This paper presents a novel cooperative estimation algorithm for visual sensor networks. We consider the situation where multiple smart vision cameras with computation and communication capability see different target objects. The objective of the present algorithm is to meet two requirements: (i) gaining estimates close to an average pose for static objects and (ii) tracking of estimates to moving objects' poses. In order to meet the requirements simultaneously, we present a cooperative estimation algorithm based on passivity of the kinematic model of rigid body motion. Though the present algorithm embodies both properties from its structure, we restrict our theoretical interests to averaging and provide an upper bound of the ultimate error between the actual average and the estimates given by the present algorithm.
We propose methods for object localization by cooperation between human position sensing and active Radio Frequency Identification (RFID) system in a home environment. The traditional object localization methods based on received signal strength indicator from active RFID system suffer from the distortion of RF signal caused by human existence. Two proposed methods improve this problem using a human position sensing system and pattern recognition technique. One method selects the classifiers based on human position in object localization. The other method narrows location candidates using position data and detection data of human handling motion with the sensor embedded in the active RFID tag. The experiment in the human natural handling scenario demonstrated that the methods improved the performance of object localization in human existence.
A method for measuring motorcycle trajectory using GPS is needed for simulating motorcycle dynamics. In GPS measurements of a motorcycle, both the declination of the motorcycle and obstacles near the course can cause problems. Therefore, we propose a new algorithm for GPS measurement of motorcycle trajectory. We interpolate the missing observation data within a few seconds using polynomial curves, and use a Kalman filter to smoothen position calculations. This results in obtaining trajectory with high accuracy and with sufficient continuity. The precision is equal to that of fixed point positioning, given a sufficient number of available satellites.
In this paper, we introduce a wearable accelerometer array system and propose a method for analysis of human movements. While many studies of sensor networks have aimed at scalability and/or accuracy, our concept is to introduce “redundancy” in the array to measure movements in human bodies. In particular, we gather data from as many points as possible, more than kinematic degrees of freedom, and then obtain various information by data mining. We developed an accelerometer array system based on our “redundant numbered BSN (rBSN)” concept. It measures 46 channels of 3-axis (15 channels of 6-axis) acceleration data in 1500Hz synchronously. We also evaluate our accelerometer array system in two applications discuss the applicability of our rBSN concept. Specifically, we describe methods to estimate arm stiffness as well as center of gyration of human movement.
In this paper, we propose a method for tracking groups of people using three-dimensional (3D) feature points obtained with use of the Kanade-Lucas-Tomasi feature tracker (KLT) method and a stereo camera system called “Subtraction stereo”. The tracking system using subtraction stereo, which focuses its stereo matching algorithm to foreground regions obtained by background subtraction, is realized using Kalman filter based tracker. The effectiveness of the proposed method is verified using 3D scenes of people walking, which are difficult to track.
In this paper, we deal with a problem to capture a target by linear multi-agent systems where the agents behave autonomously, whereas the target escapes with a reasonable strategy. We consider two cases for the dynamics of the agents and the target. First, for the simple dynamic case, we give a necessary and sufficient condition for the success of the capturing. Then, we extend the results to the general dynamics case and give similar sufficient conditions. The conditions clarify the performance competition between the target and the agents and we propose preferable strategies for them. We also demonstrate the results by using numerical simulations.
This paper deals with an output consensus problem of multiple agents and first presents a centralized algorithm for solving it by a model predictive control method based on linear matrix inequalities. It is shown that the outputs of all the agents controlled by the presented method asymptotically converge to a common point, i.e., a consensus point. Then two kinds of algorithms for solving the consensus problem in a decentralized way are presented by using primal and dual decomposition methods. In general, these algorithms require a large number of iterations, i.e., a large number of communications between agents. To cope with this communication burden, a method that can reduce the number of iterations and guarantee the convergence to a consensus point is proposed by exploiting the property that the primal and dual decomposition methods can give upper and lower bounds of the optimal value of the optimization problem to be solved. A numerical example is given to illustrate the effectiveness of the proposed method.
The authors have recently proposed a class of randomized gossip algorithms which solve the distributed averaging problem on directed graphs, with the constraint that each node has an integer-valued state. The essence of this algorithm is to maintain local records, called “surplus”, of individual state updates, thereby achieving quantized average consensus even though the state sum of all nodes is not preserved. In this paper we study a modified version of this algorithm, whose feature is primarily in reducing both computation and communication effort. Concretely, each node needs to update fewer local variables, and can transmit surplus by requiring only one bit. Under this modified algorithm we prove that reaching the average is ensured for arbitrary strongly connected graphs. The condition of arbitrary strong connection is less restrictive than those known in the literature for either real-valued or quantized states; in particular, it does not require the special structure on the network called balanced. Finally, we provide numerical examples to illustrate the convergence result, with emphasis on convergence time analysis.
A networked control system (NCS) is a control system in which plants, sensors, controllers, and actuators are connected through communication networks. In this paper, as one of the design problems for NCSs, we consider optimal sampled-data control of linear systems with uncertain input delay and uncertain sampling period. First, an input delay system is transformed into a discrete-time system with parameter uncertainty. Furthermore, the obtained system is expressed as a mixed logical dynamical model by using our previously proposed modeling method. Next, a given continuous-time cost function is transformed into a discrete-time cost function with parameter uncertainty, and the optimal control problem is approximately expressed as a mixed integer programming problem.
This paper investigates state estimation performance of sensor systems with synchronous sampling and systems with asynchronous sampling. In a sensor system with synchronous sampling, all the sensors work at the same time. Sampling times of sensors with asynchronous sampling are determined randomly and not constant. This paper shows that synchronous sampling does not always yield better estimation performance than asynchronous sampling.
The relationship between the critical probability of gossip protocol on the square lattice and the critical probability of site percolation on the square lattice is discussed. Specifically, these two critical probabilities are analytically shown to be equal to each other. Furthermore, we present a way of evaluating the critical probability of site percolation by approximating the saturation of gossip protocol. Finally, we provide numerical results which support the theoretical analysis.
A new middleware framework that is implemented as the RIBSI (Real-time Interactive Behavior-Sensing Interface) middleware on our original humanoid morph2 is proposed. A hierarchical control system architecture for humanoids is developed for morph2 that consists of a device layer, a middleware layer, which is the proposed RIBSI middleware, and an application layer. The RIBSI middleware provides the upper layer of the humanoid system with individual motor devices, integrated virtual action generation devices, individual sensor devices and virtual sensors integrating sensor information. The proposed middleware works as an infrastructure of the humanoid control system, and can respond to asynchronous requests from the upper layer. In order to verify the capabilities of the proposed middleware, the humanoid control system implementing the RIBSI middleware is evaluated not only on humanoid morph2 but also on a quadrupedal locomotion robot.