Jae Hoon Lee

Optimal design and actuator sizing of redundantly actuated omni-directional mobile robots

Despite that omni-directional mobile robots have been employed popularly in several application areas, effort on optimal design of such mobile robots has been few in literature. Thus, this paper investigates the optimal design of omni-directional mobile robots. Particularly, optimal design parameters such as one or double offset distance of wheel mechanism and the wheel radius are identifed with respect to isotropic characteristic of mobile robots. In addition, the force transmission characteristics and actuator-sizing problem of mobile robots are investigated Analysis has been performed for three actuation sets. It is shown that the redundantly acruated mobile robot with three active caster wheels represents the best performance among them.

Command system and motion control for caster-type omni-directional mobile robot

Despite that omni-directional mobile robots have been investigated and used popularly, there are rarely useful command systems and control methods for practical applications. In this research, a command system to describe the navigation task for an omni-directional mobile robot is developed, and a motion generation method considering the robot geometry and the given trajectory is proposed. Furthermore, a control method to track the given linear and circular trajectories is proposed. A closed form kinematic model was utilized for motion generation and control as well. A redundantly actuated omni-directional mobile robot that consists of three caster-type wheel mechanisms with high performance embedded controller module was developed for the experiments. And some experiments in the real environment were carried out to verify the effectiveness of the proposed command system and the performance of the motion generation and the trajectory control method.

Performance Analysis of Sawing Based on Impulse Measure and Geometry—Dublahal Arm Approach

Some of manufacturing tasks, such as sawing, often require continuous impulsive motion. In the case of sawing, such impulsive motions can be observed between the teeth of the saw and the object. The amount of the external impulse exerted on the object has been treated as an important control parameter. At the same time, the internal impulses experienced at the joints should be taken into account to avoid serious damage or injury at the joints of robot. The purpose of this paper is to improve the efficacy of sawing by using dual arms. For this, we suggest an external impulse model, and introduce a new concept of effective mass that accounts for the hardness of the object to corroborate the effectiveness of the proposed chip model employed in the derivation of the external impulse model. Closed-form internal impulse models are also proposed for both single and dual arms. Based on these models, the paper proposes a new measure for internal impulse. A normalized impulse ellipsoid reflecting the velocity direction is employed to visualize the impact geometry. Finally, we identify the optimal sawing region, wherein there is a maximum amount of external impulse and a minimal amount of the internal impulse. Simulation and experimentation demonstrate that the dual arm exhibits a better sawing performance than a single arm, in terms of external and internal impulses.

Collision-free navigation based on people tracking algorithm with biped walking model

This paper investigates a collision-free navigation algorithm for a mobile robot to work in daily environment which includes multiple walking people. The proposed algorithm consists of two main parts: recognizing walking people and collision-free path planning. For the recognition of randomly changing human walking motion, a people tracking method using laser range finder (LRF) is utilized. Robust people tracking could be achieved by embedding a new biped model with a walking frequency tracker, which can estimate the frequency of human walking motion and generate it continuously even in the case of no measurement owing to occlusion. For the other part, collision-free path planning, an iterative forecast and planning method with the concept of configuration space including space and time is employed. The position and velocity of walking people, given from people tracking algorithm, are exploited in path planning part as the information of obstacles that the robot have to avoid. The proposed method has been implemented on a real robot. It is also demonstrated with some experiments the tracking of multiple walking humans and robot navigation in indoor environment.

Design of a parallel-type gripper powered by pneumatic actuators

A new parallel gripping mechanism is proposed in this work. This device has a parallelogramic platform which can be flexibly folded. Therefore, this mechanism not only can be used to grasp an object having irregular shape or large volume, but also can be utilized as micro-positioning device after grasping the object. Forward position analysis and platform kinematics are investigated to deal with motion tracking and force control. Pneumatic rotator is employed for actuation and a compact sized, 4/3 way proportional pressure valve is also developed to deal with feedback-based dynamic control. The pressure valve also allows indirect force control by measuring the offset pressure occurring due to contact between the grasped object and the parallel platform. In experimental work, performances of the motion tracking and indirect force control are shown satisfactory.

Robot motion generation considering external and internal impulses

This paper deals with motion generation algorithm considering the external and internal impulses. Initially, we analyze the trend of the impulses with a simple 3-DOF planar robot arm in various contact positions and present a problem that is in discord with the general trend. To cope with this problem, we propose a new motion generation algorithm considering both the external and internal impulse. The gradient projection method is employed to exploit the kinematic redundancy of robot systems. Initially, the effectiveness of the proposed algorithms is verified through simulation of a 3-DOF planar robot arm model, and two application examples considering both the external and internal impulses are investigated. Through simulations for the landing of a 4-DOF planar human-body model and the sawing motion by a dual-arm model, it is shown that the posture is stable and the trend of the impulses calculated by the analytical models is coincident to the human experiences.

Design and analysis of a parallel-type gripping and micro-positioning mechanism

In this work, a parallel-type gripping and micro-positioning mechanism is proposed. This device has a parallelogrammatic platform which can be flexibly folded. Therefore, this mechanism not only can be used to grasp an object having irregular shape or large volume, but also can be utilized as a micro-positioning device after grasping the object. Configuration parameter for configuration control of the proposed gripping mechanism is defined. Grasping force and isotropic characteristic of the mechanism are considered as design indices for optimal design of the gripping mechanism. The proposed mechanism is expected to be used as an adaptable gripping and micro-positioning device by attaching it at the end-point of a robot.

Design of multi-degree-of-freedom spring mechanisms: biomimetic approach

Springs have been employed in a wide range of mechanical systems. This work deals with the design of multi degree-of-freedom spring mechanism. The adaptable spring is desired for enhancing performances of various mechanical systems employing springs. We demonstrate that such adaptable springs can be realized by adopting anthropomorphic musculoskeletal structures of the human upper-extremity, which possesses highly nonlinear kinematic-coupling among redundant muscles existing in its structures. We propose multi-degree-of-freedom spring mechanisms resembling the musculoskeletal structure of the human upper-extremity, and verify the applicability of these mechanisms through simulation.

Detection of Hepatocellular Carcinoma in CT Images Using Deep Learning

The purpose of this paper is to develop a method to detect hepatocellular carcinoma, namely liver cancer in CT (Computerized Tomography) images using the deep learning that is a kind of AI (Artificial Intelligence). Firstly, the learning and recognition programs were developed using Python as a programming language and TensorFlow provided by Google that is a machine learning library. The CT images of 30 clinical subjects were selected from the DICOM format data provided by Graduate School of Medicine of Ehime University. Then 150 sets of CT images were selected where one set consists of two CT images for early and late phases in the cases with hepatocellular carcinoma. In addition, 150 sets of CT images were also selected in the cases without hepatocellular carcinoma. The 450 sets of CT images to each the 150 sets, namely 900 sets in total were created by rotating each original CT image. Consequently, 1,200 sets of CT images (2,400 CT images) in total were used for the learning. Then validity and usefulness of the learning and recognition programs were proved by examining the calculated results. This time, the hepatocellular carcinoma could be detected with relatively high sensitivity of 92.2% even with a relatively small number of learning data, namely 1,200 sets of CT

Filtering Algorithm for Reliable Localization of Mobile Robot in Multi-Sensor Environment

Localization problem of mobile robot in an environment of interest is one of research fields that are not still completely solved. Many methods to settle this problem are unceasingly being examined. Although a lot of researchers have dedicated themselves to this problem, it demands new but more accurate method by which mobile robot itself recognizes its own position while moving. It tries to find solution by making mention of various localization problems with estimation method in this chapter. Robot researchers had to recognize the current position information for movement control of mobile robot to last destination. It is being adopted from the conventional Kalman filter, which was proposed to reduce stochastic noise error, to complicated algorithms in the field of target tracking. The localization of the mobile robot occurred in various environments such as indoor, outdoor, warehouse, harbors, airports, and offices is based on estimation theories of target tracking field. The estimation method to be proposed in this chapter is mentioned with localization problem in the text. As the most widely used method for providing the current position of mobile robot, odometry is inexpensive method to implement in real time, but has vital demerit of accumulation of position error for long distance navigation. Various external sensors were used to supplement this problem, but the adopted sensors have also intrinsic errors. GPS, used as a global sensor in the outdoors, has a fatal weak point suffering from multi-path effects in the surrounding of buildings or trees. In this chapter, method for decreasing the above error occurred in localization problem are provided and embodied. This error is assumed as bias error. New nonlinear estimation method included in data integration method to reduce that error is derived and evaluated through experiment. The proposed integration method is provided to take full advantage of characteristics of mobile and outdoor environment sensors. 11