Multi-robot cooperative search algorithm based on bio-inspired neural network and DMPC

To solve the problem of multi-robot coverage search in unknown areas, a multi-robot cooperative search algorithm based on bio-inspired neural networks and distributed model predictive control (DMPC) is proposed. Firstly, the unknown region is represented by raster map, and then the bio-inspired neural network is established based on the raster map to represent dynamic search environment. In the bio-inspired neural network, the activity value of unsearched grids is higher than searched grids and obstacle grids. On this basis, in order to balance the short-term gains and long-term gains in the process of robot coverage search, and avoid falling into local optimization in the later period, DMPC is introduced as the decision-making method. The increment of the neuron activity value of the raster covered by the robot in the forecast period is selected as the main excitation function to guide the robot to search the uncovered area. The optimal solution is obtained by using the differential evolutionary algorith (DE). Finally simulation experiments revify the effectiveness and superiority of the proposed method. © 2021, Editorial Office of Control and Decision. All right reserved.