A multi-robot foraging model on deciding predation risk vs. food quality trade-offs

Abstract

Foraging is the process of collecting food from the environment. Several food sources may be available in the environment with different properties like food nutrition, distance from the nest, predator risk, etc. A forager must collect food from source at any point in time. Forager may not always select a food source with the highest food quality as the associated risk due to predators may be high. On the other hand, the food source with poor quality may not be sufficient for community growth. Thus, foragers need to perform tradeoffs between predation risk and food intake depending on the food source properties and forager requirements. This behavior is common in several species like ants, squirrels, etc. In this paper, we apply the trade-off mechanisms observed in nature to multi-robot systems. Multiple robots can be used in several information/object foraging applications. The robots can autonomously make decisions using the trade-off information to select a foraging patch that satisfies the desired information/object collection goal and maximizes their safety. We propose a cost-reward model for robots that takes foraging history of the agent and the current food quantity at the nest to compute the modalities of a trade-off. The model is validated through simulations on ROS.