Bio-Inspired Search Strategies for Robot Swarms
James M., Michael A. · InTech eBooks · 2010
We developed and tested two biologically inspired search strategies for robot swarms. The first search technique, which we call the physically embedded Particle Swarm Optimization (pePSO) algorithm, is based on bird flocking and the PSO. The pePSO is able to find single peaks even in a complex search space such as the Rastrigin function and the Rosenbrock function. We were also the first research team to show that the pePSO could be implemented in an actual suite of robots. Our experiments with the pePSO led to the development of a robot swarm search strategy that did not require each bot to know its physical location. We based the second search strategy on the biological principle of trophallaxis and called the algorithm Trophallactic Cluster Algorithm (TCA). We have simulated the TCA and gotten good results with multipeak 1D functions but only fair results with multi-peak 2D functions. The next step to improve TCA performance is to evaluate the clustering algorithm. It appears that many times there is a cluster of bots near a peak but the clustering algorithm does not place the cluster centroid within the tolerance range of the actual peak. A realistic extension is to find the cluster locations via the K-means algorithm and then see if the actual peak falls within the bounds of the entire cluster.