Non-uniform area coverage is a challenge in which different regions of an environment require varying levels of attention. In this paper, we propose a novel multi-task swarming approach to address the non-uniform area coverage problem. In our case, this problem involves two primary sub-tasks: detecting areas of interest and subsequently covering those areas. We first approach the problem as a single-task problem solved by a homogeneous swarm. We then demonstrate how it can be solved more efficiently as a multi-tasking challenge leveraging the different capabilities of agents in a heterogeneous swarm. Our findings demonstrate reductions in time by 67% in the single-task scenario compared to alternative algorithms and by 83% in the multi-task scenario. Our results also show that optimal task distribution in our multi-tasking swarm significantly enhances efficiency and may reduce costs, highlighting the benefits of task specialization in multi-tasking approaches. |
