This paper improves the accuracy of a mine robot’s positioning and mapping for rapid rescue. Specifically, we improved the FastSLAM algorithm inspired by the lion swarm optimization method. Through the division of labor between different individuals in the lion swarm optimization algorithm, the optimized particle set distribution after importance sampling in the FastSLAM algorithm is realized. The particles are distributed in a high likelihood area, thereby solving the problem of particle weight degradation. Meanwhile, the diversity of particles is increased since the foraging methods between individuals in the lion swarm algorithm are different so that improving the accuracy of the robot’s positioning and mapping. The experimental results confirmed the improvement of the algorithm and the accuracy of the robot.

A large amount of data transmission is one of the challenges faced by communication systems. In this paper, we revisit the intelligent receiver consisting of a neural network, and we find that the intelligent receiver can reduce the data at the transmitting end while improving the decoding accuracy. Specifically, we first construct a smart receiver model, and then design two ways to reduce the data at the transmitter side, namely, end-of-transmitter data trimming and equal-interval data trimming, to investigate the decoding performance of the receiver under the different trimming methods. The simulation results show that the receiver still has an accurate decoding performance with a small amount of trimming at the end of the transmitter data, while the decoding performance of the smart receiver is better than that of the conventional receiver with complete data when the data is trimmed at equal intervals. Moreover, the receiver with equally-spaced data cropping has a lower BER when the data at the transmitter side is reduced by the same data length. This paper provides a new solution to reduce the amount of data at the transmitter side.