estimation accuracy and reduce detection time,the least square method (LSM) for the signal fitting and parameter estimation is applied. Through a turn‐table, we setup a carouseling system to decrease the substantial bias effect on gyroscope’s heading angle. For the evaluation of the proposed method, this system is implemented to the Pioneer robot platform. The performance and heading error are analyzed after the test. From the simulation and experimental results, system’s accuracy, usefulness and adaptability are shown.

Global positioning system (GPS) is widely used to measure the position of a vehicle. However, the accuracy of the GPS can be severely affected by surrounding environmental conditions. To deal with this problem, the GPS and odometry data can be combined using an extended Kalman filter. For stable navigation of an outdoor mobile robot using the GPS, this paper proposes two methods to evaluate the reliability of the GPS data. The first method is to calculate the standard deviation of the GPS data and reflect it to deal with the uncertainty of the GPS data. The second method is to match the GPS data to the traversability map which can be obtained by classifying outdoor terrain data. By matching of the GPS data with the traversability map, we can determine whether to use the GPS data or not. The experimental results show that the proposed methods can enhance the performance of the GPS‐based outdoor localization.

This paper proposes a technique of indoor localization for mobile robot by so called indoor GPS and EKF. Basically the concept of indoor GPS is similar outdoor GPS, and the indoor GPS gets distances between Anchors and Tag by a ranging method of CSS and then estimates the coordinate by distances and known Anchor positions. After we performed performance test of indoor GPS system in ideal and multipath environment, we configured that the indoor GPS has internal error factors and external error factors. This paper handled a multipath problem belonging to external error factors. At first various direct physical method are introduced to fix the multipath problems, and as expected we got errors corrected considerably. And then the method of selective anchors for indoor GPS is applied. With these two level improvement of indoor GPS performance, EKF(Extended Kalman Filter) is applied to mobile robot in indoor environment. The usefulness of the proposed methods are shown by a series of experiments in a environment giving contaminated data by multipath.