Collective decision-making on triadic graphs

Many real-world networks exhibit community structures and non-trivial clustering associated with the occurrence of a considerable number of triangular subgraphs known as triadic motifs. Triads are a set of distinct triangles that do not share an edge with any other triangle in the network. Network motifs are subgraphs that occur significantly more often compared to random topologies. Two prominent examples, the feedforward loop and the feedback loop, occur in various real-world networks such as gene-regulatory networks, food webs or neuronal networks. However, as triangular connections are also prevalent in communication topologies of complex collective systems, it is worthwhile investigating the influence of triadic motifs on the collective decision-making dynamics. To this end, we generate networks called Triadic Graphs (TGs) exclusively from distinct triadic motifs. We then apply TGs as underlying topologies of systems with collective dynamics inspired from locust marching bands. We demonstrate that the motif type constituting the networks can have a paramount influence on group decision-making that cannot be explained solely in terms of the degree distribution. We find that, in contrast to the feedback loop, when the feedforward loop is the dominant subgraph, the resulting network is hierarchical and inhibits coherent behavior

Applying scale-invariant dynamics to improve consensus achievement of agents in motion

In order to efficiently execute tasks, autonomous collective systems are required to rapidly reach accurate consensus, no matter how the group is distributed over the environment. Finding consensus in a group of agents that are in motion is a particularly great challenge, especially at larger scales and extensive environments. Nevertheless, numerous collective systems in nature reach consensus independently of scale, i.e. they are scale-free or scale-invariant. Inspired by these natural phenomena, the aim of our work is to improve consensus achievement in artificial systems by finding fundamental links between individual decision-making and scale-free collective behavior. For model validation we use physics-based simulations as well as a swarm robotic testbed

Coherent collective behaviour emerging from decentralised balancing of social feedback and noise

Decentralised systems composed of a large number of locally interacting agents often rely on coherent behaviour to execute coordinated tasks. Agents cooperate to reach a coherent collective behaviour by aligning their individual behaviour to the one of their neighbours. However, system noise, determined by factors such as individual exploration or errors, hampers and reduces collective coherence. The possibility to overcome noise and reach collective coherence is determined by the strength of social feedback, i.e. the number of communication links. On the one hand, scarce social feedback may lead to a noise-driven system and consequently incoherent behaviour within the group. On the other hand, excessively strong social feedback may require unnecessary computing by individual agents and/or may nullify the possible benefits of noise. In this study, we investigate the delicate balance between social feedback and noise, and its relationship with collective coherence. We perform our analysis through a locust-inspired case study of coherently marching agents, modelling the binary collective decision-making problem of symmetry breaking. For this case study, we analytically approximate the minimal number of communication links necessary to attain maximum collective coherence. To validate our findings, we simulate a 500-robot swarm and obtain good agreement between theoretical results and physics-based simulations. We illustrate through simulation experiments how the robot swarm, using a decentralised algorithm, can adaptively reach coherence for various noise levels by regulating the number of communication links. Moreover, we show that when the system is disrupted by increasing and decreasing the robot density, the robot swarm adaptively responds to these changes in real time. This decentralised adaptive behaviour indicates that the derived relationship between social feedback, noise and coherence is robust and swarm size independent

Scheduling access to shared space in multi-robot systems

Through this study, we introduce the idea of applying scheduling techniques to allocate spatial resources that are shared among multiple robots moving in a static environment and having temporal constraints on the arrival time to destinations. To illustrate this idea, we present an exemplified algorithm that plans and assigns a motion path to each robot. The considered problem is particularly challenging because: (i) the robots share the same environment and thus the planner must take into account overlapping paths which cannot happen at the same time; (ii) there are time deadlines thus the planner must deal with temporal constraints; (iii) new requests arrive without a priori knowledge thus the planner must be able to add new paths online and adjust old plans; (iv) the robot motion is subject to noise thus the planner must be reactive to adapt to online changes. We showcase the functioning of the proposed algorithm through a set of agent-based simulations

Collective Lévy walk for efficient exploration in unknown environments

One of the key tasks of autonomous mobile robots is to explore the unknown environment under limited energy and deadline conditions. In this paper, we focus on one of the most efficient random walks found in the natural and biological system, i.e., Lévy walk. We show how Lévy properties disappear in larger robot swarm sizes because of spatial interferences and propose a novel behavioral algorithm to preserve Lévy properties at the collective level. Our initial findings hold potential to accelerate target search processes in large unknown environments by parallelizing Lévy exploration using a group of robots

The impact of interaction models on the coherence of collective decision-making : a case study with simulated locusts

A key aspect of collective systems resides in their ability to exhibit coherent behaviors, which demonstrate the system as a single unit. Such coherence is assumed to be robust under local interactions and high density of individuals. In this paper, we go beyond the local interactions and we investigate the coherence degree of a collective decision under different interaction models: (i)Â how this degree may get violated by massive loss of interaction links or high levels of individual noise, and (ii)Â how efficient each interaction model is in restoring a high degree of coherence. Our findings reveal that some of the interaction models facilitate a significant recovery of the coherence degree because their specific inter-connecting mechanisms lead to a better inference of the swarm opinion. Our results are validated using physics-based simulations of a locust robotic swarm

Construction task allocation through the collective perception of a dynamic environment

Building structures is a remarkable collective process but its automation remains an open challenge. Robot swarms provide a promising solution to this challenge. However, collective construction involves a number of difficulties regarding efficient robots allocation to the different activities, particularly if the goal is to reach an optimal construction rate. In this paper, we study an abstract construction scenario, where a swarm of robots is engaged in a collective perception process to estimate the density of building blocks around a construction site. The goal of this perception process is to maintain a minimum density of blocks available to the robots for construction. To maintain this density, the allocation of robots to the foraging task needs to be adjusted such that enough blocks are retrieved. Our results show a robust collective perception that enables the swarm to maintain a minimum block density under different rates of construction and foraging. Our approach leads the system to stabilize around a state in which the robots allocation allows the swarm to maintain a tile density that is close to or above the target minimum.info:eu-repo/semantics/publishedDorigo, M. Stützle, T. Blesa, M. J. Blum, C. Hamann, H. Heinrich, M. K. & Strobel, V. (2020). Swarm Intelligence: 12th International Conference, ANTS 2020, Barcelona, Spain, October 26-28, 2020, Proceedings. Cham: Springer International Publishing AG

Negative updating applied to the best-of-n problem with noisy qualities

The ability to perform well in the presence of noise is an important consideration when evaluating the effectiveness of a collective decision-making framework. Any system deployed for real-world applications will have to perform well in complex and uncertain environments, and a component of this is the limited reliability and accuracy of evidence sources. In particular, in swarm robotics there is an emphasis on small and inexpensive robots which are often equipped with low-cost sensors more prone to suffer from noisy readings. This paper presents an exploratory investigation into the robustness of a negative updating approach to the best-of-n problem which utilises negative feedback from direct pairwise comparison of options and opinion pooling. A site selection task is conducted with a small-scale swarm of five e-puck robots choosing between n= 7 options in a semi-virtual environment with varying levels of sensor noise. Simulation experiments are then used to investigate the scalability of the approach. We now vary the swarm size and observe the behaviour as the number of options n increases for different error levels with different pooling regimes. Preliminary results suggest that the approach is robust to noise in the form of noisy sensor readings for even small populations by supporting self-correction within the population