Search

Against the narrow range of sonar images for underwater monitoring, an adaptive sonar image-stitching algorithm is proposed in this paper. Compared with conventional sonar image-stitching algorithms, this algorithm can be applied to stitch sonar images with fewer features and better results. This paper first introduces the hardware platform of the underwater monitoring system and a 3D model of the underwater rotational mechanism. Next, related image pre-processing is conducted to improve its accuracy. A SURF algorithm is then applied to extract the speeded-up robust feature (SURF) points. Compared with a threshold, if the number of SURF points is larger, the SURF algorithm is applied to stitch the sonar images or else the phase correlation method is selected to mosaic the sonar images. Finally, a weighted smoothing fusion algorithm based on a maintained boundary is proposed to fuse the sonar images. The algorithm is verified to give good performance in mosaicing sonar images by an experimental study.

With the increasing complexity of modern industry, the traditional single-target control of swarm robots can no longer meet application requirements. Hence, this article addresses compound task control for swarm robot systems, where the control aim and the dynamics of the robot are modeled by static group-bipartite consensus and Euler–Lagrange systems, respectively. After introducing the concept of static group-bipartite consensus in networked Euler–Lagrange systems, a distributed group-bipartite consensus control protocol with integral action is designed, and the criterion that ensures that static group-bipartite consensus is reached is presented. The most remarkable feature of the proposed algorithm is that it can accurately calculate the final state of system convergence. Finally, simulation examples are presented to verify the theoretical results. [ABSTRACT FROM AUTHOR]