Crossing-Point Estimation in Human-Robot Navigation-Statistical Linearization versus Sigma-Point Transformation.

Interactions between mobile robots and human operators in common areas require a high level of safety, especially in terms of trajectory planning, obstacle avoidance and mutual cooperation. In this connection, the crossings of planned trajectories and their uncertainty based on model fluctuations, system noise and sensor noise play an outstanding role. This paper discusses the calculation of the expected areas of interactions during human-robot navigation with respect to fuzzy and noisy information. The expected crossing points of the possible trajectories are nonlinearly associated with the positions and orientations of the robots and humans. The nonlinear transformation of a noisy system input, such as the directions of the motion of humans and robots, to a system output, the expected area of intersection of their trajectories, is performed by two methods: statistical linearization and the sigma-point transformation. For both approaches, fuzzy approximations are presented and the inverse problem is discussed where the input distribution parameters are computed from the given output distribution parameters.

Synchronization of decentralized multiple-model systems by market-based optimization.

Market-based optimization is a new optimization method for large decentralized systems where the distributed resource allocation of an economic system is adopted. Market-based algorithms can be interpreted as multi-agent scenarios where producer and consumer agents both compete and cooperate on a market of specified commodities. The market-based approach is applied to the synchronization of a set of local multiple-model systems. The method is extended to the case where each of the subsystems is represented by a Takagi-Sugeno (TS) fuzzy system. Although all local systems are provided with the same control input, the behaviors of the local systems are, in general, different because of different parameters in the subsystems. The task of the market-based optimization is to find an appropriate composition of subsystems so that all local systems exhibit a similar dynamical behavior. Examples show that even systems with potentially unstable local systems can be synchronized if there exists a stable combination of weighted subsystems.