On Using Simulation to Predict the Performance of Robot Swarms.

The discrepancy between simulation and reality-known as the reality gap-is one of the main challenges associated with using simulations to design control software for robot swarms. Currently, the reality-gap problem necessitates expensive and time consuming tests on physical robots to reliably assess control software. Predicting real-world performance accurately without recurring to physical experiments would be particularly valuable.

Empirical assessment and comparison of neuro-evolutionary methods for the automatic off-line design of robot swarms.

Neuro-evolution is an appealing approach to generating collective behaviors for robot swarms. In its typical application, known as off-line automatic design, the neural networks controlling the robots are optimized in simulation. It is understood that the so-called reality gap, the unavoidable differences between simulation and reality, typically causes neural network to be less effective on real robots than what is predicted by simulation.

Automatic modular design of robot swarms using behavior trees as a control architecture.

We investigate the possibilities, challenges, and limitations that arise from the use of behavior trees in the context of the automatic modular design of collective behaviors in swarm robotics. To do so, we introduce Maple, an automatic design method that combines predefined modules-low-level behaviors and conditions-into a behavior tree that encodes the individual behavior of each robot of the swarm. We present three empirical studies based on two missions: aggregation and Foraging.

Complexity Measures: Open Questions and Novel Opportunities in the Automatic Design and Analysis of Robot Swarms.

Complexity measures and information theory metrics in general have recently been attracting the interest of multi-agent and robotics communities, owing to their capability of capturing relevant features of robot behaviors, while abstracting from implementation details. We believe that theories and tools from complex systems science and information theory may be fruitfully applied in the near future to support the automatic design of robot swarms and the analysis of their dynamics. In this paper we discuss opportunities and open questions in this scenario.

Concurrent design of control software and configuration of hardware for robot swarms under economic constraints.

Designing a robot swarm is challenging due to its self-organized and distributed nature: complex relations exist between the behavior of the individual robots and the collective behavior that results from their interactions. In this paper, we study the concurrent automatic design of control software and the automatic configuration of the hardware of robot swarms. We introduce Waffle, a new instance of the AutoMoDe family of automatic design methods that produces control software in the form of a probabilistic finite state machine, configures the robot hardware, and selects the number of robots in the swarm.

Automatic Off-Line Design of Robot Swarms: A Manifesto.

Designing collective behaviors for robot swarms is a difficult endeavor due to their fully distributed, highly redundant, and ever-changing nature. To overcome the challenge, a few approaches have been proposed, which can be classified as manual, semi-automatic, or automatic design. This paper is intended to be the manifesto of the automatic off-line design for robot swarms.