A multi-robot two-stage adaptive sampling and rolling task reallocation method for carpet-like search

In field ecological monitoring, search and rescue, and disaster prevention missions, carpet search strategies are typically employed. The importance and quantity of tasks often change dynamically, requiring multi-robot systems to possess the capability to flexibly respond to dynamic variations. However, traditional task allocation methods are predominantly designed for static small-scale environments and struggle to meet the demands of large-scale dynamic task scenarios, nor are they optimized for carpet search strategies. To address this issue, this paper formulates carpet search as the sequential execution of multiple tasks within elongated regions and proposes a Two-stage Adaptive Sampling Rolling Task Reallocation (TASRTRA). In TASRTRA, to resolve the problem of high-importance tasks being overlooked due to random probability sampling, an adaptive probability sampling mechanism is constructed that dynamically adjusts the sampling probability distribution to increase the sampling probability of high-importance tasks, thereby ensuring a balance between task value maximization and computational efficiency. Considering the time-varying characteristics of task importance during execution, the marginal cost calculation method is improved by introducing a dynamic importance variation factor, enabling real-time perception of task importance evolution and dynamic reconstruction of task allocation schemes in multi-robot systems. Furthermore, to mitigate the elevated collision risk caused by intersecting task paths, an intersecting path exchange strategy is introduced to optimize the task allocation scheme, reducing collision probability while enhancing overall system performance. Experimental results demonstrate that TASRTRA exhibits superior performance under dynamic and various task-scale scenarios. Compared with traditional methods, it achieves an average reward improvement of 7.62% and an average runtime reduction of 15.15%. The method adapts effectively to various dynamic task conditions, demonstrates strong robustness, and significantly optimizes task allocation efficiency.