Cooperation and competition among stand-alone swarm agents increase collective fitness of the whole system. A principally new kind of collective systems is demonstrated by some bacteria and fungi, when they build symbiotic organisms. Symbiotic life forms emerge new functional and self-developmental capabilities, which allow better survival of swarm agents in different environments. In this paper we consider energy foraging scenario for two robotic species, swarm robots and symbiotic robot organism. It is indicated that aggregation of microrobots into a robot organism can provide better functional fitness for the whole group. A prototype of microrobots capable of autonomous aggregation and disaggregation are shown.
The University of Texas at Austin（德克萨斯大学奥斯汀分校）；Indian Institute of Science（印度科学学院）
We present a new social animal inspired emotional swarm intelligence technique. This technique is used to solve a variant of the popular collective robots problem called foraging. We show with a simulation study how simple interaction rules based on sensations like hunger and loneliness can lead to globally coherent emergent behavior which allows sensor constrained robots to solve the given problem
Swarm foraging is a common test case application for multi-robot systems. In this paper we present a novel algorithm for controlling swarm robots with limited communication range and storage capacity to efficiently search for and retrieve targets within an unknown environment. In our approach, robots search using random walk and adjust their turn probability based on attraction and repulsion signals they sense from other robots. We compared our algorithm with five different variations reflecting absence or presence of attractive and/or repulsive communication signals. Our results show that bes…
University of New Mexico（新墨西哥大学）；Santa Fe Institute（圣塔菲研究所）
Individual robots are not effective at exploring large unmapped areas. An alternate approach is to use a swarm of simple robots that work together, rather than a single highly capable robot. The central-place foraging algorithm (CPFA) is effective for coordinating robot swarm search and collection tasks. Robots start at a centrally placed location (nest), explore potential targets in the area without global localization or central control, and return the targets to the nest. The scalability of the CPFA is limited because large numbers of robots produce more inter-robot collisions and large sea…
Université Libre de Bruxelles（布鲁塞尔自由大学）；University of Sheffield（谢菲尔德大学）；Italian National Research Council（意大利国家研究委员会）
In this study, we investigate the emergence of naming conventions within a swarm of robots that collectively forage, that is, collect resources from multiple sources in the environment. While foraging, the swarm explores the environment and makes a collective decision on how to exploit the available resources, either by selecting a single source or concurrently exploiting more than one. At the same time, the robots locally exchange messages in order to agree on how to name each source. Here, we study the correlation between the task-induced interaction network and the emergent naming conventio…
This paper presents a novel approach that allows a swarm of heterogeneous robots to produce simultaneously segregative and flocking behaviors using only local sensing. These behaviors have been widely studied in swarm robotics and their combination allows the execution of several complex tasks, ranging from surveillance and reconnaissance, to search and rescue, to transport, and to foraging. Although there are several works in the literature proposing different strategies to achieve these behaviors, to the best of our knowledge, this paper is the first to propose an algorithm that emerges simu…
Massachusetts Institute of Technology（麻省理工学院）；University of Pisa（比萨大学）；Bruno Kessler Foundation（布鲁诺·凯斯勒基金会）；University of Florence（佛罗伦萨大学）；Université libre de Bruxelles（布鲁塞尔自由大学）
Modern cities are growing ecosystems that face new challenges due to the increasing population demands. One of the many problems they face nowadays is waste management, which has become a pressing issue requiring new solutions. Swarm robotics systems have been attracting an increasing amount of attention in the past years and they are expected to become one of the main driving factors for innovation in the field of robotics. The research presented in this paper explores the feasibility of a swarm robotics system in an urban environment. By using bio-inspired foraging methods such as multi-plac…
We present a biologically inspired design for swarm foraging based on ant's pheromone deployment, where the swarm is assumed to have very restricted capabilities. The robots do not require global or relative position measurements and the swarm is fully decentralized and needs no infrastructure in place. Additionally, the system only requires one-hop communication over the robot network, we do not make any assumptions about the connectivity of the communication graph and the transmission of information and computation is scalable versus the number of agents. This is done by letting the agents i…
The applicability of the swarm robots to perform foraging tasks is inspired by their compact size and cost. A considerable amount of energy is required to perform such tasks, especially if the tasks are continuous and/or repetitive. Real-world situations in which robots perform tasks continuously while staying alive (survivability) and maximizing production (performance) require energy awareness. This paper proposes an energy-conscious distributed task allocation algorithm to solve continuous tasks (e.g., unlimited foraging) for cooperative robots to achieve highly effective missions. We consi…
University of Stuttgart（斯图加特大学）；Fraunhofer Institute for Biomedical Engineering（弗劳恩霍夫生物医学工程研究所）；UWE Bristol（西英格兰大学布里斯托尔分校）；Sheffield Hallam University（谢菲尔德哈勒姆大学）；Ubisense AG（Ubisense股份公司）；IMA, s.r.o.（IMA有限责任公司）
There is a belief that complexity and chaos are essential for adaptability. But life deals with complexity every moment, without the chaos that engineers fear so, by invoking goal-directed behaviour. Goals can be programmed. That is why living organisms give us hope to achieve adaptability in robots. In this paper a method for the description of a goal-directed, or programmed, behaviour, interacting with uncertainty of environment, is described. We suggest reducing the structural (goals, intentions) and stochastic components (probability to realise the goal) of individual behaviour to random v…
Physics-Informed Modeling and Control of Emergent Behaviors in Robot Swarms
School of Computer and Communication Engineering, University of Science and Technology Beijing（北京科技大学计算机与通信工程学院）；Shunde Innovation School, University of Science and Technology Beijing（北京科技大学顺德创新学院）
Task allocation is an important problem for robot swarms to solve, allowing agents to reduce task completion time by performing tasks in a distributed fashion. Existing task allocation algorithms often assume prior knowledge of task location and demand or fail to consider the effects of the geometric distribution of tasks on the completion time and communication cost of the algorithms. In this paper, we examine an environment where agents must explore and discover tasks with positive demand and successfully assign themselves to complete all such tasks. We first provide a new discrete general m…
Swarm navigation of cyborg-insects in unknown obstructed soft terrain
Graduate School of Information Science and Technology, Osaka University（大阪大学信息科学与技术研究生院）；Graduate School of Advanced Science and Engineering, Hiroshima University（广岛大学先进科学与工程研究生院）；School of Mechanical & Aerospace Engineering, Nanyang Technological University（南洋理工大学机械与航空航天工程学院）
On the Cost of Evolving Task Specialization in Multi-Robot Systems
Centre for the Advanced Study of Collective Behaviour（集体行为高级研究所在）；University of Konstanz（康斯坦茨大学）；Department of Computer and Information Science（计算机与信息科学系）；University of Technology Nuremberg（纽伦堡技术大学）；Department of Computer Science and Artificial Intelligence（计算机科学与人工智能系）
Predator-prey survival pressure is sufficient to evolve swarming behaviors
Zhejiang University（浙江大学）；Westlake University（西湖大学）；Max Planck Institute of Animal Behavior（马克斯·普朗克动物行为研究所）；Univ. of Konstanz（康斯坦茨大学）；University of Konstanz（康斯坦茨大学）
Jadavpur University（贾达普大学）；National Institute Of Technology, Durgapur（杜尔加布尔国家理工学院）；Indian Statistical Institute（印度统计研究所）
Principle of Swarm Intelligence has recently found widespread application in formation control and automated tracking by the automated multi-agent system. This article proposes an elegant and effective method inspired by foraging dynamics to produce geometric-patterns by the search agents. Starting from a random initial orientation, it is investigated how the foraging dynamics can be modified to achieve convergence of the agents on the desired pattern with almost uniform density. Guided through the proposed dynamics, the agents can also track a moving point by continuously circulating around t…
This paper proposes a model-based framework to automatically and efficiently design understandable and verifiable behaviors for swarms of robots. The framework is based on the automatic extraction of two distinct models: 1) a neural network model trained to estimate the relationship between the robots' sensor readings and the global performance of the swarm, and 2) a probabilistic state transition model that explicitly models the local state transitions (i.e., transitions in observations from the perspective of a single robot in the swarm) given a policy. The models can be trained from a data …
Toward Task Capable Active Matter: Learning to Avoid Clogging in Confined Collectives via Collisions
Institute for Robotics and Intelligent Machines, Georgia Institute of Technology（佐治亚理工学院机器人与智能机器研究所）；School of Physics, Georgia Institute of Technology（佐治亚理工学院物理学院）；Department of Physics, Department of MCD Biology, University of Colorado Boulder（科罗拉多大学博尔德分校物理系与MCD生物学系）；Center for Computational Biology, Flatiron Institute（熨斗研究所计算生物学中心）；School of Biological Sciences, Georgia Institute of Technology（佐治亚理工学院生物科学学院）
College of Engineering, University of Georgia（佐治亚大学工程学院）；School of Computing, University of Georgia（佐治亚大学计算学院）
Achieving knowledge sharing within an artificial swarm system could lead to significant development in autonomous multiagent and robotic systems research and realize collective intelligence. However, this is difficult to achieve since there is no generic framework to transfer skills between agents other than a query-response-based approach. Moreover, natural living systems have a "forgetfulness" property for everything they learn. Analyzing such ephemeral nature (temporal memory properties of new knowledge gained) in artificial systems has never been studied in the literature. We propose a beh…
