Multi-robot coverage algorithm in complex terrain based on improved bio-inspired neural network

Abstract

Biological neural network (BNN) algorithms have become popular in coverage search in recent years. However, its edge activity values are weak, and it is simple to fall into a local optimum at a late stage of coverage. When applied to complex environments, the 3D BNN network structure has high computational and storage complexity. In order to solve the above problems, we propose an algorithm for multi-robot cooperative coverage of complex terrain based on an improved BNN. The algorithm models the complex terrain using a 2.5-dimensional (2.5D) elevation map. Combining the dual-layer BNN network with the 2.5D elevation map, we propose an elevation value priority mechanism. This mechanism lets the robot make elevation-based decisions and prioritizes higher terrain areas. The dual neural network's first layer plans the robot's path in normal mode. The second network layer helps the robot escape the local optimum. Finally, the algorithm's full coverage effect in complex terrains and the speed of covering high terrain are verified by simulations. The experiments show that our algorithm preferentially covers high points of the region and eventually covers 100% of complex terrain. Compared with other algorithms, our algorithm covers more efficiently and takes fewer steps than others. The speed of covering high terrain areas has increased by 34.51%.