This paper introduces several mobile robots developed by using LEGO MIDSTORM for experimental studies of robotics engineering education. The first mobile robot is the line tracer robot that tracks a line, which is a prototype of wheel-driven mobile robots. Ultra violet sensors are used to detect and follow the line. The second robot system is a two-wheel balancing robot that is somewhat nonlinear and complex. For the robot to balance, a gyro sensor is used to detect a balancing angle and PD control is used. The last robot system is a combined system of a line tracer and a two-wheel balancing robot. Sensor filtering and control algorithms are tested through experimental studies.

This study deals with the design of a spring backboned soft arm, which will be employed for generation of human gesture as an effective means of Human Robot interaction. The special features of the proposed mechanism are the light weight and the flexibility of the whole mechanism by using a spring backbone. Thus, even in the case of collision with human, this device is able to absorb the impact structurally. The kinematics and the design for the soft arm are introduced. The performance of this mechanism was shown through experiment emulating several human gestures expressing human emotion and some service contents. Finally, this soft arm was implemented as the wing mechanism of a penguin robot.

In this study, we aim to develop energy efficient walking and running robot with compliant leg. So, we propose the energy efficient locomotion control method. And, we experiment the proposed control method applying to the experimental robot with compliant leg. From the experiment, we look at whether the proposed control method can the robot walk and run energy efficiently.

In this paper, we propose a cable climbing robot which can climb up and down the cables in the bridges. The robot mechanism consists of three parts: a wheel based driving mechanism, adhesion mechanism, and safe landing mechanism. The wheel based driving mechanism is driven by tooth clutches and motors. The adhesion mechanism plays the role of maintaining adhesion force by a combination of pantograph, ball screw, and springs even when the power is lost. The safe landing mechanism is developed for guaranteeing the safety of the robot during operations on cables. It can make the robot fall down with reduced speed by dissipating the gravitational forces. The robot mechanism is designed and manufactured for validating its effectiveness.

A map of complex environment can be generated using a robot carrying sensors. However, representation of environments directly using the integration of sensor data tells only spatial existence. In order to execute high-level applications, robots need semantic knowledge of the environments. This research investigates the design of a system for recognizing objects in 3D point clouds of urban environments. The proposed system is decomposed into five steps: sequential LIDAR scan, point classification, ground detection and elimination, segmentation, and object classification. This method could classify the various objects in urban environment, such as cars, trees, buildings, posts, etc. The simple methods minimizing time-consuming process are developed to guarantee real-time performance and to perform data classification on-the-fly as data is being acquired. To evaluate performance of the proposed methods, computation time and recognition rate are analyzed. Experimental results demonstrate that the proposed algorithm has efficiency in fast understanding the semantic knowledge of a dynamic urban environment.

It is well-known that when singularities are located within the workspace of the parallel mechanism (PM), the usefulness of its workspace is significantly deteriorated. To handle this problem, we suggest an optimal design method which leads to more useful and larger workspace of the PM by taking its singularity locations into consideration in design process. Kinematic models of three selected planar PMs, a 5R type PM, a 3-RPR type planar PM, and a 3-RRR type planar PM, are derived via screw theory and their singularity analyses are conducted. Then workspace optimal designs for those three PMs are conducted to verify that the suggested design method leads more useful and larger workspace in which deterioration by singularity is minimal.

In this paper we propose the method that detects moving objects in autonomous navigation vehicle using LRF sensor data. Object detection and tracking methods are widely used in research area like safe-driving, safe-navigation of the autonomous vehicle. The proposed method consists of three steps: data segmentation, mobility classification and object tracking. In order to make the raw LRF sensor data to be useful, Occupancy grid is generated and the raw data is segmented according to its appearance. For classifying whether the object is moving or static, trajectory patterns are analysed. As the last step, Markov chain Monte Carlo (MCMC) method is used for tracking the object. Experimental results indicate that the proposed method can accurately detect moving objects.

The loop closure problem is one of the most challenging issues in the vision-based simultaneous localization and mapping community. It requires the robot to recognize a previously visited place from current camera measurements. While the loop closure often relies on visual bag-of-words based on point features in the previous works, however, in this paper we propose a line-based method to solve the loop closure in the corridor environments. We used both the floor line and the anchored vanishing point as the loop closing feature, and a two-step loop closure algorithm was devised to detect a known place and perform the global pose correction. We propose an anchored vanishing point as a novel loop closure feature, as it includes position information and represents the vanishing points in bi-direction. In our system, the accumulated heading error is reduced using an observation of a previously registered anchored vanishing points firstly, and the observation of known floor lines allows for further pose correction. Experimental results show that our method is very efficient in a structured indoor environment as a suitable loop closure solution.

The purpose of this study was to analyze Early Childhood Teachers' awareness of R-Learning. The Subjects were 6 Early Childhood Teachers who experienced to use Robot in their teaching and learning. The data was analyzed through semi-structured in-depth interview and teachers' journals. The results showed possibilities and limitations of R-learning. As possibilities of R-learning, it was found that robot is am attractive teaching materials. In addition, it was found that robot might be an assistant teacher. Third, robot helps children improving their social development. Fourth, teachers might get confident while using robots in their teaching and learning. However, there were limitations of R-learning. First, teachers need to understand what R-learning means. Second, there are some functional problems of R-learning. Third, there are lack of qualitative contents of R-learning. Finally, robots need to be general to every children.

The camera has limitations of poor visibility in underwater environment due to the limited light source and medium noise of the environment. However, its usefulness in close range has been proved in many studies, especially for navigation. Thus, in this paper, vision-based object detection and tracking techniques using artificial objects for underwater robots have been studied. We employed template matching and mean shift algorithms for the object detection and tracking methods. Also, we propose the weighted correlation coefficient of adaptive threshold -based and color-region-aided approaches to enhance the object detection performance in various illumination conditions. The color information is incorporated into the template matched area and the features of the template are used to robustly calculate correlation coefficients. And the objects are recognized using multi-template matching approach. Finally, the water basin experiments have been conducted to demonstrate the performance of the proposed techniques using an underwater robot platform yShark made by KORDI.

In this paper, we propose a cost-aware Rapidly-exploring Random Tree (RRT) path planning algorithm for mobile robots. A mobile robot is presented with a cost map of the field of interest and assigned to move from one location to another. As a robot moves, the robot is penalized by the cost at its current location according to the cost map. The overall cost of the robot is determined by the trajectory of the robot. The goal of the proposed cost-aware RRT algorithm is to find a trajectory with the minimal cost. The cost map of the field can represent environmental parameters, such as temperature, humidity, chemical concentration, wireless signal strength, and stealthiness. For example, if the cost map represents packet drop rates at different locations, the minimum cost path between two locations is the path with the best possible communication, which is desirable when a robot operates under the environment with weak wireless signals. The proposed cost-aware RRT algorithm extends the basic RRT algorithm by considering the cost map when extending a motion segment. We show that the proposed algorithm gives an outstanding performance compared to the basic RRT method. We also demonstrate that the use of rejection sampling can give better results through extensive simulation.