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The focus of this study is on the problem of manipulator system selection for a multiple-goal task by evaluating task completion time and cost with computational time constraints. An approach integrating system selection, structural configuration design, layout design, motion planning, and relative cost calculation is proposed to solve this problem within a reasonable computational time. In the proposed approach, multiple-objective particle swarm optimization (MOPSO) is utilized to search for the appropriate manipulator system with appropriate structural configuration from a set of candidate systems. Particle swarm optimization (PSO) and the nearest neighborhood algorithm are employed in layout design and motion planning due to their high convergence speed. Three methods involving a random search algorithm are compared to the proposed approach through a simulation. The simulation is done with a set of tasks and the result shows the effectiveness of the proposed approach. [ABSTRACT FROM PUBLISHER]

A rearrangement problem involving multiple mobile robots is addressed in this paper. In the problem, it is important to identify task decomposition, task allocation, and path planning applicable to distinct environments while rearrangement tasks are executed. We here define ‘task apportionment’ as an operation that sets up task decomposition and conducts task allocation based on that setup. We propose a method for task apportionment and path planning applicable to distinct environments. The method establishes the necessary intermediate configurations of objects as one way of task decomposition and determines task allocation and path planning as a semi-optimized solution by using simulated annealing. The proposed method is compared with a continuous transportation method and a territorial method through simulations and experiments. In the simulations, the proposed method is, on the average, 17 and 20% faster than the continuous transportation method and the territorial method, respectively. In the experiments, the proposed method is, on the average, 22 and 16% faster than the continuous transportation and the territorial method, respectively. These results show that the proposed method can realize an efficient rearrangement task by mobile robots in various working environments under feasible computation time, especially in environments with a mixture of wide and narrow areas and an uneven distribution of objects. [ABSTRACT FROM PUBLISHER]

This study presents a multiple-goal task realization in a system composed of a 6-d.o.f. robot arm and a one-axis rotating table. The problem is complex due to the existence of multiple goals and the kinematic redundancy of the system. We propose a design approach integrating the base placement, task sequencing and motion coordination methods. We show that this approach reduces the task completion time of the robot arm; the motion planning is realized through straight-line paths in the configuration space despite collision occurrences. Furthermore, we introduce a hybrid graph-search method combining the greedy nearest-neighbor method and the Dijkstra method to solve the motion coordination of the robot arm and the table. We show the effectiveness of the design approach and the search method through a time-constrained simulation-based optimization. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

An integrated design process for automated guided vehicle (AGV) systems is proposed in this paper. There are two problems inherent in the design of systems: transporter routings and flow-path networks. They should both be solved at the same time because one depends on the other. In the present research, this issue is solved through cooperative co-evolution. For the development of co-evolution, some partner selection strategies and some evolving cycles, which are proper for design process, are evaluated. Both the transporter routing problems and the flow-path network problems are represented as different species, and a genetic algorithm is applied to them. AGV systems designed with this method are evaluated through simulations with real data. These simulations show that the proposed method makes it possible to improve the effectiveness of AGV systems. [ABSTRACT FROM AUTHOR]

A rearrangement planning methodology of multiple movable objects by a mobile robot is proposed for a certain rearrangement problem, i.e., the LPεk (k > 2) problem. Several objects are to be moved from an initial configuration to separate destinations as a result of a robot handling operation. Proposed in this paper is a planning algorithm consisting of a global motion planner and a local motion planner. The global planner uses a precedence graph of a movable object, while the local planner uses a real-time search methodology called LRTA* (Learning Real-Time A*). Two important factors in the local planner are the generation of intermediate configurations and the design of heuristic functions. Three methods in each factor are compared in the rearrangement simulation of four, seven and 10 movable objects. The proposed method demonstrates the effectiveness of the proposed algorithm from the viewpoint of task completion time. The calculation costs for both planners are polynomial with the number of movable objects for one time activation. Simulation results indicate that the proposed algorithm can be practically applied, although not complete, to rearrangement problems with, at most, about 10 movable objects. [ABSTRACT FROM AUTHOR]

In this paper, an algorithm that acquires the intermediate goals between the initial and goal states is proposed for an agent executing multiple tasks. We demonstrate the algorithm in the problem of rearranging multiple objects. The result shows that the moving distance to transfer the entire objects to their goal configuration is 1/15 of that without using intermediate goals. We experiment using a real robot to confirm that the intermediate goal can be adapted to a real environment. Our experimental results showed that an agent could adapt the intermediate goals, which were acquired in the simulation, to the experimental environment. [ABSTRACT FROM AUTHOR]

In this paper, we present a method to generate exploration path for multiple mobile robots in a restricted working area. In exploration mission by mobile robots, the goal is to minimize the overall exploration time and ensure a thorough exploration. The key problem to be solved is to generate effective exploration paths so that mobile robots share their exploration regions in a working area. During the exploration, in situations in which new obstacles are detected on the paths or some observation points are not reachable by a mobile robot, the generated paths will not permit a thorough exploration. New paths that can ensure thorough exploration must accasionally be re-planned. The method proposed here should re-plan paths with low computational cost. The proposed method is realized by the combination of (a) deployment of the observation points in a working area using a reaction-diffusion equation on a graph, and (b) clustering the observation points to generate exploration paths that have a uniform path length for each mobile robot. Using reaction-diffusion equation on a graph in an observation point deployment will decentralize the computation of each observation point position. The efficiency and robustness of the proposed method are evaluated in simulations and in real exploration missions by two mobile robots in indoor environments. [ABSTRACT FROM AUTHOR]