Abstract

This project introduces an improved method called the Adaptive Response Threshold Model (ARTM) to help robot swarms efficiently divide tasks and adapt to changes in their environment. Traditional models struggle with adapting quickly when conditions change, leading to inefficiencies. Through simulations and real-world robot experiments, the new model showed better adaptability, such as reducing collisions during group tasks like gathering resources. As a result, robots could better respond to dynamic situations, increasing their chances of survival and productivity.