Abstract
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. Multi-marking 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.
Highlights
It’s obvious that the collective work is more interesting than the individual one, since it makes it possible to optimize the time necessary to carry out a partible task or to perform tasks that cannot be performed
The rest of this study is organized as follows: in Materials and Methods section, we introduce the multivalent systems while focusing on the reactive agents, representing the simulation model used in our experiments, we explain the Genetic Algorithm (GA) algorithm representing the selected evolutionary approach to find the appropriate chromosomes, implemented as a table structure named lockup table LT, (Table 1), for the achievement of mentioned above tasks, we describe the principal to form a heap according to the normal approach and we describe the principal to make a heap according to the proposed one based on the technique of the exclusive multi-marking
While being inspired from behaviors of social insects, a lot of works in collective robotics field hope to solve problems recognized as difficult and to help, by the same occasion, ethologists to widen their knowledge relating to the way in which biological communities realize the cooperation
Summary
It’s obvious that the collective work is more interesting than the individual one, since it makes it possible to optimize the time necessary to carry out a partible task or to perform tasks that cannot be performed. The fact that collective work makes it possible to exceed specific limitations to group members is one of the reasons which push us to be organized and to work in cooperation This is natural at the human level and certain animalist groups like social insects (for instance: ants and bees) or predators and preys which live in community (for instance: Wolves and wildebeests). From this point of view, the cooperative exploration and exploitation of an unknown environment by robots group belong to the most interesting subjects related to the domain of the collective intelligence in general and the collective robotics in particular. The advantage is the possibility for the low-level, in spite of the simplicity of its components, to self-organize providing flexibility and robustness in the high-level
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
