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.

In the manufacturing industry, the Facility Layout Problem (FLP) focuses on determining the optimal arrangement of facilities to minimize production costs, primarily by reducing the total transportation cost between facilities. As FLP is a complex combinatorial optimization problem, evolutionary algorithms such as Genetic Algorithms (GA) are often employed, requiring repeated evaluation of transportation costs. To reduce computational time, simple distance measures like Euclidean distance, Manhattan distance, or shortest paths obtained via the A* algorithm are commonly used. However, these measures do not account for collisions between transporters and facilities, leading to unrealistic solutions. Although Multi-Agent Path Finding (MAPF) algorithms are well known for calculating collision-free shortest paths, their high computational complexity makes them impractical for repeated evaluation within evolutionary frameworks. To address this challenge, this study proposes a method to reduce computation time by partitioning the MAPF problem using a divide-and-conquer approach. We demonstrate that dividing the problem reduces the number of transporters considered in each subproblem, significantly decreasing overall computation time. Furthermore, we show that additional speedup can be achieved through parallel computation of the subproblems.

In this paper, first of all, the group walking behavior by four pedestrians are observed. In the observation, not only the motion data but also the decision making of each pedestrian are collected by using special device. Then, three behavioral indicators: deceleration, detour amount, and decision entropy, are defined and calculated. It has been found that these three indicators successfully quantify the ’smoothness’of the group walking behavior. Finally, the principal component analysis(PCA) is applied to the three dimensional indicator data. As the result, the meaning of three principal components are clearly explained. The discussion based on the PCA will be a basis for the further analysis and classification of the group walking behavior.

Smooth and safe interactions between pedestrians and Autonomous Mobile Robots (AMRs) are crucial for integrating robotic systems into shared environments. Previous studies on external Human-Machine Interfaces (eHMIs) often employed static information presentation, neglecting dynamic interaction contexts. This study investigates the effectiveness of gaze-based nudge-intuitive and unconscious communication via gaze behavior—in facilitating pedestrian role selection (leader or follower) during perpendicular crossing interactions with AMRs. Virtual reality (VR) experiments using Unity and Cybershoes are conducted to evaluate two gaze patterns generated by the AMR: ‘Leader’s gaze’, a brief gaze directed toward pedestrians in the early stages of interactions, and ‘Follower’s gaze’, a gaze keeping track of pedestrians throughout the interaction until crossing completion. The impact of gaze timing (Early/Late) and initial positional relationships between pedestrians and AMRs (initial ∆TTCP) are systematically analyzed. The results indicate that gaze nudging significantly enhances pedestrians’ subjective ratings of safety, smoothness, and understanding of the robot’s intention compared to no-gaze conditions. Leader’s gaze effectively encourages pedestrians to adopt the follower role under conditions favoring AMR priority (small or negative ∆TTCP), whereas Follower’s gaze promotes pedestrians to adopt the leader role under conditions naturally favoring pedestrian priority (larger ∆TTCP). Additionally, the effectiveness of gaze nudging strongly depends on interaction timing, with early-stage gaze presentations exhibiting greater influence. These findings confirm the potential of gaze-based nudging as a non-intrusive, context-sensitive strategy for pedestrian-AMR interactions, emphasizing the importance of precisely timed gaze presentations for facilitating pedestrians’ natural and intuitive role selection.

This paper presents a novel Stochastic Optimal Control (SOC) method based on Model Predictive Path Integral control (MPPI), named Stein Variational Guided MPPI (SVG-MPPI), designed to handle rapidly shifting multimodal optimal action distributions. While MPPI can find a Gaussian-approximated optimal action distribution in closed form, i.e., without iterative solution updates, it struggles with the mul-timodality of the optimal distributions. This is due to the less representative nature of the Gaussian. To overcome this limitation, our method aims to identify a target mode of the optimal distribution and guide the solution to converge to fit it. In the proposed method, the target mode is roughly estimated using a modified Stein Variational Gradient Descent (SVGD) method and embedded into the MPPI algorithm to find a closed-form "mode-seeking" solution that covers only the target mode, thus preserving the fast convergence property of MPPI. Our simulation and real-world experimental results demonstrate that SVG-MPPI outperforms both the original MPPI and other state-of-the-art sampling-based SOC algorithms in terms of path-tracking and obstacle-avoidance capabilities. https://github.com/kohonda/proj-svg_mppi

Four-wheel independent drive and steering vehicle (4WIDS Vehicle, Swerve Drive Robot) has the ability to move in any direction by its eight degrees of freedom (DoF) control inputs. Although the high maneuverability enables efficient navigation in narrow spaces, obtaining the optimal command is challenging due to the high dimension of the solution space. This paper presents a navigation architecture using the Model Predictive Path Integral (MPPI) control algorithm to avoid collisions with obstacles of any shape and reach a goal point. The key idea to make the problem easier is to explore the optimal control input in a reasonably reduced dimension that is adequate for navigation. Through evaluation in simulation, we found that the selecting sampling space of MPPI greatly affects navigation performance. In addition, our proposed controller which switches multiple sampling spaces according to the real-time situation can achieve balanced behavior between efficiency and safety.Source code is available at https://github.com/MizuhoAOKI/mppi_swerve_drive_ros.

This study presents a new framework for vehicle motion planning and control based on the automatic generation of model predictive controllers (MPCs) named MPC Builder. In this framework, several components necessary for MPC, such as prediction models, constraints, and cost functions, are prepared in advance. The MPC Builder then generates various MPCs online in a unified manner according to traffic situations. This scheme enabled us to represent various driving tasks with less design effort than typical switched MPC systems. The proposed framework was implemented considering the continuation/generalized minimum residual (C/GMRES) method optimization solver, which can reduce computational costs. Finally, numerical experiments on multiple driving scenarios were presented.

Model predictive control (MPC) has been widely used for controlling multi-input multi-output (MIMO) systems. MIMO systems might consist of dynamics with different response speeds. Therefore, different horizons and prediction rates should be applied according to the response speed of each dynamic. However, multi-horizon and multi-rate prediction leads to mismatches of prediction points and results in prediction difficulties. In addition, multiple control rates should also be considered due to hardware constraints. This paper presents a multi-horizon and multi-rate MPC (MM-MPC) with zero-order hold interpolation for dealing with the mismatches of prediction points. Furthermore, by running MM-MPCs at different rates, a multi-control-rate system is constructed without ignoring the dynamic interaction between the dynamics. The presented methods were demonstrated through simulations. The results show that the presented MM-MPC can reach a better overall performance compared to conventional unified MPCs. In addition, the multi-control-rate system consisting of MM-MPCs with multiple execution rates reduces the average computation time without deteriorating the performance.

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.

Safety and acceptability are the main concerns in the design of driver assistance systems. However, these two requirements sometimes conflict with each other depending on the situation and the driver. This conflict is particularly emphasized in the case of elderly drivers. To solve this problem, this paper proposes a driver-vehicle cooperation scheme, an "instructor-like assisting control" consisting of model predictive constraint satisfaction and a multi-modal human-machine inter-face. The proposed assisting scheme is expected to improve the drivers’ inherent driving characteristics, which is recognized as a "coaching effect" in cognitive science. This effect was verified by long-term experiments over one month using a driving simulator.

This study compares and evaluates the effect of the choice of the vehicle's prediction model on the performance in designing a path-tracking controller for vehicles using Model Predictive Control (MPC). The Kinematic Ackermann Model (KAM), the Kinematic Bicycle Model (KBM), and the Dynamic Bicycle Model (DBM) are well known as nonlinear prediction models. The stability and tracking performance of these models are evaluated using simulations, and a newly proposed DBM improved in Low-speed range (DBM-L) is also compared. As a result of the simulation, the proposed DBM-L was able to run in the widest 0 to 120km/h speed range among the models tested, and it was able to achieve the stop-and-go behavior that was not possible with the conventional DBM. In the future, if we can solve the problem that the tracking accuracy of the DBM-L is slightly decreased in the extremely low and high speed ranges, a vehicle prediction model that can be used in all speed ranges is expected to be realized.

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.

Advanced driver assistance systems should not only make the driving experience safer and more comfortable, it should also have a positive effect on driving behaviors. In this paper, an instructor-like assistance system for collision avoidance is developed and realized on an actual vehicle. The proposed system is activated only if the driver is not operating the vehicle properly when facing a collision risk. The vehicle control is shared by the driver and the assistance system. It is controlled by servomotors. In order to fulfill this requirement, a constraint satisfaction problem (CSP) is proposed and solved based on safe driving constraints and predictive vehicle states. Vehicle motion is predicted by a combination of a dynamics model and a potential field model that reflects the driver's risk feeling to an obstacle. Improved driving behavior is verified and evaluated quantitatively based on driving simulator data. By comparing the driving data before and after using the assistance system, it is found that distance is increased and speed is reduced when passing an obstacle. As a result, driving behavior becomes safer for collision avoidance due to the system's instruction. Furthermore, an experiment with an actual vehicle also demonstrates the practicability of the control system and shows the influence of different safe driving constraints.

This study models the decision-making characteristics of a driver regarding whether he accepts a merging car at a highway junction. Then, the application of the modeling to the design of merging behavior control is proposed. First, the driving behavior on the main lane at a highway junction is observed using a driving simulator, particularly focusing on the driver's state of decision (SOD), which represents the acceptance for merging a car coming from the merging lane. Second, the driver's SOD is modeled using a logistic regression model and the prediction performance of the identified model is verified. Finally, the speed controller of the merging car is designed to maximize the acceptance from the cars on the main lane. The key idea here is to minimize the entropy of the SOD of the driver on the main lane by optimizing the speed of a merging vehicle. This problem is quantitatively formulated using an identified decision-making model and addressed by applying a randomized approach to the optimization. This enables the automated vehicle to realize a considerate merging behavior at a highway junction. Numerical experiments are performed to demonstrate the usefulness of the proposed design scheme.

In this research, an innovative framework that taking advantage of unsupervised clustering and variable selection method is proposed for the modeling of car-following behavior, suitable for incorporating explainable microscopic traffic models into understanding driver behavior. The proposed framework retains the advantages of both conventional and data-driven method. The experimental result presented in this paper shows that the unsupervised clustering method helps identify driver behaviors naturally in an intelligible way, while variable selection has shown a good property of identifying the true model of driving task while efficiently reducing model complexity. Especially, the proposed framework is demonstrated using real-world data collected from a sequence of instrumented install on a driving vehicle in Sakae, downtown area of Nagoya city, Japan. Gazis-Herman-Rothery (GHR) models, one of the most extensively used non-linear car-following models is calibrated against the same data and used as a reference benchmark.

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.

This paper presents the application of Graphics Processing Unit (GPU) to improve the control performance of sampling based predictive control algorithms. As an example problem, obstacle avoidance situation with parked cars in a street is modeled as a non-linear model predictive control problem. Car dynamics and non-linear constraints are considered to achieve collision avoidance. The control input must be optimized in every control step in real-time considering the non-linear constraints. Sampling based approach is used to solve this problem and one of the major limitations to this approach is the computational cost involved. In this paper, the sampling-based optimization algorithm was adapted to utilize the parallel computing capabilities of GPU using CUDA. The generated input sequence and the computational speeds were compared with a CPU based program for the same case. The proposed method is implemented in a simulation experiment with car dynamics simulator to verify its performance in terms of path tracking. Finally, a general relationship between sample size and GPU acceleration of its calculation speed is also discussed.

This paper proposes a model predictive controller to perform the lane merging task. We consider a hierarchical control structure which consists of an inner control loop and an outer one. The inner loop is an adaptive cruise controller which gives the acceleration command to the autonomous vehicle to follow the designated speed and keep a relative distance with the preceding vehicle while satisfying constraints. The outer loop is to determine the vehicle in the main lane that the autonomous one should follow to minimize the entropy in the decision making of the human drivers in the main lane. It is verified in the simulation that the proposed controller can complete the lane merging task under uncertainties and modeling errors.

This paper presents a new control method for merging task at highway junction by using the model predictive control in which the decision entropy of the drivers on the main lane is explicitly considered as the cost function. Authors have already proposed the evaluation measure for the acceptance of the drivers on the main lane (supposed to be manual cars) to the merging car approaching from the merging lane (supposed to be automated car). In addition, the decision entropy of the driver on the main lane has been formally defined by using the stochastic model of the decision making. Based on this previous study, a new control method for the merging task of the automated car is addressed. The control problem is formulated so as to find the optimal speed of the merging car which minimizes the decision entropy of the drivers on the main lane. The proposed control strategy achieves the harmonized merging task in a sense that the drivers on the main lane can easily decide whether to accept or reject the cut-in of the merging car. The model predictive control is formulated as a nonlinear optimization problem, and solved by using the randomized approach. Finally, the validity of the proposed method is verified through some simulation studies.

A new approach to evaluate personalized energy consumption is presented in this paper. The method consists of identifying driver-vehicle dynamics using the probability weighted autoregressive model, which is one of the multi-mode ARX models, and then of reproducing the driver-vehicle behavior in a vehicle-following task. The energy consumption of the vehicle is estimated from the velocity profile calculated by using the driver-vehicle model. In this paper, driving simulator and real-world driving data were recorded to identify the driver-vehicle model in various situations. As a result, real-world energy consumption could be reproduced in a variety of situations with an average error of 1.9% and a standard deviation within 1.5%. Several promising applications of the energy consumption evaluation are introduced in this paper, such as an online energy consumption prediction, a powertrain choice-assistance system for car buyers, and a solution to estimate the macroscopic energy consumption of aggregated vehicles in a traffic flow.

This paper presents a control system for autonomous driving based on MPC in which driving style can be easily modified by changing control parameters. Each of the motion controls for the longitudinal and the lateral direction are formulated as the model predictive control problem. Finally the experimental verification by using driving simulator and a real electric vehicle is performed by implementing MPC on each platform, and it is confirmed that the proposed system can produce a large variety of driving characteristics. The implemented MPC package will also be beneficial to the developers and researchers in various fields other than control engineering field.

This paper proposes a new method to analyze driver behavior. Analysis of the behavior is done through the observation of the time-evolution of parameters of simple driver models. The behavior analysis is decomposed in two steps. First the driver model have to be selected or designed to represent the average behavior of a large sample of drivers. Then personal driver's behavior evolution can be analyzed over the time. To be able to identify time-varying non-linear hybrid model parameters, an iterative metaheuristic method based on particle optimization and moving average filtering has been created. This method enables to identify parameters of any model type while filtering the parameter time-variation based on the possible parameter dynamics. This methods also enables to interpolate parameters values while model output values are occluded. Demonstration of the identification algorithm efficiency with Gipps car-following driver model is done based on theoretical examples, and time-evolution of parameter are identified from real-world measured data.

This paper presents cooperative adaptive cruising control of multiple cars in automated/un-automated mixed traffic. In order to take account of un-automated cars, the vehicle maneuver is expressed as a PrARX model that is a continuous approximation of hybrid dynamical system. The PrARX model describes the driver's logical decision making as well as continuous maneuver in a uniform manner. The acceleration inputs of automated vehicles are computed in model predictive control framework where the state equation includes a platoon of automated and un-automated cars coupled with PrARX driver models. For computing assisting outputs in real time, a fast computation method for nonlinear model predictive control based on the continuation technique is employed. Simulation studies of the proposed CACC system indicates that explicit prediction of un-automated cars improves the overall stability of the platoon.

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.

This paper presents an autonomous lane tracking that reflects different driving characteristics using model predictive control (MPC). We consider that human driver minimizes a cost function depending on his/her skill, experience, and preference on driving. The cost function of MPC can be used to model personal driving characteristics. To identify the parameters of cost function, at first, we analyze the difference of driving characteristics between skilled and un-skilled drivers through experiments of driving behavior on a driving simulator (DS). Next, we introduce "meta-performance indices" that can evaluate human driving data of experiment and results of autonomous driving. These parameters are expected to express the driving characteristics as a group, i.e., not tuned to personal driver. Finally, the validity of the proposed system is verified.

We investigate a possible method for detecting a driver's negative adaptation to an automated driving system by analyzing consistency of driver decision making and driver gaze behavior during automated driving. We focus on an automated driving system equivalent to Level 2 automation per the NHTSA's definition. At this level of automation, drivers must be ready to take control of the vehicle in critical situations by monitoring the driving environment and vehicle behavior. Since drivers are not required to operate the pedals or steering wheel during automated driving, a driver's negative adaptation to an automated system needs to be detected from behavior other than vehicle operation. In this study, we focus on driver gaze behavior. We conduct a simulator study to compare the gaze behavior of fifteen drivers during conventional and automated driving. We also analyze the consistency of driver decision making when changing lanes during conventional and automated driving. Experimental results show that drivers who pay less attention to the road ahead during automated driving tend to be less sensitive to risk factors in the surrounding environment and also tend to make inconsistent lane change decisions during automated driving.

This paper presents a trajectory planning method for automated parking. The proposed method constructs a state roadmap in which each node contains not only position but also orientation information of the vehicle. The roadmap is constructed by dividing the orientation space in multiple resolutions considering the non-holonomic constraints of the vehicle and the collision-avoidance constraints between the vehicle and the boundary of the parking environment. Using the state roadmap, a complex parking trajectory composed of both forward and reverse motions can be computed with small online computation cost. The proposed method is evaluated in both numerical simulations and an experiment using an electric vehicle.

This work presents a study of the frictional effect on the stability of a three-rigid link object manipulated by two cooperative robot arms in a plane. It is supposed that the three-rigid link object interacts with the two robot arms at multi contact points to perform the nonprehensile manipulation of the object. The object is figured out in a way such that one of the arms is in contact with two links of the object, while the other arm is free to slide along the third link. The effect of changing frictional forces at the contact points as well as their directions on the stability of the manipulated object is to be explored by defining the system constraints. A definition of what is called the “Stability Margin” is obtained. For the same orientation of the object links, the Stability Margin is a region of equilibrium contact points between the sliding arm and the corresponding object link where the robot arm could be placed in without dropping the object. Later, an experimental system consisting of a three-rigid link object and a two planar robot arms manipulation system is equipped to verify the concept.

This paper presents a trajectory-planning method for multibody systems. Trajectory planning of a multibody system is formulated as a constraint-solving problem on a set of variables expressing the motion of the multibody system over a unite-time interval. Constraints express the dynamics of rigid bodies, kinematic conditions of joints, various range limitations, as well as achievement of tasks, and they can be assigned different priority levels. The prioritized constraint-solving problem is then treated under the framework of lexicographical goal programming, where the local optimality of the problem is characterized in terms of Pareto efficiency condition. Based on this observation, an algorithm that iteratively updates the variables toward a locally optimal solution is derived. The proposed method is evaluated in simulation examples.

This paper presents a novel personalized driver assistance system(PDAS) based on the model predictive control(MPC) together with a continuous/discrete hybrid dynamical system model of the driving behavior. First of all, the driving behavior is identified as the piecewise ARX model. Then, it is explicitly embedded in the optimization problem for finding the optimal assisting output. Since the driving behavior includes some binary variables, the optimization problem is formulated as the mixed integer programming. Some adaptation mechanism to accommodate to the change of the situation is particularly discussed. Finally, the proposed scheme is tested by using the real vehicle wherein the real-time assisting control based on MPC is implemented.

This research develops a metric for the evaluation of an automobile driver's distraction based on a mathematical driving behavior model. Driving data was collected in a driving simulator. The primary task was to maintain a constant following distance behind a lead vehicle. The secondary task, which brings about the distraction, is to operate the in-car touch panel. A PrARX model is used to describe the vehicle-following behavior. In the PrARX model, the weighting parameter represents the driver's logical decision making and the auto-regressive exogenous models characterize the driver's continuous-time motion control behavior. By calculating the entropy of the PrARX model, the driver's distraction, which is considered a degradation of decision-making ability, is assessed in a quantitative manner.

This paper proposes a probability-weighted autoregressive exogenous (PrARX) model wherein the multiple ARX models are composed of the probabilistic weighting functions. This model can represent both the motion-control and decision-making aspects of the driving behavior. As the probabilistic weighting function, a “softmax” function is introduced. Then, the parameter estimation problem for the proposed model is formulated as a single optimization problem. The “soft” partition defined by the PrARX model can represent the decision-making characteristics of the driver with vagueness. This vagueness can be quantified by introducing the “decision entropy.” In addition, it can be easily extended to the online estimation scheme due to its small computational cost. Finally, the proposed model is applied to the modeling of the vehicle-following task, and the usefulness of the model is verified and discussed.

This research develops a new graph-map method for autonomous car-like mobile robots based on variable-resolution division of space. Unlike conventional roadmaps, which include position information only, the proposed graph-map also includes orientation information of the car-like robot. In this manner, the robot is able to plan a path in detail. The orientation information of each node is not a fixed value but a range. The range is constructed by dividing the orientation space using variable-resolution, which is obtained from the surrounding situation of links that connect to the node. Finally, the proposed method is evaluated through simulations.

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 paper describes the development of a self-coaching system to improve driving behavior by allowing drivers to review a record of their own driving activity. By employing stochastic driver-behavior modeling, the proposed system is able to detect a wide range of potentially hazardous situations, which conventional event data recorders are not able to capture, including those involving latent risks, of which drivers themselves are unaware. By utilizing these automatically detected hazardous situations, our web-based system offers a user-friendly interface for drivers to navigate and review each hazardous situation in detail (e.g., driving scenes are categorized into different types of hazardous situations and are displayed with corresponding multimodal driving signals). Furthermore, the system provides feedback on each risky driving behavior and suggests how users can safely respond to such situations. The proposed system establishes a cooperative relationship between the driver, the vehicle, and the driving environment, leading to the development of the next generation of safety systems and paving the way for an alternative form of driving education that could further reduce the number of fatal accidents. The system's potential benefits are demonstrated through preliminary extensive evaluation of an on-road experiment, showing that safe-driving behavior can be significantly improved when drivers use the proposed 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.

Recently, the demand for a man-machine cooperative system, where the machine assists the human operator, is rapidly growing in the industrial fields. To meet this demand, the human model is required to design the suitable assist controller in the man-machine cooperative system. This paper presents a new human behavior model based on a piece-wise affine model which is a class of hybrid dynamical system, and apply it to a sliding task. Since the human behavior is considered to consist of several primitive motions expressed by continuous dynamics and a decision-making expressed by the discrete switch, it seems to be natural to introduce the hybrid system modeling. Particularly, the decision strategy for the number of discrete modes is addressed by using a hierarchical clustering technique, and the measured data are classified into several modes. Then, each primitive motion in each mode is identified based on the affine model. Finally, the switching conditions among modes are identified by applying support vector machine to the classified data. The obtained piece-wise affine model can quantitatively represent both primitive motions and decision-making in the human behavior

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

This paper presents a development of the modeling of the human driving behavior based on the expression as hybrid dynamical system (HDS) focusing on the driver's collision avoidance behavior. The driving data are collected by using the three-dimensional driving simulator based on CAVE, which provides stereoscopic immersive vision. In our modeling, the relationship between the measured information such as the sensory information of range between cars, range rate and lateral displacement between cars and the output of driver of the steering amount are expressed by the piecewise linear (PWL) model, which is a class of HDS. Then, we solve the identification problem for the PWL model by using the combination of data clustering and support vector machine. By introducing the PWL model, it becomes possible to find not only coefficients in each submodel but also parameters in the logical (switching) conditions from the measured driving data. From the obtained results, it is found that the driver appropriately switches the 'control law' according to the sensory information. This enables us to capture not only the physical meaning of the driving skill, but also the decision-making aspect (switching conditions) in the driver's collision avoidance behavior.

This paper presents a modeling strategy of human driving behavior based on the controller switching model focusing on the driver's collision avoidance maneuver. The driving data are collected by using the three-dimensional (3-D) driving simulator based on the CAVE Automatic Virtual Environment (CAVE), which provides stereoscopic immersive virtual environment. In our modeling, the control scenario of the human driver, that is, the mapping from the driver's sensory information to the operation of the driver such as acceleration, braking, and steering, is expressed by Piecewise Polynomial (PWP) model. Since the PWP model includes both continuous behaviors given by polynomials and discrete logical conditions, it can be regarded as a class of Hybrid Dynamical System (HDS). The identification problem for the PWP model is formulated as the Mixed Integer Linear Programming (MILP) by transforming the switching conditions into binary variables. From the obtained results, it is found that the driver appropriately switches the "control law" according to the sensory information. In addition, the driving characteristics of the beginner driver and the expert driver are compared and discussed. These results enable us to capture not only the physical meaning of the driving skill but the decision-making aspect (switching conditions) in the driver's collision avoidance maneuver as well.

This paper presents a new hierarchical scheduling method for a large-scale production system based on a hierarchical Petri net model, which consists of FOHPN and TPN. The automobile production system equipped with 2 stand-by lines is focused as one of a typical large-scale system and these stand-by lines are controlled by binary signal. In a high level, the FOHPN model is used to represent continuous flow in production of an entire system, and MLD description is used to control the net dynamics of FOHPN. Also in a low level, TPN is used to represent production environment of each sub-line in a decentralized manner, and MCT algorithm is applied to find a feasible semi-optimal process sequences for each sub-line.

/sup H/ybrid dynamical systems (HDS), which contain both discrete logical symbol and continuous signal, are attracting great attention in the field of system control. In this paper, a new symbol based control strategy for a line following control of a two wheeled vehicle is proposed. The vehicle is supposed to have a low-resolution sensor and actuator. The control requirement, however, is specified so as to keep the vehicle as close as possible to the center of the line. The controllability and observability issues are investigated, and a concrete control policy based on the continuous state estimation is proposed. Some experimental results are shown to demonstrate the usefulness of our idea.

In this research, we discuss how to automatically generate the motion sequence of actuators for error recovery, including parallel path divergence in the programmable logic control (PLC). Since the sequential path in PLC can be expressed by a state flow, the goal of our research is to find an optimal shortest path from the initial state to the final state. In order to solve this problem, we propose a new search strategy, which consists of two search stages. In the first stage, real-time A* (RTA*) algorithm is used to find a suboptimal sequential path from the initial state to the final state. Based on the suboptimal solutions found by RTA*, the second stage tries to pick up some underlying parallel path divergences among solutions obtained in the first stage. From some simulation results, we have verified that our proposed method could search a suboptimal motion sequence including parallel path divergences with less computational amount.

Presents a method for control of autonomous mobile robots to acquire fine actions based on real-time search. In the proposed method, the information criterion for the environment is defined based on the Kullback-Leibler divergence, which measures the quality of the environmental information used for the action search. The robot searches suitable actions based on the environmental information which is improved step by step in the sense of this criterion. According to this, we can harmonize the trade-off between the calculation amount and information quality, and make the search process faster. The proposed method is applied to the moving obstacles avoidance problem, and its usefulness is shown through some simulation results.

The requirement of handling deformable objects such as leather, paper and rubber is growing. Since it is very difficult to make a physical model of them, the design of a controller to manipulate them becomes one of the significant problems in the field of robotics. If we look at the operation of a human worker, the deformable objects seem to be handled naturally and smoothly. The paper presents a method to design a controller for assembly tasks which involves the manipulation of deformable objects. The proposed method extracts the dynamics that human workers used in executing the demonstrated task, and embeds that in an event driven hybrid controller. In our control system, an event observer estimates the change of task state based on force and visual information like human workers, and switches the dynamics appropriately according to the task state. The proposed method is applied to a hose insertion task by implementing it in an industrial robot controller.

Presents a scheduling method for a manufacturing system based on a timed Petri net model and a reactive fast search algorithm. The following two typical problems are addressed in the paper. (1) Minimize the maximum completion time. (2) Minimize the total deadline over-time. As for problem (1), a search algorithm which combines RTA* and a rule-based supervisor is proposed. Since both RTA* and the rule-based supervisor can be executed in a reactive manner, machines and AGVs allocations can be scheduled reactively, and simultaneously. As for problem (2), the original Petri net model is converted to its reverse model and the algorithm developed in problem (1) is applied with regard to the due time as a starting time in the reverse model.

We propose a method based on a real-time search for the proper action of an autonomous mobile robot using the framework of the Anytime Algorithm. This is available for a real-time search in a dynamic environment. In order to apply the Anytime Algorithm to the multi-objective real-time search in the dynamic situation around the autonomous mobile robot, we adopt prediction, a switching evaluation function technique, the action watch dog and an evolution strategy. These enable the robots to realize the real time search. The paper explains the proposed method and shows the feasibility through experimental results using real robots.

Impedance control is widely used in the field of industrial world. In a certain task, it is important to decide the impedance parameters in order to realize the desired task. However, it is very difficult to calculate analytically, and the method to extract impedance parameters from human demonstration often exist unevenness in time and space in the human data. Modelling with hidden Markov model (HMM) is known as one of the promising technique to construct an efficient model for time-variant data including unevenness. HMM is capable of characterizing a doubly stochastic process with an underlying immeasurable stochastic process which can be measured through another set of stochastic processes. In this paper, we propose a method to model the series of impedance parameters identified from human teaching data with HMM as human skill model of the peg-in-hole task. In addition, realization method of the task based on the obtained model is shown.

We propose a method based on a real-time search for the optimal action of an autonomous mobile robot using an evolution strategy. By searching the optimal action for the facing situation in real time the robots need not suffer from the problem of categorization of the situations and problem of pre-definition of rules. The basic idea of the proposed real time search technique is to harmonize computational amount with a property of solution. In order to realize this requirement we adopt a two-stage evaluation technique. The proposed method also adopts the prediction and action watch dog systems. These function enable the robots to realize the real time search. The paper explains the proposed method and shows the feasibility through the experimental results using the real robot.

Since impedance control can achieve the desired dynamics between tool and environment, it is widely used for complex tasks. But deciding the impedance parameters for the task is very difficult because they cannot be calculated analytically. If we can extract impedance parameters from human demonstration, it is appropriate to use them. However, if there exist disturbances and/or noise at the playback stage, which was not taken into account at the skill acquisition stage, the executed task by the robot is far different from the human demonstration. In order to realize the robust skill, we introduce a hybrid architecture. This architecture enables robots to use the most suitable dynamics (impedance parameters) for each task situation. Moreover, because of event driven architecture, we can expect the improvement of robustness of the skill in the time domain. We apply our proposed method to the peg-in-hole task, and show some experimental results.

When we apply an impedance control to execute any tasks, it is very important how to decide the impedance parameters to realize the desired task. If we can extract impedance parameters from human teaching data as characteristics of the human skill, it is appropriate to use them for control because of the similarity between an impedance control and a human fingertips control. However, there often exists unevennesses in time and space in human data. Modeling with hidden Markov model (HMM) is one of the promising technique to construct an efficient model for time-variant data including unevennesses. HMM is capable of characterizing a doubly stochastic process with an underlying immeasurable stochastic process which can be measured through another set of stochastic processes. Therefore, the probabilistic modeling of certain time series data which includes unevennesses caused by the human is possible. In this paper, we propose a method to model the series of impedance parameters identified from human teaching data with HMM in order to extract an essential discrete model which expresses the human skill. In addition, some applications of the obtained model to robot control and skill evaluation are shown.

The principal aim of this study is to show how an autonomous mobile robot can acquire the optimal action to avoid moving obstacles through the interaction with the real world. In this paper, we propose a new architecture using the hierarchical fuzzy rules, fuzzy evaluation system and learning automata. By using our proposed method the robot acquires the fine behavior on how to move to the goal avoiding moving obstacles using the steering and velocity control inputs, simultaneously. Also we show the experimental results to confirm the feasibility of our method.

This paper studies the learning control problem of disassembly task sequence. The proposed learning control system has two sub systems. One of them is a supervisory control system which can realize the implicit specifications such as equipment-related or task-independent specifications. The other one is a learning automata system which can realize the explicit specifications such as task-dependent specifications. This approach allows the flexible design to accommodate frequently changing manufacturing requirements. Some simulation results to verify the effectiveness of the proposed system are shown.

Recently, a disturbance observer based controller has been widely used in the industrial world because of its simplicity. However, this method depends on the intuitive approach in determining the disturbance estimation filter, and therefore, more systematic approach is desired. In this paper, we first combine the generalized compensator and the disturbance observer based controller by clarifying the internal structure of the generalized 2-degree-of-freedom (DOF) compensator. Secondly, based on the clarified structure, we derive a robust stability condition, and propose the design algorithm of a free parameter taking into account the condition. The proposed design algorithm is easy to implement and, as a result, we obtain a lower order free parameter than that of the conventional design algorithm. Thirdly, we show by adopting an appropriate coprime factorization that the clarified structure can also be regarded as an extended version of the conventional PD compensator. Finally, we apply the proposed algorithm to a positioning control of a 3-DOF direct drive robot, and show some experimental results to verify the effectiveness of the proposed algorithm.

In the field of robotics, many researchers have studied the representation and analysis of an automatic assembly system. In previous research, however, no control-theoretic ideas for the assembly system have been found. In this paper, the authors study a closed-loop desired marking control problem for the automatic assembly system using the supervisory control technique. First, the authors represent the assembly system with a Petri net. Since the assembly Petri net has a "tree structure", the authors show that the assembly Petri net has some formal linguistic properties. Based on the properties, the authors clarify that the solvability of the desired marking control problem depends only on the controllability of a control object, and that one can construct the supervisor for the assembly Petri net with a finite automata. Finally, the authors discuss the supervisor reduction technique and give two examples.>

An automatic generation of assembly or disassembly sequence is a significant problem for an automatic assembly using robotic manipulators. This problem can be separated into following three stages. 1. Extraction of all feasible assembly/disassembly tasks. 2. Generation of all assembly/disassembly sequence. 3. Decision of optimal sequence. As for the stages 2 and 3, one of the efficient strategy based in the AND/OR graph has been proposed. However, as for 1, previous researches are not efficient. In this paper we propose a new technique for the examination of the feasibility of each assembly/disassembly task by using topological operations. The presented method is expected to execute with smaller computational amount compared with the previous methods, because a separating path of a mechanical part without collision with other parts is obtained without search of all space. A mapping, which maps shape of a subassembly to a feasible path of the other subassembly around the first subassembly, keeping contact, is introduced. An area of feasible translational motion of the second subassembly without interaction with the first is calculated. Criteria of feasibility of disassembly task are presented. Finally, we show a simple example to verify the proposed algorithm.>

An assembly planning method using Petri nets is presented. First, the assembly network is modeled using the conventional AND/OR net. Second, the state shift equation of the Petri net, which is its mathematical model, is analyzed. This analysis shows that the basis solution the of a state shift matrix is identical to the set of a solution which corresponds to an actual assembly sequence. This means that in considering an optimization problem which minimizes a summation of a weight value of all tasks included in the assembly sequence, a linear programming technique can be used. Some numerical examples are given to certify the validity of the analysis.

The transitional motion when a robotic manipulator and a workpiece first make contact is fundamental and occurs frequently. The authors propose a learning controller to improve the contact motion. The learning controller has two features. It observes input and output signals at different periods to improve the transient response of the system; and it can modify an initial state of the system, which includes an approaching velocity of the manipulator. By modifying the approach velocity, a fine contact motion can be achieved, because the magnitude of the impact force depends on the approach velocity. Simulation results verify the effectiveness of the proposed method.>

Most conventional trajectory planners are based on the trapezoidal pattern of the end-effector velocity on the desired path in a task space. This method can be implemented in real time. However, the motion planned by this method usually leads to a very slow motion, because it is difficult to take into consideration a constraint on the magnitude of joint velocities or joint torques. The authors propose a new trajectory planner for three DOF manipulators based on the kinetic energy of manipulators. The supplied electric power for the manipulators mainly depends on the differentiation of the kinetic energy. Therefore, the proposed method can take into consideration the constraints on the magnitude of the supplied electric power and joint velocities, i.e. one can realize a 'high speed motion' under the constraints on the supplied electric power and joint velocities. Also, the method proposed does not require much calculation and can be implemented in real time.>