We present a supervised learning from demonstration system capable of training stateful and recurrent collective behaviors for multiple agents or robots. A model space of this kind is often high-dimensional and consequently may require a large number of samples to learn. Furthermore, the inverse problem posed by emergent macrophenomena among multiple agents presents major challenges to supervis...

We propose and evaluate a novel approach to the online synthesis of neural controllers for groups and swarms of autonomous robots. We combine online evolution of weights and network topology with neuromodulated learning in a completely decentralised manner. We demonstrate our method through a series of simulation-based experiments in which a group of e-puck-like robots must perform a dynamic co...

Swarm robotics is a relatively new paradigm for the coordination of multiple robots solely based on local interactions using simple individual robotic nodes. Originally inspired by the intriguing capabilities of natural swarms such as termites, wasps, and ants which are capable of complex tasks such as nest building, brood sorting, or routing for optimal foraging, swarm robotics has the potenti...

We present a simple methodology for incremental development of complex adaptive behaviors in robots. The methodology decomposes a given root strategy into a tree of self-contained supporting strategies that can be fully implemented and tested before the next strategy is added. The methodology also uses comparative performance tests for each new strategy relative to its predecessor. The methodol...

Task allocation is a complex and open problem for multi-robot systems. In this paper, we present three strategies for dynamically allocating tasks in a colony of mobile robots. The system is partially inspired on auction and thresholds-based methods and tries to determine the optimum number of robots that are needed to solve specific tasks, or sub-goals, in order to minimize the execution time ...

To be successful in real-world applications, online robots require a high degree of autonomy and local intelligence to deal with both the uncertainties in their surroundings and the arbitrary transmission delay of the Internet. This paper describes our progress in building a new framework for remote operation of multiple online robots, which can be accessed through any Java-enabled browsers. Ou...

Foraging has been identified as a benchmark for collective robotics. It consists on exploring an area and gathering prespecified objects from the environment. In addition to efficiently exploring an area, foragers have to be able to find special targets which are common to the whole population. This work proposes a method to cooperatively perform this particular task. Instead of using local or ...

Communication plays a central role in the biology of most species (MaynardSmith and Szathmàry (1997)), particularly in social species where it allows for the transfer of vital information among group members, thereby ensuring ecological success (Wilson (1975)). Understanding communication and its evolution are therefore essential to our understanding of the mechanisms driving social behavior. D...

In this paper, to analyze the transport of autonomous robots, an artificial Capital market is used. Capital market is considered as a pier which loading and unloading of cargo is done. Autonomous robots load and unload from the ship to the warehouse wharf or vice versa. All the robots have the ability of transporting the loads, but depending on loads and the location of unloading (or loading) a...

Robotic swarms are decentralized systems formed by a large number of robots. A common problem encountered in a swarm is congestion, as a great number of robots often must move towards the same region. This happens when robots have a common target, for example during foraging or waypoint navigation. We propose three algorithms to alleviate congestion: in the first, some robots stop moving toward...