Neural Network Modelling of Optimal Robot Movement Using Branch and Bound Tree

In this paper a discrete competitive neural network is introduced to calculate the optimal robot arm movements for processing a considered commitment of tasks, using the branch and bound methodology. A special method based on the branch and bound methodology, modified with a travelling path for adapting in the neural network, is introduced. The main neural network of the system consists of different subnets, each of which is designed for a special propose. The common neuron for competitive layers and the state presentation neurons for different layers are also presented and used in the design of neural network architecture. Sigma P1 neurons are used to increase the calculation's performance. A case study with different commitments of tasks is simulated and the results are compared with Hopfield and Tank net from different view points.