Abstract
With the expansion of the research on smart cities, unmanned aerial vehicles have gained popularity in recent years, especially in search and surveillance operations. Flying robots are efficient in covering vast areas quickly, making them ideal for tasks such as search and rescue operations, and surveillance missions. To increase the productivity of these operations multi-agent systems of drones working in cooperation should be used. However, the effectiveness of cooperative search and coverage in environments with scattered uncertainties can be limited due to operational constraints. One of the primary challenges in cooperative systems is reducing operating costs while improving or maintaining efficiency. Operational limitations such as time, number of agents, and dimensions of the surveillance region significantly impact the efficiency of a multi-agent system in cooperative search and coverage missions. By considering these operational limitations and optimizing performance accordingly, cooperative search and coverage missions can be executed efficiently with reduced operating costs. To address this challenge, in this paper, an optimization-based approach has been developed for distributed cooperative search and coverage in uncertain environments with unknown stationary targets utilizing a team of agents. This approach formulates the cooperative search and coverage mission as a multi-object optimization problem. The objective function includes minimizing the number of agents used while maximizing the coverage rate. The optimization procedure is used to compute optimal operational parameters for the mission, including the number of agents, their starting positions, and so on. The effectiveness of the proposed strategy is validated and demonstrated via cooperative search and coverage problems simulation. The mission performance constrains were fully satisfied by reaching uncertainty reduction of 0.52 (should be less than 0.6) and founded targets ratio of 0.75 (should be greater than 0.7). The simulation results show that the proposed approach can significantly improve mission efficiency while reducing operating costs.