Multi-sensor detection and control network technology based on parallel computing model in robot target detection and recognition

Abstract

Robotics technology has become an interdisciplinary comprehensive information processing technology, and the analysis ability of unmanned robots has become an important factor to measure the intelligence of robots. On the basis of introducing the theory of multi-sensor information and the correlation of robot target detection and recognition, this paper focuses on the application of support vector machine in multi-sensor target recognition. A fusion method based on evidence theory and support vector machine is applied to target recognition, and the computational efficiency is improved by parallel computing model. The recognition performance of the traditional BP neural network and the algorithm is studied by simulation. The simulation results show that the improved algorithm has a high recognition rate under arbitrary noise, especially in large noise. The effectiveness of the improved algorithm in target detection and recognition is illustrated. At the same time, the simulation results also show that the parallel computing mode has been effectively improved.