In our previous research, we showed that the maximum flow and the minimum cut problem can be solved by using a resistive circuit consisting of nonlinear devices with a saturation characteristic. Usually, the flow network consists of three elements, such as connectivity between nodes, branch capacities, and flows. The network information obtained from nonlinear resistive circuit analysis also has conventional three elements such as the connectivity of edge, resistances of edge (= branch capacities) and currents (= flows). In addition, each node voltage is obtained as a 4th element. In this paper, a novel minimum cut solution by using node information as 4th element is proposed in a completely different way from conventional methods. When a number of minimum cuts exist in a given flow network, there is no conventional algorithm which can simultaneously give these cuts at the moment. However, all minimum cuts can be obtained at the same time by using proposed method. Moreover, since the proposed method is realizable by a nonlinear resistive circuit, speed improvement of the minimum cut algorithm can be expected.

In advance of network communication society by the Internet, the way how to send data fast with a little loss has become an important transportation problem. A generalized maximum flow algorithm provides the best solution to the transportation problem of determining which route is appropriated to exchange data. Therefore, the importance of the maximum flow algorithm continues to grow. In this paper, we propose a Maximum-Flow Neural Network (MF-NN) in which branch nonlinearity has a saturation characteristic and by which the maximum flow problem can be solved with analog high-speed parallel processing. Moreover, the stability of proposed network is discussed. The proposed neural network for the maximum flow problem can be achieved by using a nonlinear resistive circuit where each connection weight between nodal neurons has a sigmoidal function. The parallel hardware of the MF-NN will be easily implemented.

A concept of mutual transportation by cooperative multiple mobile robots is proposed in this paper, which enables robots to process complicated or heavy-duty tasks, and overcome the large obstruction in an environment. A method of mutual transportation utilizing forklift mechanisms is introduced with discussion on the lifting process and moving process. Cooperative mobile robots including a holonomic omni-directional platform and a forklift mechanism are developed. Finally, as an example of applications of mutual transportation, step-climbing motion is presented, and the motion realized by the developed mobile robots is shown.

A new driving mechanism for holonomic omnidirectional mobile robots is designed, which enables 3 DOF motion control by three correspondent actuators in a decoupled manner with no redundancy. A prototype of the omnidirectional mobile robot with the driving mechanism is developed including a parallel link suspension mechanism. The kinematics of the omnidirectional mobile robot is also analyzed, and simulation for velocity control of the robot is performed by a method for velocity modulation with interpolation to achieve the given target position and velocity.

A parallel inference machine based on the dataflow model and the mechanisms to support two types of logic programming languages are presented. The machine is constructed from multiple processing elements and structure memories interconnected through a low-latency hierarchical network. The preliminary evaluation results of the experimental machine are also presented. The evaluation results show that the machine can exploit parallelism in programs.