Swarm robotics is an approach to collective robotics that takes inspiration from the self-organized behaviors of social animals. Through simple rules and local interactions, swarm robotics aims at designing robust, scalable, and flexible collective behaviors for the coordination of large numbers of robots. In this paper, we analyze the literature from the point of view of swarm engineering: we focus mainly on ideas and concepts that contribute to the advancement of swarm robotics as an engineering field and that could be relevant to tackle real-world applications. Swarm engineering is an emerging discipline that aims at defining systematic and well founded procedures for modeling, designing, realizing, verifying, validating, operating, and maintaining a swarm robotics system. We propose two taxonomies: in the first taxonomy, we classify works that deal with design and analysis methods; in the second taxonomy, we classify works according to the collective behavior studied. We conclude with a discussion of the current limits of swarm robotics as an engineering discipline and with suggestions for future research directions.

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Swarm robotics: a review from the swarm engineering perspective

Software systems dealing with distributed applications in changing environments normally require human supervision to continue operation in all conditions. These (re-)configuring, troubleshooting, and in general maintenance tasks lead to costly and time-consuming procedures during the operating phase. These problems are primarily due to the open-loop structure often followed in software development. Therefore, there is a high demand for management complexity reduction, management automation, robustness, and achieving all of the desired quality requirements within a reasonable cost and time range during operation. Self-adaptive software is a response to these demands; it is a closed-loop system with a feedback loop aiming to adjust itself to changes during its operation. These changes may stem from the software system's self (internal causes, e.g., failure) or context (external events, e.g., increasing requests from users). Such a system is required to monitor itself and its context, detect significant changes, decide how to react, and act to execute such decisions. These processes depend on adaptation properties (called self-a properties), domain characteristics (context information or models), and preferences of stakeholders. Noting these requirements, it is widely believed that new models and frameworks are needed to design self-adaptive software. This survey article presents a taxonomy, based on concerns of adaptation, that is, how, what, when and where, towards providing a unified view of this emerging area. Moreover, as adaptive systems are encountered in many disciplines, it is imperative to learn from the theories and models developed in these other areas. This survey article presents a landscape of research in self-adaptive software by highlighting relevant disciplines and some prominent research projects. This landscape helps to identify the underlying research gaps and elaborates on the corresponding challenges.

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Self-adaptive software: Landscape and research challenges

Self-adaptation is typically realized using a control loop. One prominent approach for organizing a control loop in self-adaptive systems is by means of four components that are responsible for the primary functions of self-adaptation: Monitor, Analyze, Plan, and Execute, together forming a MAPE loop. When systems are large, complex, and heterogeneous, a single MAPE loop may not be sufficient for managing all adaptation in a system, so multiple MAPE loops may be introduced. In self-adaptive systems with multiple MAPE loops, decisions about how to decentralize each of the MAPE functions must be made. These decisions involve how and whether the corresponding functions from multiple loops are to be coordinated (e.g., planning components coordinating to prepare a plan for an adaptation). To foster comprehension of self-adaptive systems with multiple MAPE loops and support reuse of known solutions, it is crucial that we document common design approaches for engineers. As such systematic knowledge is currently lacking, it is timely to reflect on these systems to: (a) consolidate the knowledge in this area, and (b) to develop a systematic approach for describing different types of control in self-adaptive systems. We contribute with a simple notation for describing interacting MAPE loops, which we believe helps in achieving (b), and we use this notation to describe a number of existing patterns of interacting MAPE loops, to begin to fulfill (a). From our study, we outline numerous remaining research challenges in this area.

https://homepage.lnu.se/staff/daweaa/papers/2012SefSAS.pdf

On Patterns for Decentralized Control in Self-Adaptive Systems

Recent advances in technology are delivering robots of reduced size and cost. A natural outgrowth of these advances are systems comprised of large numbers of robots that collaborate autonomously in diverse applications. Research on effective autonomous control of such systems, commonly called swarms, has increased dramatically in recent years and received attention from many domains, such as bioinspired robotics and control theory. These kinds of distributed systems present novel challenges for the effective integration of human supervisors, operators, and teammates that are only beginning to be addressed. This paper is the first survey of human–swarm interaction (HSI) and identifies the core concepts needed to design a human–swarm system. We first present the basics of swarm robotics. Then, we introduce HSI from the perspective of a human operator by discussing the cognitive complexity of solving tasks with swarm systems. Next, we introduce the interface between swarm and operator and identify challenges and solutions relating to human–swarm communication, state estimation and visualization, and human control of swarms. For the latter, we develop a taxonomy of control methods that enable operators to control swarms effectively. Finally, we synthesize the results to highlight remaining challenges, unanswered questions, and open problems for HSI, as well as how to address them in future works.

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Human Interaction With Robot Swarms: A Survey

Recent developments in information technology have brought about important changes in distributed computing. New environments such as massively large-scale, wide-area computer networks and mobile ad hoc networks have emerged. Common characteristics of these environments include extreme dynamicity, unreliability, and large scale. Traditional approaches to designing distributed applications in these environments based on central control, small scale, or strong reliability assumptions are not suitable for exploiting their enormous potential. Based on the observation that living organisms can effectively organize large numbers of unreliable and dynamically-changing components (cells, molecules, individuals, etc.) into robust and adaptive structures, it has long been a research challenge to characterize the key ideas and mechanisms that make biological systems work and to apply them to distributed systems engineering. In this article we propose a conceptual framework that captures several basic biological processes in the form of a family of design patterns. Examples include plain diffusion, replication, chemotaxis, and stigmergy. We show through examples how to implement important functions for distributed computing based on these patterns. Using a common evaluation methodology, we show that our bio-inspired solutions have performance comparable to traditional, state-of-the-art solutions while they inherit desirable properties of biological systems including adaptivity and robustness.

Design Patterns from Biology for Distributed Computing

Mobile ad hoc networks are communication networks built up of a collection of mobile devices, which can communicate through wireless connections. Routing is the task of directing data packets from a source node to a given destination. This task is particularly hard in mobile ad hoc networks: due to the mobility of the network elements and the lack of central control, routing algorithms should be robust, adaptive, and work in a decentralized and self-organizing way. In this paper, we describe an algorithm, which draws inspiration from swarm intelligence to obtain these characteristics. More specifically, we borrow ideas from ant colonies and from the ant colony optimization framework. In an extensive set of simulation tests, we compare our routing algorithm with a state-of-the-art algorithm, and show that it gets better performance over a wide range of different scenarios and for a number of different evaluation measures. In particular, we show that it scales better with the number of nodes in the network.

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Swarm intelligence for routing in mobile ad hoc networks

In this paper we describe AntHocNet, an algorithm for routing in mobile ad hoc networks based on ideas from the nature-inspired ant colony optimization framework. The algorithm consists of both reactive and proactive components. In a reactive path setup phase, multiple paths are built between the source and destination of a data session. Data are stochastically spread over the different paths, according to their estimated quality. During the course of the session, paths are continuously monitored and improved in a proactive way. Link failures are dealt with locally. The algorithm makes extensive use of ant-like mobile agents which sample full paths between source and destination nodes in a Monte Carlo fashion. We report results of simulation experiments in which we have studied the behavior of AntHocNet and AODV as a function of node mobility, terrain size and number of nodes. According to the observed results, AntHocNet outperforms AODV both in terms of end-to-end delay and delivery ratio.

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Ant agents for hybrid multipath routing in mobile ad hoc networks

The purpose of the demonstrator is to present a novel system for gesture-based interaction between humans and a swarm of mobile robots. The human interacts with the swarm by showing hand gestures using an orange glove. Following initial hand glove detection, the robots move to adapt their positions and viewpoints. The purpose is to improve individual sensing performance and maximize the gesture information mutually gathered by the swarm as a whole. Using multi-hop message relaying, robots spread their opinions and the associated confidence about the issued hand gesture throughout the swarm. To let the robots in the swarm integrate and weight the different opinions, we developed a distributed consensus protocol. When a robot has gathered enough evidence, it takes a decision for the hand gesture, and sends it into the swarm. Different decisions compete with each other. The one assessed with the highest confidence eventually wins. When consensus is reached about the hand gesture, the swarm acts accordingly, for example by moving to a location, or splitting into groups. The working of the system is shown and explained in the video accessible at the following address:http://www.idsia.ch/ gianni/SwarmRobotics/aamasdemo.zip.

Distributed consensus for interaction between humans and mobile robot swarms

We study through simulation the performance of two swarm intelligence MANET routing algorithms in a realistic urban environment. The two algorithms, ANSI and AntHocNet, implement the swarm intelligence paradigm for routing in different ways: while ANSI applies a reactive approach in which ants are only sent out when no route is available between the source and destination of a communication session, AntHocNet integrates reactive and proactive mechanisms whereby the algorithm sends out ants at regular intervals during the entire duration of running sessions in order to continuously adapt and improve existing routes. The two swarm intelligence routing algorithms are compared to AODV, a state-of-the-art reactive algorithm, and OLSR, a state-of-the-art proactive algorithm. Our objective is to investigate the usefulness of the different approaches adopted by the algorithms when confronted with the peculiarities of urban environments and the requirements of real-world applications. At this aim we define a detailed and realistic simulation setup. We model node mobility by limiting node movements to the streets and open spaces of town, use a ray-tracing approach to model the propagation of radio waves, and investigate different kinds of interactive data traffic patterns, ranging from SMS messaging to VoIP communications.

G. A. Di Caro

Papers

Design Patterns from Biology for Distributed Computing

Swarm intelligence for routing in mobile ad hoc networks

Ant agents for hybrid multipath routing in mobile ad hoc networks

Distributed consensus for interaction between humans and mobile robot swarms

An evaluation of two swarm intelligence MANET routing algorithms in an urban environment