This paper suggests the method of the spherical signature description of 3D point clouds taken from the laser range scanner on the ground vehicle. Based on the spherical signature description of each point, the extractor of significant environmental features is learned by the Deep Belief Nets for the urban structure classification. Arbitrary point among the 3D point cloud can represents its signature in its sky surface by using several neighborhood points. The unit spherical surface centered on that point can be considered to accumulate the evidence of each angular tessellation. According to a kind of point area such as wall, ground, tree, car, and so on, the results of spherical signature description look so different each other. These data can be applied into the Deep Belief Nets, which is one of the Deep Neural Networks, for learning the environmental feature extractor. With this learned feature extractor, 3D points can be classified due to its urban structures well. Experimental results prove that the proposed method based on the spherical signature description and the Deep Belief Nets is suitable for the mobile robots in terms of the classification accuracy.
For a practical mobile robot team such as carrying out a search and rescue mission in a disaster area, the localization have to be guaranteed even in an environment where the network infrastructure is destroyed or a global positioning system (GPS) is unavailable. The proposed architecture supports localizing robots seamlessly by finding their relative locations while moving from a global outdoor environment to a local indoor position. The proposed schemes use a cooperative positioning system (CPS) based on the two-way ranging (TWR) technique. In the proposed TWR-based CPS, each non-localized mobile robot act as tag, and finds its position using bilateral range measurements of all localized mobile robots. The localized mobile robots act as anchors, and support the localization of mobile robots in the GPS-shadow region such as an indoor environment. As a tag localizes its position with anchors, the position error of the anchor propagates to the tag, and the position error of the tag accumulates the position errors of the anchor. To minimize the effect of error propagation, this paper suggests the new scheme of full-mesh based CPS for improving the position accuracy. The proposed schemes assuring localization were validated through experiment results.
Increasing interest of human health, building bio-database (Bio DB) has been become a hot issue in life science. Consequently, Single Cell Analysis (SCA) which can explain biodiversity of lives has been a significant factor for building Bio DB. In numerous studies from these analyses, they have been showed that mechanical properties of cells can serve explanation of biological heterogeneity and criterion of disease states. Therefore, measuring mechanical properties of cells have great potential to be used in bio-medical applications. However, traditionally, many researchers have undergone difficult and time consuming work because handling small sized cells usually requires high-skilled technique. Thus, this paper shows robotized stiffness measurement technique using fixed ended beam sensor, precision motorized stage and substrate which have wall structure.
In this paper, we fabricate arrayed-type flexible capacitive touch sensor using liquid metal (LM) droplets (4 mm spatial resolution). Poly-4-vinylphenol (PVP) layer is used as a dielectric layer on the electrode patterned Polyethylene naphthalate (PEN) film. Bonding tests between hydroxyl group (-OH) on the PVP film and polydimethylsiloxane (PDMS) are conducted in a various O2 plasma treatment conditions. Through the tests, we can confirm that non-O2 plasma treated PVP layer and O2 plasma treated PDMS can make a chemical bond. To measure dynamic range of the device, one-cell experiments are conducted and we confirmed that the fabricated device has a large dynamic range (~60 pF).
Localization of underwater vehicle is essential to use underwater robotic systems for various applications effectively. For this purpose, this paper presents a method of two-dimensional SLAM for underwater vehicles equipped with two hydrophones. The proposed method uses directional angles for underwater acoustic sources. A target signal transmitted from acoustic source is extracted using band-pass filters. Then, directional angles are estimated based on Bayesian process with generalized cross-correlation. The acquired angles are used as measurements for EKF-SLAM to estimate both vehicle location and locations of acoustic sources. Through these processes, the proposed method provides reliable estimation for two dimensional locations of underwater vehicles. Experimental results demonstrate the performance of the proposed method in a real sea environment.
For the underwater localization, acoustic sensor systems are widely used due to greater penetration properties of acoustic signals in underwater environments. On the other hand, the good penetration property causes multipath and interference effects in structured environment too. To overcome this demerit, a localization method using the attenuation of electro-magnetic(EM) waves was proposed in several literatures, in which distance estimation and 2D-localization experiments show remarkable results. However, in 3D-localization application, the estimation difficulties increase due to the nonuniform (doughnut like) radiation pattern of an omni-directional antenna related to the depth direction. For solving this problem, we added a depth sensor for improving underwater 3D-localization with the EM wave method. A micro scale pressure sensor is located in the mobile node antenna, and the depth data from the pressure sensor is calibrated by the curve fitting algorithm. We adapted the depth(z) data to 3D EM wave pattern model for the error reduction of the localization. Finally, some experiments were executed for 3D localization with the fast calculation and less errors.
A modular manipulator in serial-chain structure usually consists of a series of modularized revolute joint and link modules. The geometric shapes of these modules affect the number of possible configurations of modular manipulator after assembly. Therefore, it is important to design the geometry of the joint and link modules that allow various configurations of the manipulators with minimal set of modules. In this paper, a new 1-DoF(degree of freedom) joint module and simple link modules are designed based on a methodology of joint configurations using a series of Rotational(type-R) and Twist(type-T) joints. Two of the joint modules can be directly connected so that two types of 2-DoFs joints could be assembled without a link module between them. The proposed geometries of joint and link modules expand the possible configurations of assembled modular manipulators compared to existing ones. Modular manipulator system of this research can be a cornerstone of user-centered markets with various solution but low-cost, compared to conventional manipulators of fixed-configurations determined by the provider.
Lane-level vehicle positioning is an important task for enhancing the accuracy of in-vehicle navigation systems and the safety of autonomous vehicles. GPS (Global Positioning System) and DGPS (Differential GPS) are generally used in navigation service systems, which however only provide an accuracy level up to 2~3 m. In this paper, we propose a 3D vision based lane-level positioning technique which can provides accurate vehicle position. The proposed method determines the current driving lane of a vehicle by tracking the 3D position of traffic signs which stand at the side of the road. Using a stereo camera, the 3D tracking paths of traffic signs are computed and their projections to the 2D road plane are used to determine the distance from the vehicle to the signs. Several experiments are performed to analyze the feasibility of the proposed method in many real roads. According to the experimental results, the proposed method can achieve 90.9% accuracy in lane-level positioning.
Generating motion of center of mass for biped robots is a challenging issue since biped robots can easily lose balance due to limited contact area between foot and ground. In this paper, we propose force control method to generate high-speed motion of the center of mass for horizontal direction without losing balancing condition. Contact consistent multi-body dynamics of the robot is used to calculate force for horizontal direction of the center of mass considering balance. The calculated force is applied for acceleration or deceleration of the center of mass to generate high speed motion. The linear inverted pendulum model is used to estimate motion of the center of mass and the estimated motion is used to select either maximum or minimum force to stop at goal position. The proposed method is verified by experiments using 12-DOF torque controlled human sized legged robot.