Recently, In the manufacturing industry, in order to respond to changes of business environment quickly, it is required to realize variable-variety variable-volume production. Along with this, flexible production methods which can produce a wide-variety products on any line are focused on. In the flexible production methods, it is important to make an appropriate production schedule to maintain productivity because bottleneck resources fluctuate. In addition, it is also required to make production schedule quickly to respond daily demand fluctuations. In this research we develop simulation-based scheduling methods that enable high-speed and high-productivity scheduling by providing planning logic that corresponds to the characteristics of the manufacturing line. In this talk, we will discuss planning logic and numerical experiment results especially for manufacturing lines with parallel line configuration.

Planning alternative multiple carry-in/out paths is essential to the engineering work of replacement tasks in power plant maintenance, adapting to the uncertain 3D environment of the plant building. For this subject, dissimilarity of the planned paths and fast response time for the query are required for a path finding algorithm. Existing path finding algorithms can find exact multiple paths but the differences of found paths are very little. Besides, the computation times are not feasible for a large volume of 3D space such as power plant. In this paper, a novel multiple paths finding algorithm is proposed for dissimilar paths planning with fast response time for the query, realizing interactive operations. Furthermore, performance indices for the path finding is designed with consideration of secure carry-in/out operations in the proposed algorithm. The algorithm outputs crane suspension trajectory avoiding collision with the plant building. The system has been applied to replacement tasks of an existing boiler building of a power plant. The building is expressed by over 5 GB stereo-lithography binary data.

In this paper, we propose a realization method for a rearrangement task involving multiple movable objects and mobile robots. All of the objects are transported from their initial positions to goal positions. It is important for mobile robots to cope with map errors in order to execute tasks in a real environment. However, a rearrangement task is a very complicated process involving constraints related to transportation order and scarcity of spatial resources. Therefore, it is difficult to develop an adequate method to deal with map errors and still maintain task performance. We primarily address two questions: what kinds of tactics are needed and when these tactics should be applied. Our method involves adopting a problem-partitioning structure that divides a complicated rearrangement problem into simple subproblems. Furthermore, we design a real mobile robot group. Using the realization method and real robot group, we conduct experiments involving a rearrangement task in an actual environment. The results show that our proposed method can cope with map errors quickly, while maintaining task realization performance.

A rearrangement task of multiple objects is discussed here. In this paper, robots that carry more than one object simultaneously are referred to as multi-task functional, whereas a standard single-task robot carries one object at a time. With multi-task functional robots, the total length of transfer paths is shortened and the processing time is reduced. We propose a planning algorithm that consists of simulated annealing and a scheduling method using prioritization rules for a group of multi-task functional robots. We also propose a planning methodology on synchronization timing between the robots. Experiments involving two robots were conducted in real and simulated environments to show the effectiveness of the proposed algorithms.

In this paper, we address a multiple robot rearrangement problem. For different applications, problem-solving methods should be able to cope with various working environments. We focus on small working environments in particular with a concentrated arrangement of objects and narrow corridors. In this type of environment, the rearrangement problem can be very complicated because of high computational cost for priority settings to prevent robots from colliding and constraints related to the order of transportation. We propose a practical algorithm that divides a complicated rearrangement problem into simple subproblems. In our method, the rearrangement problem can be reduced to a project scheduling problem using a territorial approach. The application of a territorial approach can relax the complexity of priority settings, but yields new kinds of constraints at the same time. We propose an extended project scheduling problem solver to address these constraints. The solver is constructed on the basis of meta-heuristic strategy and generates the order of transportation that observes constraints. The proposed method is tested in a simulated environment with up to four mobile robots and 12 movable objects. Simulation results show the effectiveness of our method with respect to the applicability and a reasonable calculation time.

In this paper, we address a rearrangement task planning problem. The method proposed here is suitable for various environments with narrow corridors, and an unbalanced arrangement of robots and objects. As the solution space is too large for the search method to be applied directly, we will limit the search space. The proposed method develops an initial plan by using a simple rule and improves the task decomposition of the initial plan. That is, it effectively determines which object should be transported where. We employ the concept of the critical path method to select the task that should be modified and the concept of a territorial approach to set up new task decomposition. New task decomposition is set up so that a robot can operate without considering the movement of other robots. These concepts render our method fast. The proposed method is tested in a simulated environment with up to four robots and 12 movable objects. The simulation results show that in a feasible calculation time, our method can reduce the task time by 10% as compared to the previous method. In particular, the proposed method is much more applicable to the working environments than was the previous method. (C) Koninklijke Brill NV, Leiden and The Robotics Society of Japan, 2011

In this paper, we address a rearrangement task involving multiple robots. We focus on small working environments composed of narrow corridors. In such an environment, interference between robots resulting from a competition for physical space can affect the overall performance in a rearrangement task. Because of the enormous amount of calculation time required to develop a motion plan for each robot considering the effect of interference, it is difficult to apply the former approach directly. In this paper, we propose a practical method using a territorial approach where the working area of each robot is defined so that there is no overlap. Because robots can develop a motion plan with less consideration of interference between other robots, the entire calculation time can be reduced. In this method, we obtain the order of sub-task executions as a solution to a project scheduling problem. However, some constraints that have not been dealt with in research involving scheduling may arise. We analyzed these new constraints and proposed a metaheuristic strategy to determine the order of sub-tasks. The proposed method is tested in a simulated environment with up to four mobile robots, 12 movable objects, and a complicated arrangement of objects.

In this paper, we address a multiple robot rearrangement problem. We focus on a small working environment composed of narrow corridors. In such an environment, adequate priority settings are required to prevent robots from having collisions. As a result, enormous calculation time is required. We propose a practical method utilizing partitions that divide the entire complicated problem into multiple simple sub-problems. Among these sub-problems, new constraints related to task execution order occur. Therefore, we extend the existing project-scheduling problem solver to cope with these new constraints. The proposed method is tested in a simulated environment with up to 4 mobile robots and 12 movable objects. The simulation results show that our method can be used to obtain a solution in a feasible calculation time.

A robot having a multi-task function can carry more than two objects simultaneously; however, a robot without that function can carry only one at any given time. A multi-task function robot has the potential to shorten the transfer path, which reduces the process time; however, a single-function robot does not have that capability. In this paper, a planning algorithm that consists of simulated annealing and scheduling with prioritization rules for a robot group having a multi-task function is proposed. Experiments are conducted in a real environment to validate the proposed algorithms.

We address multiple-robot rearrangement problems in this paper. The rearrangement of multiple objects is a fundamental problem involved in numerous applications. In this case, it must be considered that a rearrangement task has constraints regarding the order of the start, grasping and finish time of transportation. Attention to these constraints makes it possible to rearrange rapidly; however, the calculation of the constraints is costly in terms of computation. In this paper, we propose a rearrangement method that calculates constraints efficiently. We analyze constraints and classify them into two groups: those that require less computational cost and those that require more. Robots do not calculate all groups at the same time — the time required for each type of calculation varies. The proposed method is tested in a simulated environment 96 times in six kinds of working environments with up to four mobile robots. Compared to the method that calculates all constraints at the same time, the robots' inactive time is significantly reduced and the total time for task completion is also eventually reduced. The proposed method is incomplete, but can be used to perform most rearrangement problems in a short time.

A position management system for forklifts involved in a warehouse storage task is proposed. With a simple, low-cost laser beam sensor and reflective stickers, the position of the forklift can be established. Furthermore, by combining probability estimation algorithms, the possible location of forklifts can be managed, and the accuracy of positioning can be improved through the exchange of information on a sensor network. Finally, the layout, which has a better system performance, is also discussed and proposed. With this system, forklift operations are easier, and the cost of a warehouse navigation system decreases.

In this paper, we propose a search and surveillance with mobile robots to collect information while minimizing repeated coverage to maximize efficiency. The problem of search and surveillance is defined as one having a mobile robot or covering a working area with sensor footprints. The problem is applicable to tasks such as floor cleaning, map building, surveillance, security patrols, and search and rescue operations. We use a reaction-diffusion equation on a graph (RDEG), we make and remake plans online base on incoming environmental information. The strategy is applicable to patrolling tasks after an environment has been completely explorated. Tasks are allocated to multiple mobile robots, among which a temporary leader, i.e., the robot detecting a drastic change in the environment, plans a strategy for other mobile robots on the team. Sensing and positioning data for each robot is broadcast and shared among robots. Simulation in different scenarios using one to three robots demonstrated the feasibility of increasing the number of robots on a team.

We address a rearrangement task by multiple robot in this paper. A rearrangement task has constraints regarding the order of the start, grasping and finish time. Calculating these constraints has a high computational cost. We propose a rearrangement method that calculates constraints efficiently. In our approach, not all constraints are calculated, but some of them will be calculated step by step. The proposed method is tested in a simulated environment with up to 4 mobile robots. The methods are compared, and the results indicate that the proposed method is superior.

A position management system for forklifts involved in a warehouse storage task is proposed. With a simple, low-cost laser beam sensor and reflective stickers, the position of the forklift can be established. Furthermore, by combining probability estimation algorithms, the possible location of forklifts can be managed and the accuracy of positioning can be improved through the exchange of information on a sensor network. With this system, forklift operations are easier, and the cost of a warehouse navigation system decreases.