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We describe a new class of decentralised control algorithms that link local wireless connectivity to low-level robot motion control in order to maintain both swarm aggregation and connectivity, which we term “coherence”, in unbounded space. We investigate the potential of first-order and second-order connectivity information to maintain swarm coherence. For the second-order algorithm we show that a single \(\beta \) parameter—the number of shared neighbours that each robot tries to maintain—acts as an “adhesion” parameter. Control of \(\beta \) alone affects the global area coverage of the swarm. We then add a simple beacon sensor to each robot and show that, by creating a \(\beta \) differential between illuminated and occluded robots, the swarm displays emergent global taxis towards the beacon; it also displays interesting global obstacle avoidance properties. The chapter then extends the idea of \(\beta \) heterogeneity within the swarm to demonstrate variants of the algorithm that exhibit emergent concentric or linear segregation of subgroups within the swarm, or—in the presence of an external beacon—the formation of horizontal or vertical axial configurations. This emergent swarm morphology control is remarkable because apparently simple variations generate very different global properties. These emergent properties are interesting both because they appear to have parallels in biology, and because they could have value to a wide range of future applications in swarm robotics.

In the present study, we are interested in verifying how the progressive addition of constraints on communication and localization impact the performance of a swarm of small robots in shape formation tasks. Identified to be of importance in a swarm-user interaction context, the time required to construct a given spatial configuration is considered as a performance metric. The experimental work reported in this paper starts from global and synchronized localization information, shown to be successful both on a real hardware system and in simulation. In a second step, communication is constrained to a local scale, thus obliging a single designated robot to disseminate the global localization information to the other agents. The reliability of the radio communication channel and its impact upon the performance of the system are considered.

It is a characteristic of swarm robotics that modelling the overall swarm behaviour in terms of the low-level behaviours of individual robots is very difficult. Yet if swarm robotics is to make the transition from the laboratory to real-world engineering realisation such models would be critical for both overall validation of algorithm correctness and detailed parameter optimisation. We seek models with predictive power: models that allow us to determine the effect of modifying parameters in individual robots on the overall swarm behaviour. This paper presents results from a study to apply the probabilistic modelling approach to a class of wireless connected swarms operating in unbounded environments. The paper proposes a probabilistic finite state machine (PFSM) that describes the network connectivity and overall macroscopic behaviour of the swarm, then develops a novel robot-centric approach to the estimation of the state transition probabilities within the PFSM. Using measured data from simulation the paper then carefully validates the PFSM model step by step, allowing us to assess the accuracy and hence the utility of the model.

Swarm Intelligence provides us with a powerful new paradigm for building fully distributed decentralised systems in which overall system functionality emerges from the interaction of individual agents with each other and with their environment. Such systems are intrinsically highly parallel and can exhibit high levels of robustness and scalability; qualities desirable in high-integrity distributed systems. Making use of a laboratory based swarm robotic system as a case study, this review paper explores dependability, robustness and reliability modelling in swarm based systems, and argues that there is considerable merit in further investigating their application to distributed safety-critical systems.

This review paper sets out to explore the question of how future complex engineered systems based upon the swarm intelligence paradigm could be assured for dependability. The paper introduces the new concept of ‘swarm engineering': a fusion of dependable systems engineering and swarm intelligence. The paper reviews the disciplines and processes conventionally employed to assure the dependability of conventional complex (and safety critical) systems in the light of swarm intelligence research and in so doing tries to map processes of analysis, design and test for safety-critical systems against relevant research in swarm intelligence. A case study of a swarm robotic system is used to illustrate this mapping. The paper concludes that while some of the tools needed to assure a swarm for dependability exist, many do not, and hence much work needs to be done before dependable swarms become a reality.

The swarm intelligence literature frequently asserts that swarms exhibit high levels of robustness. That claim is, however, rather less frequently supported by empirical or theoretical analysis. But what do we mean by a 'robust' swarm? How would we measure the robustness or – to put it another way – fault-tolerance of a robotic swarm? These questions are not just of academic interest. If swarm robotics is to make the transition from the laboratory to real-world engineering implementation, we would need to be able to address these questions in a way that would satisfy the needs of the world of safety certification. This paper explores fault-tolerance in robot swarms through Failure Mode and Effect Analysis (FMEA) and reliability modelling. The work of this paper is illustrated by a case study of a wireless connected robot swarm, employing both simulation and real-robot laboratory experiments.