Social learning in swarm robotics

In this paper, we present an implementation of social learning for swarm robotics. We consider social learning as a distributed online reinforcement learning method applied to a collective of robots where sensing, acting and coordination are performed on a local basis. While some issues are specific to artificial systems, such as the general objective of learning efficient (and ideally, optimal) behavioural strategies to fulfill a task defined by a supervisor, some other issues are shared with social learning in natural systems. We discuss some of these issues, paving the way towards cumulative cultural evolution in robot swarms, which could enable complex social organization necessary to achieve challenging robotic tasks. This article is part of a discussion meeting issue ‘The emergence of collective knowledge and cumulative culture in animals, humans and machines’.

Velocity Obstacles and Emergent Rules-of-the-Air for Autonomous Drone Traffic Management

As the use of unmanned aerial vehicles (UAVs) becomes ever more widespread there is a growing need to develop traffic management and flight rules, in particular for autonomous UAVs or where the predicted traffic densities far exceed those of traditional manned aviation. Inspired by the current rules of the air and multi-agent systems (e.g., pedestrians and swarm robotics) we outline a set of flight rules for autonomous UAVs that consist of waypoint following and conflict avoidance schemes. These flight rules are then explored in small,pairwise simulations and thus refined to allow a UAV to choose from three potential avoidance behaviors based on it and its neighbors velocities and positions. Finally we compare the original and modified flight rules in larger scale simulations modelling two streams of UAV traffic crossing at a point. We show that the modified rules significantly reduce the mean transit time by reducing the impact of UAVs avoiding other UAVs from the same stream.

Velocity Obstacles and Emergent Rules-of-the-Air for Autonomous Drone Traffic Management

As the use of unmanned aerial vehicles (UAVs) becomes ever more widespread there is a growing need to develop traffic management and flight rules, in particular for autonomous UAVs or where the predicted traffic densities far exceed those of traditional manned aviation. Inspired by the current rules of the air and multi-agent systems (e.g., pedestrians and swarm robotics) we outline a set of flight rules for autonomous UAVs that consist of waypoint following and conflict avoidance schemes. These flight rules are then explored in small,pairwise simulations and thus refined to allow a UAV to choose from three potential avoidance behaviors based on it and its neighbors velocities and positions. Finally we compare the original and modified flight rules in larger scale simulations modelling two streams of UAV traffic crossing at a point. We show that the modified rules significantly reduce the mean transit time by reducing the impact of UAVs avoiding other UAVs from the same stream.

Intelligent Control of Swarm Robotics Employing Biomimetic Deep Learning

The collective motion of biological species has robust and flexible characteristics. Since the individual of the biological group interacts with other neighbors asymmetrically, which means the pairwise interaction presents asymmetrical characteristics during the collective motion, building the model of the pairwise interaction of the individual is still full of challenges. Based on deep learning (DL) technology, experimental data of the collective motion on Hemigrammus rhodostomus fish are analyzed to build an individual interaction model with multi-parameter input. First, a Deep Neural Network (DNN) structure for pairwise interaction is designed. Then, the interaction model is obtained by means of DNN proper training. We propose a novel key neighbor selection strategy, which is called the Largest Visual Pressure Selection (LVPS) method, to deal with multi-neighbor interaction. Based on the information of the key neighbor identified by LVPS, the individual uses the properly trained DNN model for the pairwise interaction. Compared with other key neighbor selection strategies, the statistical properties of the collective motion simulated by our proposed DNN model are more consistent with those of fish experiments. The simulation shows that our proposed method can extend to large-scale group collective motion for aggregation control. Thereby, the individual can take advantage of quite limited local information to collaboratively achieve large-scale collective motion. Finally, we demonstrate swarm robotics collective motion in an experimental platform. The proposed control method is simple to use, applicable for different scales, and fast for calculation. Thus, it has broad application prospects in the fields of multi-robotics control, intelligent transportation systems, saturated cluster attacks, and multi-agent logistics, among other fields.