Towards formal models and languages for verifiable Multi-Robot Systems

Incorrect operations of a Multi-Robot System (MRS) may not only lead to unsatisfactory results, but can also cause economic losses and threats to safety. These threats may not always be apparent, since they may arise as unforeseen consequences of the interactions between elements of the system. This call for tools and techniques that can help in providing guarantees about MRSs behaviour. We think that, whenever possible, these guarantees should be backed up by formal proofs to complement traditional approaches based on testing and simulation. We believe that tailored linguistic support to specify MRSs is a major step towards this goal. In particular, reducing the gap between typical features of an MRS and the level of abstraction of the linguistic primitives would simplify both the specification of these systems and the verification of their properties. In this work, we review different agent-oriented languages and their features; we then consider a selection of case studies of interest and implement them useing the surveyed languages. We also evaluate and compare effectiveness of the proposed solution, considering, in particular, easiness of expressing non-trivial behaviour.Comment: Changed formattin

An Amalgamation of Hormone Inspired Arbitration Systems For Application In Robot Swarms

Previous work has shown that virtual hormone systems can be engineered to arbitrateswarms of robots between sets of behaviours. These virtual hormones act similarly to theirnatural counterparts, providing a method of online, reactive adaptation. It is yet to be shownhow virtual hormone systems could be used when a robotic swarm has a large variety of task typesto execute. This paper details work that demonstrates the viability of a collection of virtual hormonesthat can be used to regulate and adapt a swarm over time, in response to different environmentsand tasks. Specifically, the paper examines a new method of hormone speed control for energyefficiency and combines it with two existing systems controlling environmental preference as wellas a selection of behaviours that produce an effective foraging swarm. Experiments confirm theeffectiveness of the combined system, showing that a swarm of robots equipped with multiple virtualhormones can forage efficiently to a specified item demand within an allotted period of time

A study of error diversity in robotic swarms for task partitioning in foraging tasks

Often in swarm robotics, an assumption is made that all robots in the swarm behave the same and will have a similar (if not the same) error model. However, in reality this is not the case and this lack of uniformity in the error model, and other operations, can lead to various emergent behaviours. This paper considers the impact of the error model and compares robots in a swarm that operate using the same error model (uniform error) against each robot in the swarm having a different error model (thus introducing error diversity). Experiments are presented in the context of a foraging task. Simulation and physical experimental results show the importance of the error model and diversity in achieving expected swarm behaviour

An Efficient Multiple-Place Foraging Algorithm for Scalable Robot Swarms

Searching and collecting multiple resources from large unmapped environments is an important challenge. It is particularly difficult given limited time, a large search area and incomplete data about the environment. This search task is an abstraction of many real-world applications such as search and rescue, hazardous material clean-up, and space exploration. The collective foraging behavior of robot swarms is an effective approach for this task. In our work, individual robots have limited sensing and communication range (like ants), but they are organized and work together to complete foraging tasks collectively. An efficient foraging algorithm coordinates robots to search and collect as many resources as possible in the least amount of time. In the foraging algorithms we study, robots act independently with little or no central control. As the swarm size and arena size increase (e.g., thousands of robots searching over the surface of Mars or ocean), the foraging performance per robot decreases. Generally, larger robot swarms produce more inter-robot collisions, and in swarm robot foraging, larger search arenas result in larger travel distances causing the phenomenon of diminishing returns. The foraging performance per robot (measured as a number of collected resources per unit time) is sublinear with the arena size and the swarm size. Our goal is to design a scale-invariant foraging robot swarm. In other words, the foraging performance per robot should be nearly constant as the arena size and the swarm size increase. We address these problems with the Multiple-Place Foraging Algorithm (MPFA), which uses multiple collection zones distributed throughout the search area. Robots start from randomly assigned home collection zones but always return to the closest collection zones with found resources. We simulate the foraging behavior of robot swarms in the robot simulator ARGoS and employ a Genetic Algorithm (GA) to discover different optimized foraging strategies as swarm sizes and the number of resources is scaled up. In our experiments, the MPFA always produces higher foraging rates, fewer collisions, and lower travel and search time than the Central-Place Foraging Algorithm (CPFA). To make the MPFA more adaptable, we introduce dynamic depots that move to the centroid of recently collected resources, minimizing transport times when resources are clustered in heterogeneous distributions. Finally, we extend the MPFA with a bio-inspired hierarchical branching transportation network. We demonstrate a scale-invariant swarm foraging algorithm that ensures that each robot finds and delivers resources to a central collection zone at the same rate, regardless of the size of the swarm or the search area. Dispersed mobile depots aggregate locally foraged resources and transport them to a central place via a hierarchical branching transportation network. This approach is inspired by ubiquitous fractal branching networks such as animal cardiovascular networks that deliver resources to cells and determine the scale and pace of life. The transportation of resources through the cardiovascular system from the heart to dispersed cells is the inverse problem of transportation of dispersed resources to a central collection zone through the hierarchical branching transportation network in robot swarms. We demonstrate that biological scaling laws predict how quickly robots forage in simulations of up to thousands of robots searching over thousands of square meters. We then use biological scaling predictions to determine the capacity of depot robots in order to overcome scaling constraints and produce scale-invariant robot swarms. We verify the predictions using ARGoS simulations. While simulations are useful for initial evaluations of the viability of algorithms, our ultimate goal is predicting how algorithms will perform when physical robots interact in the unpredictable conditions of environments they are placed in. The CPFA and the Distributed Deterministic Spiral Algorithm (DDSA) are compared in physical robots in a large outdoor arena. The physical experiments change our conclusion about which algorithm has the best performance, emphasizing the importance of systematically comparing the performance of swarm robotic algorithms in the real world. We illustrate the feasibility of implementing the MPFA with transportation networks in physical robot swarms. Full implementation of the MPFA in an outdoor environment is the next step to demonstrate truly scalable and robust foraging robot swarms

Arquitectura para robots de búsqueda y rescate urbano mediante el uso de algoritmos de anti-feromonas

[ES] El atentado del 11 de septiembre de 2001 fue el ataque terrorista con mayor mortalidad en la historia de la humanidad, con un resultado de 2.996 muertes y mas de 25.000 heridos. Entre las víctimas, un total de 343 bomberos y 72 policías perdieron sus vidas. La muerte de una gran parte de estas personas, y en especial de los servicios de emergencia, fue a causa del peligro que ataña acceder a través de los escombros de los edificios derruidos. Dada la situación, varios equipo y universidades que disponían de robots de rescate, acudieron hasta la zona cero para ayudar en la ardua tarea de buscar víctimas con vida. Este fatídico evento provocó el auge de la investigación en el ámbito de la Búsqueda y Rescate Urbano. Desde entonces hasta el día de hoy, se han empleado robots como respuesta a una catástrofe en diversas ocasiones. En este trabajo se ha desarrollado una arquitectura para el uso de un enjambre de robots heterogéneo y semi-supervisado en un entorno de Búsqueda y Rescate Urbano. Más concretamente, la arquitectura permite la combinación de diversos algoritmos orientados a este ámbito para la obtención de un sistema complejo y a su vez independiente tanto del hardware como de los métodos usados. Además, se propone una nueva estrategia de exploración colaborativa basada en el comportamiento social de las hormigas. El algoritmo planteado hace uso de feromonas repelentes como mecanismo para fomentar la exploración en entornos desconocidos. Para el análisis y prueba del algoritmo y la arquitectura propuestos en este trabajo, se han diseñado una serie de experimentos. En primer lugar se ha analizado el comportamiento del algoritmo de exploración con anti-feromonas en entornos acotados basados en topologías de rejilla y de laberinto; posteriormente se han realizado en un entorno real. Los experimentos han sido estudiados tanto con simulaciones como con robots reales. Para el análisis de la arquitectura planteada, se ha implementado un sistema de búsqueda y rescate completo sobre un robot Jetbot de Nvidia, el cual ha sido probado en un entorno real. Para finalizar, se demuestra cómo la arquitectura planteada y el algoritmo propuestos son soluciones adecuadas para su uso en respuesta a una catástrofe. Además, la arquitectura planteada en este trabajo también puede permitir el uso de algoritmos que surjan en el futuro

Multi-Robot Systems: Challenges, Trends and Applications

This book is a printed edition of the Special Issue entitled “Multi-Robot Systems: Challenges, Trends, and Applications” that was published in Applied Sciences. This Special Issue collected seventeen high-quality papers that discuss the main challenges of multi-robot systems, present the trends to address these issues, and report various relevant applications. Some of the topics addressed by these papers are robot swarms, mission planning, robot teaming, machine learning, immersive technologies, search and rescue, and social robotics