Dynamic interactive self organizing aggregation method in swarm robots.

The present study aims to propose a dynamic interactive self-organizing aggregation (DISA) method for swarm robots. The proposed method was determined by way of the movement of swarm robots, obstacle, and robot sensors. The controller used in the DISA method helps the state selector decide through the utilization of these sensors. Systematic simulations were conducted a different number of robots {10, 25, 50}, different detection radii {3, 4} and different arena sizes {40 × 40, 50 × 50, 60 × 60}. The performance of aggregation behavior was compared with other aggregation methods recommended in literature using Total Distance (TD) between robots, Cluster Metrics (CM), Expected Cluster Size (ECS) metric and aggregation completion time. Moreover, noise at different intensities was applied to sensor inputs of the robots. The robustness of the effect of increasing noise on aggregation behavior was examined comparatively. Consequently, the simulation results based on the other compared methods indicated that the utilization of the proposed DISA method led to a higher performance by 88% in the ECS and CM metric as well as in all TD metric measurements and aggregation completion time results.

Fuzzy-based self organizing aggregation method for swarm robots.

Fuzzy-based self-organizing aggregation method was suggested in the present study for swarm robots. In the suggested method, Swarm robots evaluate their limited sensor input via rules of fuzzy logic and display aggregation behavior with the suggested aggregation method. In the meantime, swarm robots also have the ability to escape the obstacles in a bounded arena with this method. Swarm robots perceive the neighboring robots with this method, make individual decisions and display aggregation behaviors. Different from the traditional self-organizing aggregation methods, the suggested approach utilizes fuzzy logic controllers to evaluate limited sensor data. Systematic experiments were applied on different number of swarm robots with different detection areas in arenas of different sizes. Moreover, noise was applied on the sensor inputs for examining the performance of the fuzzy logic based self-organizing aggregation method. The scalability and flexibility of the self-organizing aggregation behaviors of swarm robots were evaluated by way of systematic experiments. The swarm robots displayed aggregation behavior during the systematic experiments applied despite the changes in the number of robots and detection distances.