When less is more: Robot swarms adapt better to changes with constrained communication

To effectively perform collective monitoring of dynamic environments, a robot swarm needs to adapt to changes by processing the latest information and discarding outdated beliefs. We show that in a swarm composed of robots relying on local sensing, adaptation is better achieved if the robots have a shorter rather than longer communication range. This result is in contrast with the widespread belief that more communication links always improve the information exchange on a network. We tasked robots with reaching agreement on the best option currently available in their operating environment. We propose a variety of behaviors composed of reactive rules to process environmental and social information. Our study focuses on simple behaviors based on the voter model—a well-known minimal protocol to regulate social interactions—that can be implemented in minimalistic machines. Although different from each other, all behaviors confirm the general result: The ability of the swarm to adapt improves when robots have fewer communication links. The average number of links per robot reduces when the individual communication range or the robot density decreases. The analysis of the swarm dynamics via mean-field models suggests that our results generalize to other systems based on the voter model. Model predictions are confirmed by results of multiagent simulations and experiments with 50 Kilobot robots. Limiting the communication to a local neighborhood is a cheap decentralized solution to allow robot swarms to adapt to previously unknown information that is locally observed by a minority of the robots.

Minimization of Fuel Consumption of A Swarm Of Spacecraft Through A Genetic Algorithm Approach

As humanity moves closer to forming realis-tic paths toward space exploration beyond that what we have already accomplished, multiple new chal-lenges have presented themselves. Traditional large spacecraft prove to be unfeasible both logistically and economically for missions where a single prob-lem can completely halt operations, especially given that higher reward missions are also of higher risk. A possible alternative to large craft is using a swarm of smaller craft made to accomplish the same goals while mitigating some of the drawbacks large craft face. Rockets, space shuttles, and satellites all prove to be too large to navigate areas of space dense with obstacles. Smaller craft on the scale of one meter in a large swarm would navigate these regions. Due to the decentralized nature of a swarm, any problems faced by one craft do not necessarily affect the oth-ers, allowing the swarm to stay operational despite some crafts becoming compromised. This feature means that a problem or miscalculation that could completely derail an entire mission in the context of a large spacecraft would not do the same to a swarm. In the context of exploring dense and/or extreme en-vironments in space, many logistic and economic problems faced by large craft due to their size and centralized nature will not affect a swarm. With an ac-curate mathematical model of the swarm dynamics from Benet et al.[1], a genetic algorithm’s metaheuristic method is utilized[2] to find optimal pa-rameters that yield a minimal fuel consumption value for a given trajectory/mission objective. From this approach, the total fuel consumption was cut in half while retaining desirable characteristics of the trajec-tory such as collision avoidance and final formation constraints, giving us a similar course that accom-plishes the same goal of transporting craft around objects and disturbances while also minimizing eco-nomic losses.

Mutual anticipation can contribute to self-organization in human crowds

Human crowds provide paradigmatic examples of collective behavior emerging through self-organization. Understanding their dynamics is crucial to help manage mass events and daily pedestrian transportation. Although recent findings emphasized that pedestrians’ interactions are fundamentally anticipatory in nature, whether and how individual anticipation functionally benefits the group is not well understood. Here, we show the link between individual anticipation and emergent pattern formation through our experiments of lane formation, where unidirectional lanes are spontaneously formed in bidirectional pedestrian flows. Manipulating the anticipatory abilities of some of the pedestrians by distracting them visually delayed the collective pattern formation. Moreover, both the distracted pedestrians and the nondistracted ones had difficulties avoiding collisions while navigating. These results imply that avoidance maneuvers are normally a cooperative process and that mutual anticipation between pedestrians facilitates efficient pattern formation. Our findings may influence various fields, including traffic management, decision-making research, and swarm dynamics.

Brownian Swarm Dynamics and Burgers’ Equation with Higher Order Dispersion

The concept of ranked order probability distribution unveils natural probabilistic interpretations for the kink waves (and hence the solitons) solving higher order dispersive Burgers’ type PDEs. Thanks to this underlying structure, it is possible to propose a systematic derivation of exact solutions for PDEs with a quadratic nonlinearity of the Burgers’ type but with arbitrary dispersive orders. As illustrations, we revisit the dissipative Kotrweg de Vries, Kuramoto-Sivashinski, and Kawahara equations (involving third, fourth, and fifth order dispersion dynamics), which in this context appear to be nothing but the simplest special cases of this infinitely rich class of nonlinear evolutions.

Cherry Picking: Consumer Choices in Swarm Dynamics, Considering Price and Quality of Goods

This paper proposes a further development of the mathematical theory of swarms to behavioral dynamics of social and economic systems, with an application to the modeling of price series in a market. The complexity features of the system are properly described by modeling the asymmetric interactions between buyers and sellers, specifically considering the so-called cherry picking phenomenon, by which not only prices but also qualities are considered when buying a good. Finally, numerical simulations are performed to depict the predictive ability of the model and to show interesting emerging behaviors, as the coordination of buyers and their division in endogenous clusters.

Orthogonal Learning Firefly Algorithm

The primary aim of this original work is to provide a more in-depth insight into the relations between control parameters adjustments, learning techniques, inner swarm dynamics and possible hybridization strategies for popular swarm metaheuristic Firefly Algorithm (FA). In this paper, a proven method, orthogonal learning, is fused with FA, specifically with its hybrid modification Firefly Particle Swarm Optimization (FFPSO). The parameters of the proposed Orthogonal Learning Firefly Algorithm are also initially thoroughly explored and tuned. The performance of the developed algorithm is examined and compared with canonical FA and above-mentioned FFPSO. Comparisons have been conducted on well-known CEC 2017 benchmark functions, and the results have been evaluated for statistical significance using the Friedman rank test.

Adaptive Swarm Coordination and Formation Control

Swarm coordination and formation control designs focus on multi-agent dynamic system behavior and aim to achieve desired coordinated behavior or predefined geometric shape. They utilize techniques from the control theory and graph theory literature. On the other hand, adaptive control theory is concerned with uncertainties in the system dynamics, and has structured frameworks for various types of plant models. Therefore, in case there are uncertainties in the swarm dynamics, adaptive control methodologies can be utilized to achieve the desired coordinated behavior and there exist remarkable works in this direction. However, connection among swarm coordination, formation control, and adaptive control theory brings some restrictions as well as advantages. Hence adaptive swarm coordination and formation control has been developed in limited aspects. In this chapter, we review some existing works of the adaptive formation control literature along with non-adaptive ones, and discuss the advantages of application of adaptive control frameworks to swarm coordination and formation control.

Formulas

Under the term formulas, this chapter investigates complementary strategies in order to describe the dynamics and functions of biological collectives. It examines how, on the basis of patchy empirical data, attempts were made to construct mathematical models concerned with the geometric form of fish schools or with the algorithms of the local behavior of swarm individuals. It thereby follows traces which link biological swarm research to cybernetic ideas of ‘communication’ or ‘information transmission.’ Equipped with a new technical vocabulary, researchers began to describe swarms as ‘systems’ and were able to conceive of them in new ways. Nevertheless, the first approaches to simulating swarm dynamics in the 1970s received little attention, a fact that was likely due to the inability at the time to display dynamic processes visually.