In the field of robot path planning, aiming at the problems of the standard genetic algorithm, such as premature maturity, low convergence path quality, poor population diversity, and difficulty in breaking the local optimal solution, this paper proposes a multi-population migration genetic algorithm. The multi-population migration genetic algorithm randomly divides a large population into several small with an identical population number. The migration mechanism among the populations is used to replace the screening mechanism of the selection operator. Operations such as the crossover operator and the mutation operator also are improved. Simulation results show that the multi-population migration genetic algorithm (MPMGA) is not only suitable for simulation maps of various scales and various obstacle distributions, but also has superior performance and effectively solves the problems of the standard genetic algorithm.

To solve the problem of low accuracy of a mobile node localization in an underwater sensor network, this paper proposes a hybrid localization algorithm based on Doppler shift and angle of arrival (AOA) for the underwater mobile node (DAHL). This algorithm utilizes the Doppler frequency shift between the different signals received from the anchor node and the mobile node and the AOA measurement of the signal reflected by the mobile node to estimate the instantaneous position and velocity information of the mobile node through a two-stage algebraic method. In view of the existing measurement errors in the actual situation, the position and velocity estimation errors of the mobile node are optimized by introducing auxiliary parameters. Theoretical analysis and simulation experiments show that the DAHL method can effectively improve the localization accuracy of the mobile node. The localization accuracy of this algorithm can be close to the Cramér-Rao Lower Bound (CRLB) with the condition of small measurement errors.

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