In this paper, a single mobile robot is used to patrol an indoor environment composed of rooms. The robot is required to monitor the rooms and detect visitors as many as possible. A key idea in the patrolling mission is to allow the robot to utilize information about visitor trends. Thus far, we have focused on a visiting probability as the trend. However, the visitor trends usually change over time. Therefore, the robot is further required to detect the changes and identify the correct visitor trends in each room. For this challenge, we present a novel change-point detection method. In this method, the Jensen-Shannon divergence is used as a dissimilarity measure between two distributions of the visiting probability. The change-point detection method is activated in the Bayesian learning for estimating the changed visiting probability. Consequently, the robot is allowed to adaptively patrol the changing environment. Through the simulation experiments, we discuss the effectiveness of the adaptive patrolling for the changes in the visitor trends.

This paper focuses on a surveillance mission performed by an autonomous mobile robot in an environment. In this mission, the robot is required to detect as many intruders as possible by utilizing the mobility and monitoring capability. However, the robot does not know where and how many intruders are in the environment. Without the information, it is impossible for the robot to estimate an intrusion trend and detect the unknown intruders. For this challenge, we propose a novel surveillance method in order for the robot to estimate the intrusion trend. For this purpose, Bayes' rule is basically used. This probabilistic approach allows the robot to express the intruders through existence probability and appearance frequency. As the contribution of this paper, the robot is enabled to mainly monitor the places with the intrusion trend, in addition to other minor places. Through simulation experiments, the effectiveness of the proposed method for the unknown intruders is shown. Furthermore, an adaptability of the robot based on the probabilistic approach to various kinds of intrusion trends is discussed.

Patrolling is an important task for autonomous mobile robots. In this paper, we focus on a single patrolling robot in indoor environments composed of rooms. For the robot, it is required to visit the rooms and detect unknown intruders as many as possible. However, the robot does not have correct information about intruders in the initiation step of patrolling. Therefore, the robot first acquires the information through the patrolling trials. As for the information, the robot focuses on intrusion probabilities into the rooms. Subsequently, target rooms are determined on the basis of the intrusion probabilities. This so-called explore/exploit tradeoff in reinforcement learning is a challenge for the intelligent patrolling. In this paper, we tackle the patrolling problem as a part of the learning issues. Thus a multi-armed bandit problem is introduced. For the challenge, we propose stochastic patrolling strategies based on Bayesian learning. Simulation experiments show that the robot is enabled to estimate the correct intrusion probabilities of the rooms. Through the discussion of the patrolling results, finally, we present the most effective patrolling strategy.

Ensuring fault tolerance of robotic systems is a challenge for factory automation. This paper focuses on multi robot manipulation and maintenance for fault-tolerant systems. For this purpose, a manipulation strategy of multiple mobile robots, that enables a system to continue operating even if a working robot undergoes preventive maintenance or fails and undergoes corrective maintenance, is implemented. In addition, a robot failure and a maintenance policy for preventive and corrective maintenance activities are mathematically modeled on the basis of reliability engineering. Thus, working robots are allowed to undergo preventive maintenance at an optimal interval and corrective maintenance each time they fail. Finally, through simulation experiments, the effectiveness of an integrated multi-robot manipulation and maintenance in industrial applications is shown.

This paper describes a novel coordination methodology for autonomous mobile robots in congested systems with bottlenecks. This methodology consists of two approaches. 1) A previous robot behavior control technique that utilizes external interaction force among robots is improved. This enables robots to reduce velocity not only for the jam preceding them, but also for the decelerating robot immediately in front. 2) An environmental rule in connection with the interaction force is designed and provided on congested lanes where robots move slowly. Thus, amplified interaction force affects the robots, and they move more slowly in the congested lanes. The improved interaction force and environmental rule are implemented in simulation experiments and compared to the previous robot behavior control technique and an adaptive cruise control (ACC) that has been proposed for autonomous vehicles. The interaction force and environmental rule can be implemented in the ACC; then, the resulting ACC improvement is discussed. Finally, the effectiveness of the usage of the improved interaction force and environmental rule for multi-robot coordination in congested systems with bottlenecks is shown.

This paper focuses on the effectiveness of maintenance in fault-tolerant multi-robot systems. Such a system is enabled to work as long as one robot works; namely, completely parallel. For this system, it is required to ensure fault tolerance and maintain high performance taking robot failures and maintenance, such as prevention and correction of robots, into account. Therefore, we propose an optimal maintenance strategy on the basis of reliability engineering. This strategy enables robots to undergo preventive maintenance at optimal intervals and corrective maintenance each time they fail. Through simulation experiments, the effectiveness of the optimal maintenance strategy is investigated. In addition, the influence of robots undergoing maintenance on system performance is mathematically and experimentally discussed on the basis of the number of robots and maintenance strategies.

This paper describes a methodology regarding an autonomous cruise control (ACC) of circulating multi-robot which is directly effective for solving the congestion regardless of the presence of a bottleneck. For this purpose, we focus on an external interaction force between the robots. This force is generated with the use of a virtual damper. In this methodology, two control scenarios are presented: a damping force acts on a moving robot as the external interaction force, (I) only when its preceding robot(s) is/are stopping or being congested and (II) when a robot exists in front of the moving robot, in order not to become involved in the congestion. This paper deals with a circuit and a robotic manufacturing-transportation system for industrial automation. Through simulation experiments, it is shown that the proposed ACC successfully solves the congestion, and finally, improves the throughput. In addition, the superiority of the two control scenarios, ACCs (I) and (II), is discussed.

In this paper, we focus on a batch manufacturing system with multiple industrial robots. In this system, material-handling robots (MHRs) and material-processing robots (MPRs) are operating. Since various operations are conducted at certain places, one of them might include a localized bottleneck. A localized bottleneck is a constraint that dominates the maximum amount of production in a system, that is, the productivity. In addition, the bottleneck induces congestion; as a result, the productivity declines. For these issues, there are two primary challenges: bottleneck prevention and restraint. To solve them, suitable operational techniques with respect to the MHRs and MPRs are needed in order for them to operate appropriately while relating to each other. In this paper, a constructive approach toward multi-robot coordination problems is taken. In this approach, we propose applicable operational techniques for the robots. Through simulation experiments, we examine the effectiveness of the proposed techniques and their combinations and, finally, show an integrated operational technique. Each of the operational techniques solves a localized bottleneck and the congestion, and the integrated technique successfully improves the productivity and results in the most efficient system.

This paper focuses on an automated batch manufacturing system with material-handling robots (MHRs) and material-processing robots (MPRs). In this robotic manufacturing system, materials transported by the MHRs are processed by the MPRs. These operations cause a bottleneck in the system. Furthermore, the bottleneck induces congestion of the MHRs. In the system, the effect of an operational delay due to bottlenecks affects the entire operation. Accordingly, there is an event in which the congestion extends and the system throughput not only fails to increase but also may become worse, even if more robots are used to improve the productivity. For this challenge, we propose a behavior control method for the MHRs to eliminate or ease the congestion that arises from a bottleneck. Each MHR controls its own behavior adequately by using the external force of a virtual damper in order not to become involved in the congestion. Finally, through a simulation experiment, we show that the proposed control method improves the system throughput and its effectiveness for a more efficient system operation.

When robot maintenance impedes the operation of other robots in a seaport container terminal, the operational efficiency is reduced. Therefore, robot reliability is necessary. In this paper, we propose a reactive robot control system with hybrid operational models for flexible and efficient container handling in consideration of robot reliability. For this purpose, we focus on three operational models in the states of normal, preventive, and corrective maintenance in order to utilize the mutual substitutability of the operation among robots. By applying hybrid operational models, each robot is able to respond to the dynamically changing states reactively. Finally, we discuss the effectiveness of the proposed system in a seaport container terminal.

Automation of transportation systems and promotion of the operations are becoming an international demand on seaport container terminals. For this issue, we propose an integrated design methodology. In other words, in addition to the design of the appropriate number of machines, efficient system layout, and system management models, we attempt to design machines' specifications, which are operating in an automated transportation system. The objective of this study is to maximize the system efficiency while minimizing the changes of the specifications. Therefore, we evaluate the specifications based on the system throughput of the constructed system; then, make clear the impacts of the specifications on the system. Finally, for imposed demands, we design highly efficient transportation systems. From the construction costs and design results, we show a need to take into consideration the machines' specifications.

For the design of an automated transportation system in an actual seaport container terminal, it is necessary to take into consideration the maintenance of operating robots (AGV: Automated Guided Vehicle and ATC: Automated Transfer Crane). For this purpose, we develop an operation model in which each robot enters a maintenance mode while operating on the basis of its reliability. We also aim to design the mean time between failure (MTBF) which is a kind of a measure of robot performance on the basis of the robots’ reliability as well as the number of robots. However, this is a combinatorial design problem. Therefore, we propose a design methodology in order to derive one combinatorial design solution for a given demand by considering a system management cost that includes a system construction cost and a penalty cost. The designed systems are evaluated on the basis of the system management costs. Finally, we present the validity of the proposed design methodology and designed systems.

In this paper, we analyze and compare the performance of the vertical and the horizontal automated-guided-vehicle transportation systems. We use results in queuing network theory and a transportation simulator to design a hybrid strategy for this study, and to set the appropriate number of agents in the systems. Next, these two transportation systems are evaluated based on cost-effectiveness criteria. For this purpose, the total construction costs of the systems for the various transportation demands are compared. Finally, we provide analytical results to evaluate and to obtain the most efficient system, based on the validity of each system, under different demand scenario. Note to Practitioners-A good design methodology is essential for the study of the optimal layout in an automated container terminal. Port designers need to select the most efficient automated-guided-vehicle (AGV) transportation system, and to set the appropriate number of agents operating in the system. This study presents a hybrid design methodology and a cost-effectiveness comparison of the vertical and the horizontal transportation systems. Our proposed design methodology is able to derive the combinatorial optimal design solutions rapidly, and at the same time pin point the bottleneck in the system. This proposed methodology can be easily applied to any transportation or logistics system, provided the system can be divided into components represented as nodes in a graph. Our results demonstrate that the horizontal AGV transportation system is more effective than the vertical AGV transportation system under most demand scenarios.

In this paper, two designs for optimal Automated Guided Vehicle (AGV) transportation systems are presented. One is vertical and the other, horizontal. For these systems, the hybrid design methodology proposed here is used. Therefore, we describe how to model and formulate these transportation systems and derive the design parameters, that is, the optimal design solutions. We next evaluate these two transportation systems based on the various transportation requirements, that is, the demands from a port authority. For this purpose, we compare the systems based on the total costs in constructing them. Finally, the evaluation and analytical results are provided, and the most convenient system is presented based on the validity of each system for the given demand.

The development of a highly efficient management methodology for an automated container terminal (ACT) poses a problem for port authorities. The focus here is on a transportation system for an automated guided vehicle (AGV) for an ACT. In this paper, we design the detailed management models, i.e., agent cooperation and container storage planning for the transportation system. Then, we optimally design systems that are constructed with the use of the designed management models. Comparisons of the systems are made to evaluate cost effectiveness based on the total construction cost and validity of the management models. Finally, a proposal is made for the most efficient management system.

In this paper, we provide the comparison indicator of the AGV transportation systems. For this purpose, we propose an optimal design methodology for the AGV transportation system by using the queuing network theory. In this methodology, the queuing network theory and a simulation-based optimization method are integrated to obtain the optimal design parameters (i.e., these are the design solutions of this design problem). In this study, two different types of the AGV transportation systems are designed. Then the performance of transportation is compared. Finally, the characteristic of each transportation system that depends on the design parameters is provided.