Maintenance and inspection of old buildings, bridges, tunnels and so on are one of the important tasks of robots, and these robots are expected to be used for search and rescue missions in case of disasters. However, in such environment, it is difficult to operate robots autonomously because the surface of them are complex and unknown. In inspection tasks, many robots are expected to be simultaneously operated by a small number of operators, therefor lack of autonomy is a serious problem for practical use. To solve this problem, we focus on flexible mechanisms of bodies of creatures, and we propose two types of multi-legged robot. One is for climbing vertical walls and the other is for climbing vertical pipes. We developed prototype robots and conducted experiments. As a result, we confirmed that the developed robot can climb autonomously by utilizing the passive mechanisms.

In this paper, we modify a simulated annealing-based (SA-based) household synthesizing method in order to synthesize a population in the same scale of the target area. Micro-simulations (MS) and agent-based simulations (ABS) are recently employed for social simulations. For enabling MS or ABS, each household composition such as ages, occupations, or other properties of each member of a household should be prepared before simulations. However real household compositions are not available to researchers due to privacy or security reasons. Therefore, we need to synthesize household compositions from available statistics for MS or ABS. However, it should be noted that the synthesized population is just an artificial population that is suitable to the employed statistics. In this paper, we modify an SA-based household synthesizing method based on statistics. We propose a household generation method, new 21 statistics, and an age exchange method for members in households. In our previous research, we employed nine statistics to synthesize populations. In this paper, we synthesize an artificial population from 21 statistics, and we show how errors between the artificial population and real statistics are reduced by the proposed algorithm.

This paper deals with merging control of automated vehicles using distributed model predictive control. Firstly, we consider a road model which has two main lanes. In the distributed control algorithm, vehicles repeat sharing the planned future trajectory with other vehicles traveling in the vicinity of each vehicle by using communication and re-planning its own trajectory considering the future trajectory of other vehicles. Moreover, in order to further improve the stability of model predictive control, terminal constraints are set and feasible conditions for optimization problems under the terminal conditions are derived. At the last part of this paper, we discuss the result of numerical simulations using distributed merging control algorithm and mention the effectiveness of proposed methods and future works.

Power usage optimization by two stage stochastic programming is studied for a single smart house equipped with a solar power generator and a rechargeable battery, and also for a power transfer network composed of smart houses. Optimization is defined as the minimization of the total power purchase over a certain period with decision variables which describe the power purchase, charge/discharge and mutual transfer. Linear programming model (LP) is extended to a stochastic programming model (SP) when power generation and consumption are given not as fixed values but as stochastic variables under a certain distribution. Two types of recourse variables are introduced to express the power purchase and the charge to and discharge from a battery. Numerical experiments are executed to compare the optimal plans obtained by LP, 1-variable SP and 2-variables SP using measurement data obtained by survey researches. The first experiment is for a community which shares the power generator and battery. The second experiment is for a small network of two smart houses which mutually transfer the generated power. Both experiments show the effectiveness of 2-variables SP with charge/discharge recourses. Obtained global optimality for a whole network is compared with a local optimality for each house under a certain power price. Price adjustment is discussed for the global optimality based on the local optimality.