In our previous research, we proposed a nonlinear digital filter to Estimate the Smoothed and Differential values of the sensor inputs by using Sliding mode system (ESDS). This estimator is able to eliminate impulsive noise efficiently from time series data. We applied this filter to processing outputs of robot sensors, and it became possible to perform robust environment recognition. ESDS is designed using a theory of variable structure system (VSS) with sliding mode. In short, ESDS is a nonlinear filter. Therefore, it is very difficult to clarify the behavior of the system analytically. Consequentially, we deal with the step function with impulsive noise as an example, and we attempt to eliminate this impulsive noise by keeping the sudden shift of signals. In this case, there is a trade-off between the noise elimination ability and the tracking performance for an input signal. Although ESDS is a nonlinear filter, it has the same trade-off as linear filters such as a low-pass filter. In order to satisfy these two conditions simultaneously, we use two filters whose parameters are independent of each other. Furthermore, in order to repress the adverse affect of impulsive noise in the steady-state, we introduced the boundary layer. Generally, a boundary layer is used so as to inhibit the harmful effect of chattering. Chattering is caused in the sliding mode system when the state of the system vibrates on the switching line of a sliding mode system. By introducing the boundary layer to ESDS, we can repress the adverse effect of impulsive noise in the steady-state. According to these considerations, we clarify the relationship between these characteristics of ESDS and the arbitrary parameters.

This paper proposes genetic algorithms (GAs) for path planning and trajectory planning of an autonomous mobile robot. Our GA-based approach has an advantage of adaptivity such that the GAs work even if an environment is time-varying or unknown. Therefore, it is suitable for both off-line and on-line motion planning. We first presents a GA for path planning in a 2D terrain. Simulation results on the performance and adaptivity of the GA on randomly generated terrains are shown. Then, we discuss an extension of the GA for solving both path planning and trajectory planning simultaneously.

In this paper, we propose the distributed stable marriage problem and apply it to planning for cooperative works of autonomous mobile robots and battery charger stations. We develop and analyze a distributed algorithm to determine the partner by message communication.