The increasing penetration of renewable energy sources, particularly solar power, has led to significant challenges in balancing electricity generation and consumption due to mismatched timing. Shared Energy Storage System (Shared ESS) has emerged as a promising solution to address these issues by enabling efficient utilization of surplus electricity at the community level. This paper proposes a novel Model Predictive Control (MPC)-based Energy Management System (EMS) that integrates electric vehicles (EVs) as mobile energy transport devices with Shared ESS. The proposed system simultaneously optimizes EV transport schedules, charging/discharging profiles, and electricity trading prices between EVs and Shared ESS using a Mixed Integer Quadratic Programming (MIQP) approach.Simulation results demonstrate that the proposed method effectively reduces electricity purchase costs for a household and a facility. Additionally, the dynamic pricing mechanism based on Lagrangian relaxation ensures fairness and monetary benefits for EV users during surplus electricity periods.

A novel collision-free motion planner was proposed for tractor-trailer mobile robots (TTMRs) in a narrow environment with consideration of the polygonal shape of the TTMR and obstacles. The motion planner was designed as an iterative nonlinear optimization problem with a receding horizon similar to the model predictive control. Collision-free constraints with a configuration of the TTMRs were derived from the Farkas’ lemma with simplification, which were the hard constraints in the optimization problem. As such, the proposed method guarantees collision avoidance in its motion planning. The presented modified Farkas’ lemma stabled the fluctuated calculation time during the optimization. Numerical simulations confirmed the validity of the proposed method. A thorough evaluation of the dynamic environment with a moving obstacle was also carried out.

This study develops an immersive operation system for a hexapod robot equipped with a stereo camera, which allows the operator wearing head-mounted display (HMD) to control the robot in virtual reality (VR). The hexapod robot can be made to walk on uneven terrain with follow-the-contact-point gait control only when the contact points of the foremost legs are indicated by the operator. The environment surrounding the robot is constructed in VR based on the image captured by the stereo camera, and the operator indicates the contact points of the foremost legs through VR controllers. The proposed immersive operation system is compared with the conventional operation system in which the operator inputs the ground contact point with a mouse on 2D image. Experimental results establish that the proposed immersive operation system is more effective in controlling the movement of the robot across uneven terrain because it enables the operator to easily understand the unevenness and distance.

Ancillary services are becoming an indispensable tool for maintaining power grid stability due to the increasing adoption of renewable energy resources, many of which (e.g., wind and solar power) are inherently variable. Some energy resources, such as electric vehicles (EVs), have a significant potential for providing their own ancillary services and creating ancillary service markets in smart electric grids. The installation convenience of EVs and plug-in hybrid vehicles (PHVs) has made them the target of many studies. In previous works, the grid-integrated-vehicle (GIV) mechanisms are recognized as a suitable approach to exploit EVs and PHVs for ancillary service markets, particularly regulation markets, which require fast responses. It is important to consider individual consumption behavior (e.g., vehicle usage and energy consumption) in selecting optimal operational points of EV and PHV for maximizing resource effectiveness and user profit. There is, however, currently no mechanism that takes the individual consumption behavior of market participants into account. In this article, a new vehicle-to-home (V2H) aggregator is proposed, which allows individuals to participate in a regulation market using the in-vehicle batteries of their EVs or PHVs. The results show that the proposed V2H aggregator can successfully supply predictable power to the power grid and maximize the profits of individual market participants. Note to Practitioners-This article proposes an architecture of home energy management systems (HEMSs) with electric vehicles (EVs) and plug-in hybrid vehicles (PHVs) to participate in a regulation market using the in-vehicle batteries. Ancillary services are the mechanism for the power grid to ensure the quality of electricity. The proposed architecture is composed of two stages: 1) calculation of the charge and discharge profiles considering minimizing the electricity charge at home and maximizing the capacity to provide for ancillary services and 2) real-time control of charging and discharging the in-vehicle batteries to follow the regulation signal provided from the manager of ancillary services. The simulation result shows the estimated benefit of the aggregator obtained by the trade in the market and the precision of HEMSs' charging and discharging to follow the request signal.

In this paper, a new control method for a hexapod robot walking on irregular terrain based on a human operator's foot-placement navigation is proposed and evaluated. The control method is based on the Follow-the-contact-point (FCP) gait control that operates on the principle that each leg follows the contact point of its foreleg. Hence, planning the contact points for all the legs are summarized to one of the front legs. To dedicate the FCP gait control to a hexapod robot, three control architectures are added. First, new constraints in the transition from a stance phase to a swing phase are added to maintain static stability when a leg leaves the ground. Second, a real-time posture control system for contacting legs is added. The third is an adaptive control to adjust the time elapsed in each control mode, by which specifications of deadlock-free and static stability are satisfied. The proposed control architecture is installed to a small hexapod robot, and its performance is evaluated through experiments wherein the robot walks on uneven terrain.

Aggregators in smart grid are business operators providing services to aggregate and visualize electricity information, balance demand and supply, reduce energy consumption, and do the other activities with Energy Management Systems (EMSs). An aggregator for Apartment Building Energy Management Systems (Apartment-BEMSs) balances demand and supply considering all households in its building. The buildings with the BEMS are generally equipped with stationary batteries, PV generators, or the other devices to reduce energy cost and wasteful consumption. On the other hand, energy storages such as high capacity batteries are necessary to balance demand and supply in power grid installed a large amount of renewable energy such as solar power or wind power. However, these installation cost is expensive. Therefore alternative storages are required in some cases. As an approach, Vehicle-to-X (V2X) is carried out promptly. The V2X is a utilization of in-vehicle batteries installed in Electric Vehicles (EVs) and Plug-in Hybrid Electric Vehicles (PHEVs) instead of stationary batteries. Then, the authors target constructing an effective EMS for apartment buildings with Vehicle-to-Home (V2H) systems. As the latest work, the authors propose an improved apartment BEMS considering characteristics of actual devices. Through computational experiments based on real data observed and measured in actual society, the authors show the effectiveness of the improved BEMS.

Gait recognition is a promising non-intrusive biometric method. By using kinematical cues, a new gait recognition model which synthesizes dynamic model and statistical model is proposed. The proposed model allows us to establish security applications without the aids of shape cues deriving from marker-dependent motion capture cameras, for it only exploits low-dimensional kinematical cues. Angular variables of ankle joint are adopted as the model's input, and a 2-link virtual passive walking model plays an important role both in the configuration of the parameter matrix and the selection of the matrix's initial values. By evaluation the recognition rates of different models, the velocity-robust characteristics of the new model and its low computational load compared with conventional HMM are verified. Furthermore, the dynamics of human walking, the kinematical cues derived from the dynamics model, and any accessible position cues extracted either from pictorial data or ranging sensor can be integrated into a coherent system.

This research addresses a method for mobile robots that simultaneously performs map building and path-planning on line. A graph representation of a workspace with variable resolutions is constructed using measurement data obtained by omni-directional distance sensors. At the same time, a real-time search for a feasible path to the goal is executed on the constructed graph-map. The proposed method is evaluated through experiments using an omni-directional mobile robot equipped with laser range finders.

This paper proposes a novel locomotion control scheme of centipede-like multi-legged robot, which is called Follow-the-Contact-Point (FCP) gait control. A centipede-like multi-legged robot is composed of segmented trunks which have a pair of legs and are connected with fore and/or rear ones by joints. This control scheme realizes locomotion control of multi-legged robot on uneven terrain with perfectly decentralized manner. The main concept of the control scheme is to relay the contact points from the fore leg to the rear leg. By creating contact points of the first legs adequately on the environment, the robot can climb over obstacles and be navigated successfully. Finally, the result of physical simulation of a 20-legged robot shows the availability of the proposed method.

Inspired by forensic experts working on authentication of oriental characters like Chinese and Japanese who usually rely on distinguishing detailed features of individual strokes such as dots and straight lines, our new HMM model consisted of many sub-models each represents an individual stroke of a signature. Furthermore, 3 models were compared in 2 steps using a hierarchical manner. First, original user was distinguished from data corpus consisted of random forgeries; Secondly, original user was distinguished from skilled forgeries.

This paper presents a new control methodology for a nonholonomic electric two-wheeled vehicle wherein the autonomous and man-machine cooperative controls are synthesized. In the proposed control scheme, the dasiaautonomous controlpsila and the dasiaman-machine cooperative controlpsila are designed by synthesizing time-state control and impedance control. The time-state controller tries to reduce the machine's deviation from the guideline, the impedance controller, on the other hand, generates power to assist the operator's maneuver. Furthermore, experimental results are shown to demonstrate the usefulness of the proposed strategy.

This paper presents a new hierarchical segmentation of the observed driving behavioral data based on the multiple levels of abstraction of the underlying dynamics. By synthesizing the ideas of a feature vector definition revealing the dynamical characteristics and an unsupervised clustering technique, the hierarchical segmentation is achieved. The identified mode can be regarded as a kind of symbol in the abstract model of the behavior. Second, the grammatical inference technique is introduced to develop the context-dependent grammar of the behavior, i.e., the symbolic dynamics of the human behavior. In addition, the behavior prediction based on the obtained symbolic model is performed.

To realize the harmonious cooperation with the operator, the man-machine cooperative system must be designed so as to accommodate with the characteristics of the operator's skill. One of the important considerations in the skill analysis is to investigate the switching mechanism underlying the skill dynamics. On the other hand, the combination of the feedforward and feedback schemes has been proved to work successfully in the modeling of human skill. In this paper, a new stochastic switched skill model for the sliding task, wherein a minimum jerk motion and feedback schemes are embedded in the different discrete states, is proposed. Then, the parameter estimation algorithm for the proposed switched skill model is derived. Finally, some advantages and applications of the proposed model are discussed.

This paper presents the development of a mathematical model of the human decision making. The main contributions of this paper are introduction of the logistic regression model as the mathematical model of the decision, development of the real time prediction of the decision based on the model, and proposal of some useful quantified measures to evaluate the characteristics of the human behavior. The proposed modeling and analysis strategies are applied to the driving behavior, in particular, focusing on the turn-right task in the intersections. Furthermore, two quantified measures “decision entropy” and “decision aggressiveness” are defined based on the estimated parameters in the logistic regression model. The objective evaluation made by these measures agrees well with the subjective evaluation made by the questionnaires.

This paper presents the development of the modeling and recognition of human driving behavior based on a stochastic switched autoregressive exogenous (SS-ARX) model. First, a parameter estimation algorithm for the SS-ARX model with multiple measured input-output sequences is developed based on the expectation-maximization algorithm. This can be achieved by extending the parameter estimation technique for the conventional hidden Markov model. Second, the developed parameter estimation algorithm is applied to driving data with the focus being on driver's collision avoidance behavior. The driving data were collected using a driving simulator based on the cave automatic virtual environment, which is a stereoscopic immersive virtual reality system. Then, the parameter set for each driver is obtained, and certain driving characteristics are identified from the viewpoint of switched control mechanism. Finally, the performance of the SS-ARX model as a behavior recognizer is examined. The results show that the SS-ARX model holds remarkable potential to function as a behavior recognizer.

This paper presents a new mathematical model for the human behavior called stochastic switched linear dynamical (SS-LD) model. The SS-LD model can be regarded as a natural extension of the conventional hidden Markov model (HMM), where different linear dynamical model is allocated to each discrete state of the HMM to represent continuous dynamics. Then, the parameter identification technique for SS-LD model is developed based on the EM algorithm. Finally, we apply the proposed behavior model to a typical man-machine cooperative system, and the usefulness of the proposed model is verified through some experiments