Swarm robotics is an emerging field that can offer efficient solutions to real-world problems with minimal cost. Despite recent developments in the field, however, it is still not sufficiently mature, and challenges clearly remain. The dynamic deadline problem is neglected in the literature, and thus, time-sensitive foraging tasks are still an open research problem. This paper proposes a novel approach—ED_Foraging—that allows simple robots with limited sensing and communication abilities to perform complex foraging tasks that are dynamic and time constrained. A new mathematical model is developed in this paper to utilize epidemiological modeling and predict the dynamics of resource deadlines. Moreover, an improved dynamic task allocation (DTA) method is proposed to assign robots to the most critical region, where a deadline is represented by a state and time. The main goal is to reduce the number of expired resources and collect them as quickly as possible by giving priority to those that are more likely to expire if not collected. The deadlines are unknown and change dynamically. Thus, the robots continuously collect local information throughout their journeys and allocate themselves dynamically to the predicted hotspots. In the experiments, the proposed approach is adapted to four DTA methods and tested with different setups using simulated foot-bot robots. The flexibility, scalability, and robustness of this approach are measured in terms of the foraging and expiration rates. The empirical results support the hypothesis that epidemiological modeling can be utilized to handle foraging tasks that are constrained by dynamic deadlines. It is also confirmed that the proposed DTA method improves the results, which were found to be flexible, scalable, and robust to changes in the number of robots and the map size.
An Adaptive Epidemiology-Based Approach to Swarm Foraging with Dynamic Deadlines
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