A macroscopic analytical model of collaboration in distributed robotic systems

In this article, we present a macroscopic analytical model of collaboration in a group of reactive robots. The model consists of a series of coupled differential equations that describe the dynamics of group behavior. After presenting the general model, we analyze in detail a case study of collaboration, the stick-pulling experiment, studied experimentally and in simulation by Ijspeert et al. [Autonomous Robots, 11, 149-171]. The robots' task is to pull sticks out of their holes, and it can be successfully achieved only through the collaboration of two robots. There is no explicit communication or coordination between the robots. Unlike microscopic simulations (sensor-based or using a probabilistic numerical model), in which computational time scales with the robot group size, the macroscopic model is computationally efficient, because its solutions are independent of robot group size. Analysis reproduces several qualitative conclusions of Ijspeert et al.: namely, the different dynamical regimes for different values of the ratio of robots to sticks, the existence of optimal control parameters that maximize system performance as a function of group size, and the transition from superlinear to sublinear performance as the number of robots is increased

Collective Complexity out of Individual Simplicity

The concept of Swarm Intelligence (SI) was first introduced by Gerardo Beni, Suzanne Hackwood, and Jing Wang in 1989 when they were investigating the properties of simulated, self-organizing agents in the framework of cellular robotic systems [1]. Eric Bonabeau, Marco Dorigo, and Guy Theraulaz extend the restrictive context of this early work to include “any attempt to design algorithms or distributed problem-solving devices inspired by the collective behavior of social insect colonies,” such as ants, termites, bees, wasps, “and other animal societies.” The abilities of such systems appear to transcend the abilities of the constituent individuals. In most biological cases studied so far, robust and capable high-level group behavior has been found to be mediated by nothing more than a small set of simple low-level interactions between individuals, and between individuals and the environment. The SI approach, therefore, emphasizes parallelism, distributedness, and exploitation of direct (agent-to-agent) or indirect (via the environment) local interactions among relatively simple agents

Behaviour-based pattern formation in a swarm of anonymous robots

The ability to form patterns is useful to maximize the sensor coverage of a team of robots. Current pattern formation algorithms for multi-robot systems require the robots to be able to uniquely identify each other. This increases the sensory and computational requirements of the individual robots, and reduces the scalability, ro- bustness, and exibility of the pattern formation algorithm. The research presented in this thesis focuses on the development of a novel pattern formation algorithm called the Dynamic Neighbour Selection (DNS) algorithm. The DNS algorithm does not require robots to be uniquely identified to each other, thus improving the scal- ability, robustness, and exibility of the technique. The algorithm was developed in simulation, and demonstrated on a team of vision-enabled Bupimo robots. The Bupimo robots were developed as part of the research reported in this thesis. They are a low-cost, vision enabled, mobile robotic platform intended for use in swarm robotics research and education. Experiments conducted using the DNS algorithm were performed using a computer simulation and in real world trials. The exper- iments conducted via simulation compared the performance of the DNS algorithm to an other similar algorithm when forming a number of patterns. The results of these experiments demonstrate that the DNS algorithm was able to assume the de- sired formation while the robots traversed a shorter distance when compared to the alternative algorithm. The real robot trials had three outcomes. First, they demon- strated the functionality of the Bupimo robots, secondly they were used to develop an effective robot-robot collision avoidance technique, and lastly they demonstrated the performance of the DNS algorithm on real robots