Abstract-In this paper, a spatial-temporal collaborative sequential Monte Carlo architecture for mobile robot localization is designed to well suites intelligent environment for service robotic system. A proposed algorithm, namely Distributed Proportional Allocation-Augmented Particle Filter (DPA-APF), resolves the sensor collaboration problem by the processes of augmented sampling, inter-node resampling, inner-node resampling and particle exchange. These procedures exploit data parallelism and pipelining of resampling operations and improve the scalability of distributed particle filters (PFs).Moreover, modified visual and laser sensor perception models are also addressed to guarantee reliable and accurate robot localization in dynamic scenarios that robot coexists with people. The proposed method is applied to a home-care robotic intelligent room with distributed smart nodes, and the

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To improve the dynamic performance of the two-dimensional sensors, we presented a modified ANN (artificial neural network) inverse compensating method. The modified method is based on the state-space equation, which can fully describe the complex sensor and make the obtained inverse compensator more accurate, as well as decrease the derivative orders appeared in the inverse compensator. Simulation result verifies the modified compensator is more suitable to be used to compensate the complex two-dimensional sensor and the compensating result of the modified method is better that of the unmodified one.

In this paper, it shows that the neural network inverse system control of induction motor is robust to machine parameters, but like other high performance control methods, The induction motor control system with NNISC (Neural network inverse system controller) also need accurate flux and speed as feedback signals, therefore, the EKF(Extended Kalman Filter) is adopted to estimate the stator resistance, rotor resistance and rotor flux jointly, the adaptive estimation of rotor flux when variable resistance is implemented, subsequently, the designed flux estimator is used to provide the estimated flux to the flux regulator in neural network inverse system control of induction motor, In the last, the proposed method is validated by simulation.