Improving social odometry robot networks with distributed reputation systems for collaborative purposes

Autofocus Entropy Repositioning Method Bioinspired in the Magnetic Field Memory of the Bees Applied to Pollination

In this paper, a bioinspired method in the magnetic field memory of the bees, applied in a rover of precision pollination, is presented. The method calculates sharpness features by entropy and variance of the Laplacian of images segmented by color in the HSV system in real-time. A complementary positioning method based on area feature extraction between active markers was developed, analyzing color characteristics, noise, and vibrations of the probe in time and frequency, through the lateral image of the probe. From the observed results, it can be seen that the unsupervised method does not require previous calibration of target dimensions, histogram, and distances involved in positioning. The algorithm showed less sensitivity in the extraction of sharpness characteristics regarding the number of edges and greater sensitivity to the gradient, allowing unforeseen operation scenarios, even in small sharpness variations, and robust response to variance local, temporal, and geophysical of the magnetic declination, not needing luminosity after scanning, with the two freedom of degrees of the rotation.

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.