본 연구는 육묘용 로봇 이식기를 개발하기 위한 목적으로 수행되었으며 그 결과는 다음과 같다. 1. 기계시각에 의한 결주 및 불량묘 판별은 72공 플러그묘판의 경우 98.8%, 128공의 플러그묘판의 경우 94.9%, 잎의 방향을 고려한 경우 72공의 플러그묘판의 경우 93.5%, 128공의 경우 91.0%의 정확도를 나타내었다. 2. 로봇 이식기의 기구부와 구동부를 개발하였고, 이를 제어하기 위한 제어기를 개발하였으며, 머니플레이터의 위치제어 정확도는 ± 1mm로 나타났다. 3. 잎의 방향을 고려하지 않은 이식방법에서의 이식성공률은 72공 플러그묘판의 경우 95.5%, 128공의 플러그묘판의 경우 94.5%로 나타났으며, 잎의 방향을 고려한 이식방법에서의 이식성공률은 72공의 경우 96.0%, 128공의 경우 95.0%로 나타났다.

This paper presents a robot simulator which can automatically generate a smooth collision free path. This simulator has the characterstisc of the object - oriented programming which is coded in Borland C+ +. Using General Inverse Algorithm, the inverse kinematics solutions of any kind of robots can be found generally. This simulator also uses Bezier Functions for the description of a smooth collision - free path. In addition, GUI(Graphic User Interface)technique is employed for user's convenience.

This paper presents a dynamic compensation methodology for robust trajectory tracking control of uncertain robot manipulators. To improve tracking performance of the system, a full model-based feedforward compensation with continuous VS-type robust control is developed in this paper(i.e,. robust decentralized adaptive control scheme). Since possible bounds of uncertainties are unknown, the adaptive bounds of the robust control is used to directly estimate the uncertainty bounds(instead of estimating manipulator parameters as in centralized adaptive control0. The global stability and robustness issues of the proposed control algorithm have been investigated extensively and rigorously via a Lyapunov method. The presented control algorithm guarantees that all system responses are uniformly ultimately bounded. Thus, it is shown that the control system is evaluated to be highly robust with respect to significant uncertainties.

A differential drive wheeled robot is a kind of mobile robot suitable for indoor navigation. Model predictive control is an optimal control technique with various advantages and can achieve excellent performance. One of the main advantages of model predictive control is that it can easily handle constraints. Therefore, it deals with realistic constraints of the mobile robot and achieves admirable performance for trajectory tracking. In addition, the intention of the robot can be properly realized by adjusting the weight of the cost function component. This control technique is applied to the local planner of the navigation component so that the mobile robot can operate in real environment. Using the Robot Operating System (ROS), which has transcendent advantages in robot development, we have ensured that the algorithm works in the simulation and real experiment.

As we become an aging society, the number of elderly patients continues to increase. Pressure sores that can easily occur in patients with trauma cause serious socio-economic problems. In general, prevention of bedsores through predicting the patient's posture is being developed. Developed method usually use artificial intelligence techniques to estimate the patient’s posture by measured pressure images in the mattress. In this method, it has a problem the reduction of estimation accuracy when posture of patient is changed. Therefore, it is necessary to use the filter of pressure images in the position transition of patient. In this paper, we propose an algorithm to predict the patient's posture, and an algorithm to reduce the ambiguity that can occur in the patient's posture transition section. By obtaining stable data through this algorithm, learning/prediction stability of the neural network can be expected, and prediction performance is improved accordingly. Through experiments, the effectiveness of the algorithm was verified.

This study is about a design method for deriving task safety scenarios for the application of collaborative robots. A five-step process for deriving task safety scenarios for collaborative robots has been proposed, which focuses on the type of collaboration between human and collaborative robot. The three types of collaboration were classified according to the collaboration workspace and the worktime of human and collaborative robot. Based on these three types of collaboration, task safety scenarios include scenarios that predict risk from unintended use during work. Collaboration with collaborative robot is a human-centered process because human actions can create dangerous situations. Besides, we improved the understanding of this design methodology by presenting examples of the application of task safety scenarios according to the process for each type of collaboration.

The aims of this paper is to develop a modular agricultural robot and its autonomous driving algorithm that can be used in field farming. Actually, it is difficult to develop a controller for autonomous agricultural robot that transforming their dynamic characteristics by installation of machine modules. So we develop for the model based control algorithm of rotary machine connected to agricultural robot. Autonomous control algorithm of agricultural robot consists of the path control, velocity control, orientation control. To verify the developed algorithm, we used to analytical techniques that have the advantage of reducing development time and risks. The model is formulated based on the multibody dynamics methods for high accuracy. Their model parameters get from the design parameter and real constructed data. Then we developed the co-simulation that is combined between the multibody dynamics model and control model using the ADAMS and Matlab simulink programs. Using the developed model, we carried out various dynamics simulation in the several rotation speed of blades.

This paper deals with a strategy of gain optimization for the kinematic control algorithm of a wire-driven surgical robot. The proposed controller consists of the closed-loop inverse kinematics with the back-calculation method. The closed-loop inverse kinematics has 18 PID control gains, and the back-calculation method has 6 gains. An efficient strategy is designed to optimize 18 values first and then the remaining 6 values. The optimal gain sets are searched under the step input with performance indices. In this gain optimization, the objective function is defined as the minimum value of signal-to-noise ratio of the performance indices for 6 DoF (Degree-of-Freedom) motion that is based on the Taguchi method, and the constraints are applied to obtain stable responses for each motion evenly. The gain sets obtained are verified by simulations using the test trajectories. In comparative results, the optimal gain value based on the performance index combined with ISE (integral of square error) and settling time showed the best control performance.

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.

In this paper, we present a learning platform for robotic grasping in real world, in which actor-critic deep reinforcement learning is employed to directly learn the grasping skill from raw image pixels and rarely observed rewards. This is a challenging task because existing algorithms based on deep reinforcement learning require an extensive number of training data or massive computational cost so that they cannot be affordable in real world settings. To address this problems, the proposed learning platform basically consists of two training phases; a learning phase in simulator and subsequent learning in real world. Here, main processing blocks in the platform are extraction of latent vector based on state representation learning and disentanglement of a raw image, generation of adapted synthetic image using generative adversarial networks, and object detection and arm segmentation for the disentanglement. We demonstrate the effectiveness of this approach in a real environment.

Collaborative Robot (Cobot) that can collaborate with humans by fusion with many advanced technologies among industrial robots in the industrial field are attracting attention. In this study, the engineers of Small and Medium Enterprises can directly participate in the cobot design, and ultimately, the possibility of deriving the shape design of the differentiated cobot was studied. The method applied to derive the shape design of differentiated cobot is ‘Morphological Analysis’. First, the design elements of the form of cobots were derived as ‘Link’ and ‘Joint’. In addition, by analyzing the image form of the Link and Joint of the existing cobot, a new form element of the Link and Joint was proposed. In order to quantitatively identify the most discriminating cobot shape design, FGI (Focus Group Interview) was conducted to derive image types of 4 Link and 3 Joint. Then, the most important ‘Shape Combination’ was carried out in morphological analysis, and 12 new cobot shape designs were drawn. Through this, the applicability of the morphological analysis method in the derivation of differentiated cobot shape design was examined.

There exists a popular belief that the elderly are more conservative than the younger people in acceptability of new technology. This study explores whether the generation gap in technology acceptance exists in the case of using telepresence robots, which project the presence and mobility of remote operator, for the universal purpose of social participation rather than for specific applications. Two groups of senior citizens and undergraduate students in their twenties personally experienced the telepresence robots operation and conducted a survey on how they perceived the social participation of a remote operator mediated by telepresence robot and to what extent the remote operator deserve equal rights to be treated as if one really exists in the local environment. The results show that the elderly have higher expectation on the role and functions of telepresence robots, and more favorable in principle for a remote operator to exercise equal rights by operating telepresence robot. It suggests that the stereotypes, the elderly lag behind younger generation in accepting new technology, is unlikely to fit into the telepresence robot market, for the elderly have more favor and support using telepresence robots as an universal avatar for social participation.

This paper proposes a low-cost robotic surgery system composed of a general purpose robotic arm, an interface for daVinci surgical robot tools and a modular haptic controller utilizing smart actuators. The 7 degree of freedom (DOF) haptic controller is suspended in the air using the gravity compensation, and the 3D position and orientation of the controller endpoint is calculated from the joint readings and the forward kinematics of the haptic controller. Then the joint angles for a general purpose robotic arm is calculated using the analytic inverse kinematics so that that the tooltip reaches the target position through a small incision. Finally, the surgical tool wrist joints angles are calculated to make the tooltip correctly face the desired orientation. The suggested system is implemented and validated using the physical UR5e robotic arm.

This paper designed modular agricultural robotic platform capable of a variety of agricultural tasks to address the problems caused by a decline in agricultural populations and an increase in average age. We propose a modular robotic platform that can perform many tasks required in field farming by replacing only work modules with common robotic platforms. This platform is capable of steering while driving on four wheels in an upland environment where farm work is performed, and an attitude control module is attached to each drive module to control the attitude of the platform. In addition, the width of the platform is designed to be variable in order to operate in various ridges according to the crop cultivation method. Finally, we evaluated five items: variable width, gradient, attitude control angle, step and road speed in order to carry out the farming industry while maintaining a stable posture.

In robotic harvesting, a gripper to manipulate the fruits needs to be attached to the robot system. We proposed a flexible robot gripper that can actively respond to the shape of an object such as fruits in the previous work. However, we found that there is a possibility of not being reliably gripped when the object slides during contact with a finger. In this paper, the improved gripper design is proposed to fundamentally solve the problems of the previous gripper. The position of the finger and the maximum closed position are changed, and the design improvement is performed to increase the grip stability by changing the installation angle of the link portion of the finger. Based on the improved design, a modified gripper is fabricated by 3-D printing, and then gripping experiments are performed on spherical object and fruit model object. It is shown that the gripper can stably grip the objects without excessive bending of the finger link of the gripper. The contact pressure between the finger and the surface of the object is measured, and it is verified that it is a sufficiently small pressure that does not cause damage to the fruit. Therefore, the proposed gripper is expected to be successfully applied in harvesting.

In the agricultural field, interests in research using robots for fruit harvesting are continuously increasing. Dual arm manipulators are promising because of its abilities like task-distribution and role-sharing. To operate it efficiently, the task sequence must be planned adequately. In our previous study, a collision-free path planning method based on a genetic algorithm is proposed for dual arm manipulators doing tasks cooperatively. However, in order to simplify the complicated collision-check problem, the movement between tasks of two robots should be synchronized, and thus there is a problem that the robots must wait and resume their movement. In this paper, we propose a heuristic algorithm that can reduce the total time of the optimal solution obtained by using the previously proposed genetic algorithm. It iteratively desynchronizes the task sequence of two robots and reduces the waiting time. For evaluation, the proposed algorithm is applied to the same work as the previous study. As a result, we can obtain a faster solution having 22.57 s than that of the previous study having 24.081 s. It will be further studied to apply the proposed algorithm to the fruit harvesting.

Fruit and vegetable harvesting robots have been widely studied and developed in recent years to reduce the cost of harvesting tasks such as labor and time. However, harvesting robots have many challenges due to the difficulty and uncertainty of task. In this paper, we characterize the crop environment related to the harvesting robot and analyzes state-of-the-art of the harvesting robot especially, in the viewpoint of robotic end-effector. The end-effector, an one of most important element of the harvesting robot, was classified into gripper and harvesting module, which were reviewed in more detail. Performance measures for the evaluation of harvesting robot such as test, detachment success, harvest success, and cycle time were also introduced. Furthermore, we discuss the current limitations of the harvesting robot and challenges and directions for future research.

Multi-floor navigation of a mobile robot requires a technology that allows the robot to safely get on and off the elevator. Therefore, in this study, we propose a method of recognizing the elevator from the current position of the robot and estimating the location of the elevator locally so that the robot can safely get on the elevator regardless of the accumulated position error during autonomous navigation. The proposed method uses a deep learning-based image classifier to identify the elevator from the image information obtained from the RGB-D sensor and extract the boundary points between the elevator and the surrounding wall from the point cloud. This enables the robot to estimate the reliable position in real time and boarding direction for general elevators. Various experiments exhibit the effectiveness and accuracy of the proposed method.

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.

Home robot arms require a payload of 2 kg to perform various household tasks; at the same time, they should be operated by low-capacity motors and low-cost speed reducers to ensure reasonable product cost. Furthermore, as robot arms on mobile platforms are battery-driven, their energy efficiency should be very high. To satisfy these requirements, we designed a lightweight counterbalance mechanism (CBM) based on a spring and a wire and developed a home robot arm with five degrees of freedom (DOF) based on this CBM. The CBM compensates for gravitational torques applied to the two pitch joints that are most affected by the robot’s weight. The developed counterbalance robot adopts a belt-pulley based parallelogram mechanism for 2-DOF gravity compensation. Experiments using this robot demonstrate that the CBM allows the robot to meet the above-mentioned requirements, even with low-capacity motors and speed reducers.