Ultrastable neuroendocrine robot controller

The work of a simulated neuroendocrine controller with ultrastable neurons and glands is sketched and tested in terms of stability and adaptability. The artificial neurons connect to each other and to motors, while hormones produced by behaviour-related glands regulate their output. The ultrastable nature of the cells allows them to maintain their homeostasis by random reconfiguration of their connections and parameters without reference to the global goal of the system. Interactions of these ultrastable components cause individual robot behaviours to emerge to certain extents. The pre- sented results show that the controller as a whole is capable of not only configuring itself to perform random walk, obstacle avoidance, mineral collection and recharging, but also to stay robust or adapt to a number of environmental and body perturbations without a need for a body model

Feature and performance comparison of the V-REP, Gazebo and ARGoS robot simulators

© Springer International Publishing AG, part of Springer Nature 2018. In this paper, the characteristics and performance of three open-source simulators for robotics, V-REP, Gazebo and ARGoS, are thoroughly analysed and compared. While they all allow for programming in C++, they also represent clear alternatives when it comes to the trade-off between complexity and performance. Attention is given to their built-in features, robot libraries, programming methods and the usability of their user interfaces. Benchmark test results are reported in order to identify how well the simulators can cope with environments of varying complexity. The richness of features of V-REP and the strong performance of Gazebo and ARGoS in complex scenes are highlighted. Various usability issues of Gazebo are also noted

Information Exchange Design Patterns for Robot Swarm Foraging and Their Application in Robot Control Algorithms

In swarm robotics, a design pattern provides high-level guidelines for the implementation of a particular robot behaviour and describes its impact on swarm performance. In this paper, we explore information exchange design patterns for robot swarm foraging. First, a method for the specification of design patterns for robot swarms is proposed that builds on previous work in this field and emphasises modular behaviour design, as well as information-centric micro-macro link analysis. Next, design pattern application rules that can facilitate the pattern usage in robot control algorithms are given. A catalogue of six design patterns is then presented. The patterns are derived from an extensive list of experiments reported in the swarm robotics literature, demonstrating the capability of the proposed method to identify distinguishing features of robot behaviour and their impact on swarm performance in a wide range of swarm implementations and experimental scenarios. Each pattern features a detailed description of robot behaviour and its associated parameters, facilitated by the usage of a multi-agent modeling language, BDRML, and an account of feedback loops and forces that affect the pattern's applicability. Scenarios in which the pattern has been used are described. The consequences of each design pattern on overall swarm performance are characterised within the Information-Cost-Reward framework, that makes it possible to formally relate the way in which robots acquire, share and utilise information. Finally, the patterns are validated by demonstrating how they improved the performance of foraging e-puck swarms and how they could guide algorithm design in other scenarios

Task Allocation in Foraging Robot Swarms:The Role of Information Sharing

Autonomous task allocation is a desirable feature of robot swarms that collect and deliver items in scenarios where congestion, caused by accumulated items or robots, can temporarily interfere with swarm behaviour. In such settings, self-regulation of workforce can prevent unnecessary energy consumption. We explore two types of self-regulation: non-social, where robots become idle upon experiencing congestion, and social, where robots broadcast information about congestion to their team mates in order to socially inhibit foraging. We show that while both types of self-regulation can lead to improved energy efficiency and increase the amount of resource collected, the speed with which information about congestion flows through a swarm affects the scalability of these algorithms

Controlling ant-based construction

This paper investigates the dynamics of decentralised nest construction in the ant species Leptothorax tuberointerruptus, exploring the contribution of, and interaction between, a pheromone building template and a physical building template (the bodies of the ants themselves). We present a continuous-space model of ant behaviour capable of generating ant-like nest structures, the integrity and shapes of which are non-trivially determined by choice of parameters and the building template(s) employed. We go on to demonstrate that the same behavioural algorithm is capable of generating a somewhat wider range of architectural forms, and discuss its limitations and potential extensions