Modeling and simulation of dynamic ant colony's labor division for task allocation of UAV swarm

- In this paper, we introduce our method for dynamic task allocation problem of unmanned aerial vehicle.
- A new dynamic ant colony's labor division (DACLD) model is proposed, which also has a better practicability in various multi-agent systems.
- DACLD can meanwhile get both UAVs' states and real-time positions, and has high degree of self-organization and flexibility under dynamic environments.

The problem of unmanned aerial vehicle (UAV) task allocation not only has the intrinsic attribute of complexity, such as highly nonlinear, dynamic, highly adversarial and multi-modal, but also has a better practicability in various multi-agent systems, which makes it more and more attractive recently. In this paper, based on the classic fixed response threshold model (FRTM), under the idea of “problem centered + evolutionary solution” and by a bottom-up way, the new dynamic environmental stimulus, response threshold and transition probability are designed, and a dynamic ant colony's labor division (DACLD) model is proposed. DACLD allows a swarm of agents with a relatively low-level of intelligence to perform complex tasks, and has the characteristic of distributed framework, multi-tasks with execution order, multi-state, adaptive response threshold and multi-individual response. With the proposed model, numerical simulations are performed to illustrate the effectiveness of the distributed task allocation scheme in two situations of UAV swarm combat (dynamic task allocation with a certain number of enemy targets and task re-allocation due to unexpected threats). Results show that our model can get both the heterogeneous UAVs' real-time positions and states at the same time, and has high degree of self-organization, flexibility and real-time response to dynamic environments.