Optimization of Clustering Time by a Group of Autonomous Robots Making Use of an Exclusive Multi-Marking

Problem statement: For solving complex issues, the current tendency goes towards the swarms behaviors, realized on a basis of collective interactions, which results from a cooperative work favoring exchanges between individuals of a same group at microscopic level and allowing the emergence of complex collective behaviors at macroscopic level. Many models were inspired by these attitudes to find simple rules, guiding mobile, autonomous robots with limited capacities in their environment in order to achieve tasks like those of exploration, self-assembly and gathering. Multimarking technique as indirect communication inside the same robots group can optimize time of such achievements Approach: A method based on the reversed emergence principle combined to a genetic algorithm is presented here, making evolve a global behavior inside simulated robots group called agent-robots, with an aim to find the micro-rules forming a heap according to two approaches. The first approach accomplishes an ordinary grouping and the second one, which we propose, based on the exclusive multi-marking principle. The control device, guiding these robots-agent to succeed this task, functions on a basis of sensor-motor rules being used to arbitrate between a given number of elementary behaviors with which we equip each one of them initially. Results: Simulation results, implemented according to a reactive agent’s model, making it possible to show the consistency of the detected rules and the efficient of the proposed approach in comparison with the ordinary one, are provided and commented. The time optimization of grouping by robots like these can have a huge economic and strategic impact in sectors as important as industry, agriculture and military domain. Conclusion: Like examples, we can quote the grouping of goods in a warehouse, the grouping of ores from mines, the grouping of vegetables and fruits in gardens and the recovery of weapons, in real time, from a battle field. This work can be generalized, in the future, to the multi-heap formation to perform the classification task according to given criteria.