Bio-inspired exploring and recruiting tasks in a team of distributed robots over mined regions

In this paper, the problem of coverage and exploration of unknown and mined spaces is investigated using a team of robots. The goal is to propose a strategy capable to minimize the overall exploration and mine disarming time, while avoiding that robots pass many times through the same places. The key problem is that the robots simultaneously have to explore different regions of the environment and for this reason they should spread among the search areas. However, at the same time, when a mine is discovered, more robots are needed to be engaged in order to disarm the mine. Because the problem of the unknown lands with the constraint to disarm mine is a NP hard problem, we proposed a combined approach using two bio-inspired meta-heuristic approaches such as Ant Colony Optimization (ACO) and Firefly algorithm (FA) to perform the coordination task among robots. We have compared the simulation results considering a common exploration task of the robot spreading and an ACO based robot recruiting(ATS-RR) and Firefly inspired (FTS-RR) strategies to perform the mine disarming task. Performance has been evaluated in terms of both overall exploring time and mine disarming time and in terms of number of accesses distributed in the operative grid area. The results show that the combined approach provides a better tool for both exploration and disarmament

Bio-inspired exploring and recruiting tasks in a team of distributed robots over mined regions

In this paper, the problem of coverage and exploration of unknown and mined spaces is investigated using a team of robots. The goal is to propose a strategy capable to minimize the overall exploration and mine disarming time, while avoiding that robots pass many times through the same places. The key problem is that the robots simultaneously have to explore different regions of the environment and for this reason they should spread among the search areas. However, at the same time, when a mine is discovered, more robots are needed to be engaged in order to disarm the mine. Because the problem of the unknown lands with the constraint to disarm mine is a NP hard problem, we proposed a combined approach using two bio-inspired meta-heuristic approaches such as Ant Colony Optimization (ACO) and Firefly algorithm (FA) to perform the coordination task among robots. We have compared the simulation results considering a common exploration task of the robot spreading and an ACO based robot recruiting(ATS-RR) and Firefly inspired (FTS-RR) strategies to perform the mine disarming task. Performance has been evaluated in terms of both overall exploring time and mine disarming time and in terms of number of accesses distributed in the operative grid area. The results show that the combined approach provides a better tool for both exploration and disarmament

Self-Organized Structures: Modeling Polistes dominula Nest Construction with Simple Rules

The self-organized nest construction behaviors of European paper wasps (Polistes dominula) show potential for adoption in artificial intelligence and robotic systems where centralized control proves challenging. However, P. dominula nest construction mechanisms are not fully understood. This research investigated how nest structures stimulate P. dominula worker action at different stages of nest construction. A novel stochastic site selection model, weighted by simple rules for cell age, height, and wall count, was implemented in a three-dimensional, step-by-step nest construction simulation. The simulation was built on top of a hexagonal coordinate system to improve precision and performance. Real and idealized nest data were used to evaluate simulated nests via two parameters: outer wall counts and compactness numbers. Structures generated with age-based rules were not significantly different from real nest structures along both parameters