A Novel Algorithm of Cooperative Foraging for Swarm Robot Based on Neural Network Swarm robotics is an emerging discipline that has a great deal of potential realworld applications. Swarm robotics aims to produce robust, scalable, and flexible selforganizing behaviors through local interactions of large numbers of simple robots. In this paper, a novel pheromone model of swarm foraging behavior is developed based on a neural network. The output of the neurons corresponds to the density or pheromone. The output diffuses to the neighboring neurons through their local connections. The neural network is updated based on the proposed evaporation model. The evolution of the neural network can mimic the features of pheromones. Simulation experiments are performed under different foraging scenarios. The experimental results verify the effectiveness of the proposed pheromone model.

What is A Robot Swarm: A Definition for Swarming Robotics The swarm, a type of multiagent system, has enjoyed a recent surge in popularity within the autonomous robotics field. Despite a variety of theoretical and simulated research work in the area of swarm theory and multiagent artificial intelligence, the practical use of swarms remains limited. Though many limiting factors lie on the technical front, one limiting factor may be a lack of appreciation for swarm capabilities and applications as opposed to those of conventional robotics. To help address the latter limiting factor, this paper proposes a definition of a swarm in the context of autonomous robotics, describes many realworld problems that can be addressed through use of swarms, and details current applications of swarming robotic systems.