Adaptive Control in Swarm Robotic Systems

Inspired by the collective behavior observed in natural insects, swarm robotics is a new approach in designing control algorithms for a large group of robots performing a certain task. In such robotic systems, an individual robot with only limited capabilities in terms of sensing, computation, and communication can adapt its own behavior so that a desired collective behavior emerges from the local interactions among robots and between robots and the environment. Swarm robotics has been the focus of increased attention recently because of the beneficial features demonstrated in such systems, such as higher group efficiency, robustness against the failures of individual robots, flexibility to adapt to changes in the environment, and scalability over a wide range of group sizes. In this article we present an adaptive algorithm to regulate the behavior of an individual robot performing collective foraging tasks. Through the interactions between robots, a desired division of labor can be achieved at the group level. Robot groups also demonstrate the ability to improve energy efficiency and its potential robustness in different environments

Adaptive foraging for simulated and real robotic swarms: The dynamical response threshold approach

Developing self-organised swarm systems capable of adapting to environmental changes as well as to dynamic situations is a complex challenge. An efficient labour division model, with the ability to regulate the distribution of work among swarm robots, is an important element of this kind of system. This paper extends the popular response threshold model and proposes a new adaptive response threshold model (ARTM). Experiments were carried out in simulation and in real-robot scenarios with the aim of studying the performance of this new adaptive model. Results presented in this paper verify that the extended approach improves on the adaptability of previous systems. For example, by reducing collision duration among robots in foraging missions, our approach helps small swarms of robots to adapt more efficiently to changing environments, thus increasing their self-sustainability (survival rate). Finally, we propose a minimal version of ARTM, which is derived from the conclusions drawn through real-robot and simulation results

Towards Swarm Calculus: Urn Models of Collective Decisions and Universal Properties of Swarm Performance

Methods of general applicability are searched for in swarm intelligence with the aim of gaining new insights about natural swarms and to develop design methodologies for artificial swarms. An ideal solution could be a `swarm calculus' that allows to calculate key features of swarms such as expected swarm performance and robustness based on only a few parameters. To work towards this ideal, one needs to find methods and models with high degrees of generality. In this paper, we report two models that might be examples of exceptional generality. First, an abstract model is presented that describes swarm performance depending on swarm density based on the dichotomy between cooperation and interference. Typical swarm experiments are given as examples to show how the model fits to several different results. Second, we give an abstract model of collective decision making that is inspired by urn models. The effects of positive feedback probability, that is increasing over time in a decision making system, are understood by the help of a parameter that controls the feedback based on the swarm's current consensus. Several applicable methods, such as the description as Markov process, calculation of splitting probabilities, mean first passage times, and measurements of positive feedback, are discussed and applications to artificial and natural swarms are reported