Towards adaptive multi-robot systems: self-organization and self-adaptation

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.The development of complex systems ensembles that operate in uncertain environments is a major challenge. The reason for this is that system designers are not able to fully specify the system during specification and development and before it is being deployed. Natural swarm systems enjoy similar characteristics, yet, being self-adaptive and being able to self-organize, these systems show beneficial emergent behaviour. Similar concepts can be extremely helpful for artificial systems, especially when it comes to multi-robot scenarios, which require such solution in order to be applicable to highly uncertain real world application. In this article, we present a comprehensive overview over state-of-the-art solutions in emergent systems, self-organization, self-adaptation, and robotics. We discuss these approaches in the light of a framework for multi-robot systems and identify similarities, differences missing links and open gaps that have to be addressed in order to make this framework possible

Swarmodroid 1.0: A Modular Bristle-Bot Platform for Robotic Active Matter Studies

Large swarms of extremely simple robots (i.e., capable just of basic motion activities, like propelling forward or self-rotating) are widely applied to study collective task performance based on self-organization or local algorithms instead of sophisticated programming and global swarm coordination. Moreover, they represent a versatile yet affordable platform for experimental studies in physics, particularly in active matter - non-equilibrium assemblies of particles converting their energy to a directed motion. However, a large set of robotics platforms is being used in different studies, while the universal design is still lacking. Despite such platforms possess advantages in certain application scenarios, their large number sufficiently limits further development of results in the field, as advancing some study requires to buy or manually produce the corresponding robots. To address this issue, we develop an open-source Swarmodroid 1.0 platform based on bristle-bots with reconfigurable 3D-printed bodies, external control of motion velocity, and basic capabilities of velocity profile programming. In addition, we introduce AMPy software package in Python featuring OpenCV-based extraction of robotic swarm kinematics accompanied by the evaluation of key physical quantities describing the collective dynamics. We perform a detailed analysis of individual Swarmodroids' motion characteristics and address their use cases with two examples: a cargo transport performed by self-rotating robots and a velocity-dependent jam formation in a bottleneck by self-propelling robots. Finally, we provide a comparison of existing centimeter-scale robotic platforms, a review of key quantities describing collective dynamics of many-particle systems, and a comprehensive outlook considering potential applications as well as further directions for fundamental studies and Swarmodroid 1.0 platform development.Comment: 18 pages, 7 figures, 1 table + Supplementary Information. Comments are welcom

Swarm robotics in wireless distributed protocol design for coordinating robots involved in cooperative tasks

The mine detection in an unexplored area is an optimization problem where multiple mines, randomly distributed throughout an area, need to be discovered and disarmed in a minimum amount of time. We propose a strategy to explore an unknown area, using a stigmergy approach based on ants behavior, and a novel swarm based protocol to recruit and coordinate robots for disarming the mines cooperatively. Simulation tests are presented to show the effectiveness of our proposed Ant-based Task Robot Coordination (ATRC) with only the exploration task and with both exploration and recruiting strategies. Multiple minimization objectives have been considered: the robots' recruiting time and the overall area exploration time. We discuss, through simulation, different cases under different network and field conditions, performed by the robots. The results have shown that the proposed decentralized approaches enable the swarm of robots to perform cooperative tasks intelligently without any central control

A macroscopic analytical model of collaboration in distributed robotic systems

In this article, we present a macroscopic analytical model of collaboration in a group of reactive robots. The model consists of a series of coupled differential equations that describe the dynamics of group behavior. After presenting the general model, we analyze in detail a case study of collaboration, the stick-pulling experiment, studied experimentally and in simulation by Ijspeert et al. [Autonomous Robots, 11, 149-171]. The robots' task is to pull sticks out of their holes, and it can be successfully achieved only through the collaboration of two robots. There is no explicit communication or coordination between the robots. Unlike microscopic simulations (sensor-based or using a probabilistic numerical model), in which computational time scales with the robot group size, the macroscopic model is computationally efficient, because its solutions are independent of robot group size. Analysis reproduces several qualitative conclusions of Ijspeert et al.: namely, the different dynamical regimes for different values of the ratio of robots to sticks, the existence of optimal control parameters that maximize system performance as a function of group size, and the transition from superlinear to sublinear performance as the number of robots is increased

Fault Recovery in Swarm Robotics Systems using Learning Algorithms

When faults occur in swarm robotic systems they can have a detrimental effect on collective behaviours, to the point that failed individuals may jeopardise the swarm's ability to complete its task. Although fault tolerance is a desirable property of swarm robotic systems, fault recovery mechanisms have not yet been thoroughly explored. Individual robots may suffer a variety of faults, which will affect collective behaviours in different ways, therefore a recovery process is required that can cope with many different failure scenarios. In this thesis, we propose a novel approach for fault recovery in robot swarms that uses Reinforcement Learning and Self-Organising Maps to select the most appropriate recovery strategy for any given scenario. The learning process is evaluated in both centralised and distributed settings. Additionally, we experimentally evaluate the performance of this approach in comparison to random selection of fault recovery strategies, using simulated collective phototaxis, aggregation and foraging tasks as case studies. Our results show that this machine learning approach outperforms random selection, and allows swarm robotic systems to recover from faults that would otherwise prevent the swarm from completing its mission. This work builds upon existing research in fault detection and diagnosis in robot swarms, with the aim of creating a fully fault-tolerant swarm capable of long-term autonomy

New Models of Self-Organized Multi-Robot Clustering

For self-organized multi-robot systems, one of the widely studied task domains is object clustering, which involves gathering randomly scattered objects into a single pile. Earlier studies have pointed out that environment boundaries influence the cluster formation process, generally causing clusters to form around the perimeter rather than centrally within the workspace. Nevertheless, prior analytical models ignore boundary effects and employ the simplifying assumption that clusters pack into rotationally symmetric forms. In this study, we attempt to solve the problem of the boundary interference in object clustering. We propose new behaviors, twisting and digging, which exploit the geometry of the object to detach objects from the boundaries and cover different regions within the workplace. Also, we derive a set of conditions that is required to prevent boundaries causing perimeter clusters, developing a mathematical model to explain how multiple clusters evolve into a single cluster. Through analysis of the model, we show that the time-averaged spatial densities of the robots play a significant role in producing conditions which ensure that a single central cluster emerges and validate it with experiments. We further seek to understand the clustering process more broadly by investigating the problem of clustering in settings involving different object geometries. We initiate a study of this important area by considering a variety of rectangular objects that produce diverse shapes according to different packing arrangements. In addition, on the basis of the observation that cluster shape reflects object geometry, we develop cluster models that describe clustering dynamics across different object geometries. Also, we attempt to address the question of how to maximize the system performance by computing a policy for altering the robot division of labor as a function of time. We consider a sequencing strategy based on the hypothesis that since the clustering performance is influenced by the division of labor, it can be improved by sequencing different divisions of labor. We develop a stochastic model to predict clustering behavior and propose a method that uses the model's predictions to select a sequential change in labor distribution. We validate our proposed method that increases clustering performance on physical robot experiments

Devobot: From Biological Morphogenesis to Morphogenetic Swarm Robotics

Complex systems are composed of a large number of relatively simple entities interacting with each other and their environment. From those entities and interactions emerge new and often unpredictable collective structures. Complex systems are widely present in nature, from cells and living organisms to human societies. A major biological process behind this emergence in natural complex systems is morphogenesis, which refers mainly, although not exclusively, to shape development in multicellular organisms. Inspired by morphogenesis, the field of Morphogenetic Engineering (ME) aims to design a system’s global architecture and behaviour in a bottom-up fashion from the self-organisation of a myriad of small components. In particular, Morphogenetic Robotics (MR) strives to apply ME to Swarm Robotics in order to create robot collectives exhibiting morphogenetic properties. While most MR works focus on small and cheap hardware, such as Kilobots, only few or them investigate swarms of mobile and more “intelligent” robot models. In this thesis, we present two original works involving higher-end MR swarms based on the PsiSwarm platform, a two-wheeled saucer-size robot running the Mbed operating system. First, we describe a novel distributed algorithm capable of growing a densely packed “multi-robot organism” out of a group of 40 PsiSwarms, based on ME principles. Then, in another study closer to Modular Robotics (MoR), and taking inspiration from “programmable network growth”, we demonstrate the self-organisation of (virtual) branched structures among a flock of robots. Both works use MORSE, a realistic simulation tool, while a path toward crossing the “reality gap” is shown by preliminary experiments conducted using real hardware