Toward a global picture of the radiation exposure of an area, particularly for fast emergency response, a UAV based exploration method is proposed. Without a priori knowledge of the radiation field, it is difficult to select the region of interest (ROI) which includes all radiation sources. For the case of a single radiation source, a greedy algorithm may localize the source by finding the maximum radiation value. However, when multiple sources generate a hotspot in a cumulative manner, the hotspot position does not coincide with one of the source positions. Therefore, we propose an efficient exploration method to quickly localize the radiation sources using the following procedures: (1) ROI selection using topographic maps with specific radiation level selection methods and (2) source localization estimating the number of sources and their positions with incremental variational Bayes inference of Gaussian mixtures. Under three different conditions according to the number of sources and their positions, we have shown that the proposed model can reduce the ROI and significantly improve the estimation accuracy than existing methods.

An unsupervised attention-path planning algorithm is proposed and applied to large unknown area classification with small field-of-view cameras. Attention-path planning is formulated as the sequential feature selection problem that greedily finds a sequence of attentions to obtain more informative observations, yielding faster training and higher accuracies. In order to find the near-optimal attention-path, adaptive submodular optimization is employed, where the objective function for the internal belief is adaptive submodular and adaptive monotone. First, the amount of information of attention areas is modeled as the dissimilarity variance among the environment data set. With this model, the information gain function is defined as a function of variance reduction that has been shown to be submodular and monotone in many cases. Furthermore, adapting to increasing numbers of observations, each information gain for attention areas is iteratively updated by discarding the non-informative prior knowledge, enabling to maximize the expected information gain. The effectiveness of the proposed algorithm is verified through experiments that can significantly enhance the environment classification accuracy, with reduced number of limited field of view observations.

Our paper newly presents unsupervised feature representation method for very low-resolution (VLR) images called informative census transform (ICT) based on statistical analysis of CT binary features and submodular optimization. A new cost function is designed to measure the informativeness of each binary feature: (1) an individual informativeness of features to represent unlabeled image dataset and (2) relative informativeness between binary features to represent different binary features. Therefore, we considered informativeness of binary feature according to two relationship (1) between feature space and image space, and (2) between different features within same feature space. Moreover, two constraints are designed by considering sub-modular characteristics to guarantee theoretical performance and fast optimization via simple greedy algorithm. Experimental results show that the proposed ICT features with two constraints outperforms the traditional CT features in terms of recognition performance and computational cost at VLR problem.

Biologically inspired control approaches have been attracted much attention as alternatives in recent time, for efficiently solving problems in controlling multi-DOF robotic systems, since most human beings or animals exhibit their behaviors in a natural way without explicit computation. Also, they show natural adaptive behaviors irrespective of unexpected external forces or changes of environment. This work is inspired from these novel features. Thus, a self-adapting robotic arm-hand control is proposed exploiting a control scheme based on central pattern generators (CPGs). Instead of a trajectory planning and inverse kinematics problem, this work endeavors to exploit robotic systems coupled with neural oscillators and virtual forces with joint velocity damping. We demonstrate self-adapting motions without the ill-posedness from extensive simulations that enable a robotic arm-hand to make adaptive changes from the given motion to a compliant motion. In addition, it is verified that reaching-to-grasping motion is possible by adopting only transit points sustaining motion repeatability under kinematic redundancy of joints.

This paper proposes a new attention-path planning algorithm that allows robots with limited sensing coverage to identify an unknown entity efficiently. Our focus is placed on how to plan optimal sequences of views to access more useful information needed to understand the entity. The adaptive submodular optimization technique guaranteed to achieve near-optimal performance is used to maximize the expected information gain. We verified the validity of the proposed approach to the face recognition problem through preliminary experiments.

One of the main reasons for using a joint torque sensor in a robot is to measure body forces acting on the system. Especially, the motor actuating torque and the external link torque are important for a flexible joint control. However, it is difficult to measure or estimate those two torques simultaneously with a single joint torque sensor only due to the lack of information. In this paper, Kalman Filter based two-port Body Force OBserver(KF 2-port BFOB) was proposed to estimate two output torques from the motor-side port and the link-side port simultaneously using the two-port system dynamics, the joint angle and the sensor torque from the joint torque sensor based on the Kalman filter. The basic idea of KF 2-port BFOB is illustrated and its performance is verified by implementing the algorithm with disturbance observer(DOB) in the motor actuated one-DOF flexible joint robot. The experiments are executed in the planar situation (under the gravity-free condition) and in the vertical situation(under the gravity condition) respectively to verify the performance of KF 2-port BFOB. The results show something with conclusion.

This paper presents a new control scheme of an assistive robotic cart that helps a user easily transport objects in various weights. The maneuverability of the cart would be highly affected by the loaded weight of the cart and the friction between the wheel and the floor. Our focus is placed on how to enable the cart to offer easy maneuverability by creating desired interactions with a user. For this, a switched observer based impedance control scheme is proposed to allow the cart to autonomously adapt to changes in the weight of the load and the friction. Specifically, a pre-determined impedance between the user and the cart is regulated to generate an assist force using the user's input force and the velocity of the cart. The switched observer is designed to estimate the loaded weight and the friction coefficient for the precise computation of the amount of assistance. Further, using the process integration and design optimization approach, the observer gains are automatically adjusted, resulting in enhancing the control performance. We describe the proposed scheme in detail, and perform extensive simulations to demonstrate its effectiveness.