Self-organizing nervous systems for robot swarms.

We present the self-organizing nervous system (SoNS), a robot swarm architecture based on self-organized hierarchy. The SoNS approach enables robots to autonomously establish, maintain, and reconfigure dynamic multilevel system architectures. For example, a robot swarm consisting of independent robots could transform into a single -robot SoNS and then into several independent smaller SoNSs, where each SoNS uses a temporary and dynamic hierarchy.

Action-based confidence sharing and collective decision making.

Joint action research explores how multiple humans can coordinate their movements to achieve common goals. When there is uncertainty about the joint goal, individuals need to integrate their perceptual information of the environment to collaboratively determine their new goal. To ensure that a group reaches a consensus about the goal, collective decision making among the individuals is required.

A blockchain-based information market to incentivise cooperation in swarms of self-interested robots.

Robot swarms are generally considered to be composed of cooperative agents that, despite their limited individual capabilities, can perform difficult tasks by working together. However, in open swarms, where different robots can be added to the swarm by different parties with potentially competing interests, cooperation is but one of many strategies. We envision an information market where robots can buy and sell information through transactions stored on a distributed blockchain, and where cooperation is encouraged by the economy itself.

Robot swarms neutralize harmful Byzantine robots using a blockchain-based token economy.

Through cooperation, robot swarms can perform tasks or solve problems that a single robot from the swarm could not perform/solve by itself. However, it has been shown that a single Byzantine robot (such as a malfunctioning or malicious robot) can disrupt the coordination strategy of the entire swarm. Therefore, a versatile swarm robotics framework that addresses security issues in inter-robot communication and coordination is urgently needed.

Self-Reconfigurable Hierarchical Frameworks for Formation Control of Robot Swarms.

Hierarchical frameworks-a special class of directed frameworks with a layer-by-layer architecture-can be an effective mechanism to coordinate robot swarms. Their effectiveness was recently demonstrated by the mergeable nervous systems paradigm (Mathews et al., 2017), in which a robot swarm can switch dynamically between distributed and centralized control depending on the task, using self-organized hierarchical frameworks.

Learning from humans to build social cognition among robots.

Self-organized groups of robots have generally coordinated their behaviors using quite simple social interactions. Although simple interactions are sufficient for some group behaviors, future research needs to investigate more elaborate forms of coordination, such as social cognition, to progress towards real deployments. In this perspective, we define social cognition among robots as the combination of social inference, social learning, social influence, and knowledge transfer, and propose that these abilities can be established in robots by building underlying mechanisms based on behaviors observed in humans.

3D hybrid formation control of an underwater robot swarm: Switching topologies, unmeasurable velocities, and system constraints.

This paper addresses formation control of underactuated autonomous underwater vehicles in three-dimensional space, using a hybrid protocol that combines aspects of centralized and decentralized control with constraints that are particular to underwater vehicles, including switching topologies, unmeasurable velocities, and system constraints. Using a distributed leader-follower model, the hybrid formation protocol does not require velocity sensing, access to global information, or static and connected topologies. To handle switching jointly connected networks-that is, to tolerate temporary disconnections-a distributed observer is designed for followers to cooperatively estimate leader states using local measurements and local interactions.

Secure and secret cooperation in robot swarms.

The importance of swarm robotics systems in both academic research and real-world applications is steadily increasing. However, to reach widespread adoption, new models that ensure the secure cooperation of large groups of robots need to be developed. This work introduces a method to encapsulate cooperative robotic missions in an authenticated data structure known as a Merkle tree.

Blockchain Technology Secures Robot Swarms: A Comparison of Consensus Protocols and Their Resilience to Byzantine Robots.

Consensus achievement is a crucial capability for robot swarms, for example, for path selection, spatial aggregation, or collective sensing. However, the presence of malfunctioning and malicious robots (Byzantine robots) can make it impossible to achieve consensus using classical consensus protocols. In this work, we show how a swarm of robots can achieve consensus even in the presence of Byzantine robots by exploiting blockchain technology.

Language Evolution in Swarm Robotics: A Perspective.

While direct local communication is very important for the organization of robot swarms, so far it has mostly been used for relatively simple tasks such as signaling robots preferences or states. Inspired by the emergence of meaning found in natural languages, more complex communication skills could allow robot swarms to tackle novel situations in ways that may not be a priori obvious to the experimenter. This would pave the way for the design of robot swarms with higher autonomy and adaptivity.

Reflections on the future of swarm robotics.

Swarm robotics will tackle real-world applications by leveraging automatic design, heterogeneity, and hierarchical self-organization.

Publisher Correction: Mergeable nervous systems for robots.

The original version of this Article contained an error in the author contributions section, whereby credit for design of the experiments was not attributed to N.M. This error has now been corrected in both the PDF and HTML versions of the Article.

Yield prediction in parallel homogeneous assembly.

We investigate the parallel assembly of two-dimensional, geometrically-closed modular target structures out of homogeneous sets of macroscopic components of varying anisotropy. The yield predicted by a chemical reaction network (CRN)-based model is quantitatively shown to reproduce experimental results over a large set of conditions. Scaling laws for parallel assembling systems are then derived from the model.

Mergeable nervous systems for robots.

Robots have the potential to display a higher degree of lifetime morphological adaptation than natural organisms. By adopting a modular approach, robots with different capabilities, shapes, and sizes could, in theory, construct and reconfigure themselves as required. However, current modular robots have only been able to display a limited range of hardwired behaviors because they rely solely on distributed control.

The k -Unanimity Rule for Self-Organized Decision-Making in Swarms of Robots.

In this paper, we propose a collective decision-making method for swarms of robots. The method enables a robot swarm to select, from a set of possible actions, the one that has the fastest mean execution time. By means of positive feedback the method achieves consensus on the fastest action.

A Design Pattern for Decentralised Decision Making.

The engineering of large-scale decentralised systems requires sound methodologies to guarantee the attainment of the desired macroscopic system-level behaviour given the microscopic individual-level implementation. While a general-purpose methodology is currently out of reach, specific solutions can be given to broad classes of problems by means of well-conceived design patterns. We propose a design pattern for collective decision making grounded on experimental/theoretical studies of the nest-site selection behaviour observed in honeybee swarms (Apis mellifera).

Evolution of Self-Organized Task Specialization in Robot Swarms.

Division of labor is ubiquitous in biological systems, as evidenced by various forms of complex task specialization observed in both animal societies and multicellular organisms. Although clearly adaptive, the way in which division of labor first evolved remains enigmatic, as it requires the simultaneous co-occurrence of several complex traits to achieve the required degree of coordination. Recently, evolutionary swarm robotics has emerged as an excellent test bed to study the evolution of coordinated group-level behavior.

Task partitioning in a robot swarm: object retrieval as a sequence of subtasks with direct object transfer.

We study task partitioning in the context of swarm robotics. Task partitioning is the decomposition of a task into subtasks that can be tackled by different workers. We focus on the case in which a task is partitioned into a sequence of subtasks that must be executed in a certain order.

Elasticity-based mechanism for the collective motion of self-propelled particles with springlike interactions: a model system for natural and artificial swarms.

We introduce an elasticity-based mechanism that drives active particles to self-organize by cascading self-propulsion energy towards lower-energy modes. We illustrate it on a simple model of self-propelled agents linked by linear springs that reach a collectively rotating or translating state without requiring aligning interactions. We develop an active elastic sheet theory, complementary to the prevailing active fluid theories, and find analytical stability conditions for the ordered state.

Self-organized discrimination of resources.

When selecting a resource to exploit, an insect colony must take into account at least two constraints: the resource must be abundant enough to sustain the whole group, but not too large to limit exploitation costs, and risks of conflicts with other colonies. Following recent results on cockroaches and ants, we introduce here a behavioral mechanism that satisfies these two constraints. Individuals simply modulate their probability to switch to another resource as a function of the local density of conspecifics locally detected.

Incremental social learning in particle swarms.

Incremental social learning (ISL) was proposed as a way to improve the scalability of systems composed of multiple learning agents. In this paper, we show that ISL can be very useful to improve the performance of population-based optimization algorithms. Our study focuses on two particle swarm optimization (PSO) algorithms: a) the incremental particle swarm optimizer (IPSO), which is a PSO algorithm with a growing population size in which the initial position of new particles is biased toward the best-so-far solution, and b) the incremental particle swarm optimizer with local search (IPSOLS), in which solutions are further improved through a local search procedure.

Artificial pheromone for path selection by a foraging swarm of robots.

Foraging robots involved in a search and retrieval task may create paths to navigate faster in their environment. In this context, a swarm of robots that has found several resources and created different paths may benefit strongly from path selection. Path selection enhances the foraging behavior by allowing the swarm to focus on the most profitable resource with the possibility for unused robots to stop participating in the path maintenance and to switch to another task.

Evolving self-assembly in autonomous homogeneous robots: experiments with two physical robots.

This research work illustrates an approach to the design of controllers for self-assembling robots in which the self-assembly is initiated and regulated by perceptual cues that are brought forth by the physical robots through their dynamical interactions. More specifically, we present a homogeneous control system that can achieve assembly between two modules (two fully autonomous robots) of a mobile self-reconfigurable system without a priori introduced behavioral or morphological heterogeneities. The controllers are dynamic neural networks evolved in simulation that directly control all the actuators of the two robots.

An Open Localization and Local Communication Embodied Sensor.

In this paper we describe a localization and local communication system which allows situated agents to communicate locally, obtaining at the same time both the range and the bearing of the emitter without the need of any centralized control or any external reference. The system relies on infrared communications with frequency modulation and is composed of two interconnected modules for data and power measurement. Thanks to the open hardware license under which it is released, the research community can easily replicate the system at a low cost and/or adapt it for applications in sensor networks and in robotics.