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.

Advantages of Task-Specific Multi-Objective Optimisation in Evolutionary Robotics

The application of multi-objective optimisation to evolutionary robotics is receiving increasing attention. A survey of the literature reveals the different possibilities it offers to improve the automatic design of efficient and adaptive robotic systems, and points to the successful demonstrations available for both task-specific and task-agnostic approaches (i.e., with or without reference to the specific design problem to be tackled). However, the advantages of multi-objective approaches over single-objective ones have not been clearly spelled out and experimentally demonstrated. This paper fills this gap for task-specific approaches: starting from well-known results in multi-objective optimisation, we discuss how to tackle commonly recognised problems in evolutionary robotics. In particular, we show that multi-objective optimisation (i) allows evolving a more varied set of behaviours by exploring multiple trade-offs of the objectives to optimise, (ii) supports the evolution of the desired behaviour through the introduction of objectives as proxies, (iii) avoids the premature convergence to local optima possibly introduced by multi-component fitness functions, and (iv) solves the bootstrap problem exploiting ancillary objectives to guide evolution in the early phases. We present an experimental demonstration of these benefits in three different case studies: maze navigation in a single robot domain, flocking in a swarm robotics context, and a strictly collaborative task in collective robotics.

The Evolution of Cooperative Behaviours in Physically Heterogeneous Multi-Robot Systems

Evolutionary Robotics (ER) is a methodology that uses evolutionary computation to develop controllers for autonomous robots. The existing works focus on single robot systems or physically homogenous multi-robot teams, although physical heterogeneousness is more prevalent in the real world. It is therefore instructive to examine whether cooperative behaviours can be synthesized using artificial evolution for a team of physically heterogeneous robots. This paper makes an important contribution in answering the question of whether robots with distinct capabilities can synthesize their control strategies to accommodate their own capabilities without human intervention. We present an empirical analysis of the collaboration mechanisms and suggest guidelines about how to choose appropriate evolution methods. Simulated experiments with a team of e-puck robots show that evolution can lead to effective controllers for robots with distinct capabilities.