Tatsuya Ibuki

1. The bilateral teleoperation system has been a popular research topic in recent decades. It allows human operators to interact with and control remote robots in environments that are either inacc...

This article presents a novel method for environmental exploration that takes safety into account in unknown areas by using recursive Gaussian process regression (RGPR). Safety in unknown environments is ensured by an RGPR-based control barrier function, which are constructed non-parametrically from the posterior mean of RGPR. Simultaneously, an exploration function inspired by a control Lyapunov function is presented. This function utilizes the posterior variance of RGPR to achieve efficient exploration. Moreover, the use of RGPR mitigates the cubic growth in computational cost inherent to conventional Gaussian process regression, thereby enabling online learning. To validate the effectiveness of the proposed method, an experiment using differential-drive robots equipped with a LiDAR sensor is conducted.

This paper addresses distributed learning of a complex object for multiple networked robots based on distributed optimization and kernel-based support vector machine. In order to overcome a fundamental limitation of polynomial kernels assumed in our antecessor, we employ Gaussian kernel as a kernel function for classification. The Gaussian kernel prohibits the robots to share the function through a finite number of equality constraints due to its infinite dimensionality of the function space. We thus reformulate the optimization problem assuming that the target function space is identified with the space spanned by the bases associated with not the data but a finite number of grid points. The above relaxation is shown to allow the robots to share the function by a finite number of equality constraints. We finally demonstrate the present approach through numerical simulations.

This article proposes a collision avoidance method for ellipsoidal rigid bodies that utilizes a control barrier function (CBF) designed from a supporting hyperplane. We formulate the problem in the special Euclidean groups ${SE}(2)$ and ${SE}(3)$ , where the kinematics are described as rigid body motion (RBM). We consider the condition for separating two ellipsoidal rigid bodies by employing a signed distance from a supporting hyperplane of a rigid body to the other rigid body. Although a positive value of this signed distance implies that the two rigid bodies are collision-free, a naively designed supporting hyperplane yields a smaller value than the actual distance. To avoid such a conservative evaluation, the supporting hyperplane is rotated so that the signed distance from the supporting hyperplane to the other rigid body is maximized. We prove that the maximum value of this optimization problem is equal to the actual distance between two ellipsoidal rigid bodies, hence eliminating excessive conservativeness. We leverage this signed distance as a CBF to prevent collision while the supporting hyperplane is rotated via a gradient-based input. The designed CBF is integrated into a quadratic programming (QP) problem, where each rigid body calculates its collision-free input in a distributed manner, given communication among rigid bodies. We exemplify that our method can be extended to other kinematic models. The proposed method is demonstrated through simulation results.

This paper presents a novel structure for a quadrotor, making it hover only with clockwise rotors, where we denote it as a 4P (Positive) rotor. We first investigate the condition for a 4P rotor to hover. More specifically, we conduct the hoverability analysis, which is closely related to the controllability analysis, and show that the tilting angles of each rotor are crucial conditions for the hoverability of the 4P rotor. The derived condition for the tilting angles is then utilized to present two prototypes. Each prototype is developed with the following design purposes: One is to reduce the horizontal force generated by the tilted rotors, and the other is to make its torque generation the same as that of a standard quadrotor. The hoverability and dynamical property of both prototype is demonstrated in simulations and experiments. GRAPHICAL ABSTRACT

This paper addresses distributed learning of object shapes using multiple robots, and proposes a systematic design procedure for distributed optimization algorithms with data-independent performance certificates. We start with formulating the object shape learning as a distributed classification problem based on so-called kernel method. A distributed algorithm, continuous-time alternating direction method of multipliers, is then applied to the problem, wherein poor transient performances are observed. To improve the performance, we reformulate the classification problem so that singular values of sub-blocks in the algorithm are appropriately scaled. We then propose a systematic design procedure of the algorithm based on the concept of loop-shaping. The procedure is further extended so that the performance is independent of the data, and its effectiveness is verified through a numerical example. The proposed method is finally demonstrated through simulation on a high fidelity simulator. GRAPHICAL ABSTRACT