Collaborative Path Planning and Target Search in Multi-Robot Systems with PID-Controlled Uniform Area Partitioning

Autonomous exploration with target searching using multi-robot systems is a key area of research with applications in search-and-rescue, disaster response, environmental monitoring and space exploration. These systems enable faster and more efficient coverage of unknown environments compared to singlerobot solutions. However, challenges in equal area partitioning, efficient coverage path planning, and accurate target detection remain significant. This paper introduces a novel multi-robot exploration framework for ground robots operating within unknown convex polygonal environments, coordinated by a Unmanned Aerial Vehicle (UAV) as a central coordinator, forming an effective semi-centralized system. The approach overcomes the limitations of high overlap, inefficient coverage, and the lack of explicit area allocation in previous methods through a dynamic PID-controlled bisector-based partitioning strategy. By dividing the exploration region equitably among the robots, our approach minimizes coverage time and ensures efficient exploration. Each sub-region is discretized into uniform grid points, and an optimized path is generated using a Traveling Salesman Problem (TSP) formulation. Moreover, radio-frequency identification (RFID)-based target detection scheme is proposed to detect targets in a search and rescue operation to reduce false positives, overcoming challenges commonly faced by visual-based detection methods. Simulation results demonstrate the effectiveness of the proposed method in balancing exploration workloads, reducing coverage time, and improving detection reliability.