Exploring unknown environments with multi-modal locomotion swarm

International audienceSwarm robotics is focused on creating intelligent systems from large number of simple robots. The majority of nowadays robots are bound to operations within mono-modal locomotion (i.e. land, air or water). However, some animals have the capacity to alter their locomotion modalities to suit various terrains, operating at high levels of competence in a range of substrates. One of the most significant challenges in bio-inspired robotics is to determine how to use multi-modal locomotion to help robots perform a variety of tasks. In this paper, we investigate the use of multi-modal locomotion on a swarm of robots through a multi-target search algorithm inspired from the behavior of flying ants. Features of swarm intelligence such as distributivity, robustness and scalability are ensured by the proposed algorithm. Although the simplicity of movement policies of each agent, complex and efficient exploration is achieved at the team level

Human Swarm Interaction: An Experimental Study of Two Types of Interaction with Foraging Swarms

In this paper we present the first study of human-swarm interaction comparing two fundamental types of interaction, coined intermittent and environmental. These types are exemplified by two control methods, selection and beacon control, made available to a human operator to control a foraging swarm of robots. Selection and beacon control differ with respect to their temporal and spatial influence on the swarm and enable an operator to generate different strategies from the basic behaviors of the swarm. Selection control requires an active selection of groups of robots while beacon control exerts an influence on nearby robots within a set range. Both control methods are implemented in a testbed in which operators solve an information foraging problem by utilizing a set of swarm behaviors. The robotic swarm has only local communication and sensing capabilities. The number of robots in the swarm range from 50 to 200. Operator performance for each control method is compared in a series of missions in different environments with no obstacles up to cluttered and structured obstacles. In addition, performance is compared to simple and advanced autonomous swarms. Thirty-two participants were recruited for participation in the study. Autonomous swarm algorithms were tested in repeated simulations. Our results showed that selection control scales better to larger swarms and generally outperforms beacon control. Operators utilized different swarm behaviors with different frequency across control methods, suggesting an adaptation to different strategies induced by choice of control method. Simple autonomous swarms outperformed human operators in open environments, but operators adapted better to complex environments with obstacles. Human controlled swarms fell short of task-specific benchmarks under all conditions. Our results reinforce the importance of understanding and choosing appropriate types of human-swarm interaction when designing swarm systems, in addition to choosing appropriate swarm behaviors

An Agent-Based Simulation API for Speculative PDES Runtime Environments

Agent-Based Modeling and Simulation (ABMS) is an effective paradigm to model systems exhibiting complex interactions, also with the goal of studying the emergent behavior of these systems. While ABMS has been effectively used in many disciplines, many successful models are still run only sequentially. Relying on simple and easy-to-use languages such as NetLogo limits the possibility to benefit from more effective runtime paradigms, such as speculative Parallel Discrete Event Simulation (PDES). In this paper, we discuss a semantically-rich API allowing to implement Agent-Based Models in a simple and effective way. We also describe the critical points which should be taken into account to implement this API in a speculative PDES environment, to scale up simulations on distributed massively-parallel clusters. We present an experimental assessment showing how our proposal allows to implement complicated interactions with a reduced complexity, while delivering a non-negligible performance increase

An Energy-Aware Algorithm for Large Scale Foraging Systems

International audienceThe foraging task is one of the canonical testbeds for cooperative robotics, in which a collection of coordinated robots have to find and transport one or more objects to one or more specific storage points. Swarm robotics has been widely considered in such situations, due to its strengths such as robustness, simplicity and scalability. Typical multi-robot foraging systems currently consider tens to hundreds of agents. This paper presents a new algorithm called Energy-aware Cooperative Switching Algorithm for Foraging (EC-SAF) that manages thousands of robots. We investigate therefore the scalability of EC-SAF algorithm and the parameters that can affect energy efficiency overtime. Results indicate that EC-SAF is scalable and effective in reducing swarm energy consumption compared to an energy-aware version of the reference well-known c-marking algorithm (Ec-marking)

A Bio-Inspired Algorithm and Foldable Robot Platform for Collective Excavation

abstract: Existing robotic excavation research has been primarily focused on lunar mining missions or simple traffic control in confined tunnels, however little work attempts to bring collective excavation into the realm of human infrastructure. This thesis explores a decentralized approach to excavation processes, where traffic laws are borrowed from swarms of fire ants (Solenopsis invicta) or termites (Coptotermes formosanus) to create decision rules for a swarm of robots working together and organizing effectively to create a desired final excavated pattern. First, a literature review of the behavioral rules of different types of insect colonies and the resulting structural patterns over the course of excavation was conducted. After identifying pertinent excavation laws, three different finite state machines were generated that relate to construction, search and rescue operations, and extraterrestrial exploration. After analyzing these finite state machines, it became apparent that they all shared a common controller. Then, agent-based NetLogo software was used to simulate a swarm of agents that run this controller, and a model for excavating behaviors and patterns was fit to the simulation data. This model predicts the tunnel shapes formed in the simulation as a function of the swarm size and a time delay, called the critical waiting period, in one of the state transitions. Thus, by controlling the individual agents' behavior, it was possible to control the structural outcomes of collective excavation in simulation. To create an experimental testbed that could be used to physically implement the controller, a small foldable robotic platform was developed, and it's capabilities were tested in granular media. In order to characterize the granular media, force experiments were conducted and parameters were measured for resistive forces during an excavation cycle. The final experiment verified the robot's ability to engage in excavation and deposition, and to determine whether or not to begin the critical waiting period. This testbed can be expanded with multiple robots to conduct small-scale experiments on collective excavation, such as further exploring the effects of the critical waiting period on the resulting excavation pattern. In addition, investigating other factors like tuning digging efficiency or deposition proximity could help to transition the proposed bio-inspired swarm excavation controllers to implementation in real-world applications.Dissertation/ThesisMasters Thesis Mechanical Engineering 201

Emergent communication enhances foraging behaviour in evolved swarms controlled by Spiking Neural Networks

Social insects such as ants communicate via pheromones which allows them to coordinate their activity and solve complex tasks as a swarm, e.g. foraging for food. This behavior was shaped through evolutionary processes. In computational models, self-coordination in swarms has been implemented using probabilistic or simple action rules to shape the decision of each agent and the collective behavior. However, manual tuned decision rules may limit the behavior of the swarm. In this work we investigate the emergence of self-coordination and communication in evolved swarms without defining any explicit rule. We evolve a swarm of agents representing an ant colony. We use an evolutionary algorithm to optimize a spiking neural network (SNN) which serves as an artificial brain to control the behavior of each agent. The goal of the evolved colony is to find optimal ways to forage for food and return it to the nest in the shortest amount of time. In the evolutionary phase, the ants are able to learn to collaborate by depositing pheromone near food piles and near the nest to guide other ants. The pheromone usage is not manually encoded into the network; instead, this behavior is established through the optimization procedure. We observe that pheromone-based communication enables the ants to perform better in comparison to colonies where communication via pheromone did not emerge. We assess the foraging performance by comparing the SNN based model to a rule based system. Our results show that the SNN based model can efficiently complete the foraging task in a short amount of time. Our approach illustrates self coordination via pheromone emerges as a result of the network optimization. This work serves as a proof of concept for the possibility of creating complex applications utilizing SNNs as underlying architectures for multi-agent interactions where communication and self-coordination is desired.Comment: 27 pages, 16 figure

A Review of Platforms for the Development of Agent Systems

Agent-based computing is an active field of research with the goal of building autonomous software of hardware entities. This task is often facilitated by the use of dedicated, specialized frameworks. For almost thirty years, many such agent platforms have been developed. Meanwhile, some of them have been abandoned, others continue their development and new platforms are released. This paper presents a up-to-date review of the existing agent platforms and also a historical perspective of this domain. It aims to serve as a reference point for people interested in developing agent systems. This work details the main characteristics of the included agent platforms, together with links to specific projects where they have been used. It distinguishes between the active platforms and those no longer under development or with unclear status. It also classifies the agent platforms as general purpose ones, free or commercial, and specialized ones, which can be used for particular types of applications.Comment: 40 pages, 2 figures, 9 tables, 83 reference

Swarm-inspired solution strategy for the search problem of unmanned aerial vehicles

Learning from the emergent behaviour of social insects, this research studies the influences of environment to collective problem-solving of insect behaviour and distributed intelligent systems. Literature research has been conducted to understand the emergent paradigms of social insects, and to investigate current research and development of distributed intelligent systems. On the basis of the literature investigation, the environment is considered to have significant impact on the effectiveness and efficiency of collective problem-solving. A framework of collective problem-solving is developed in an interdisciplinary context to describe the influences of the environment to insect behaviour and problem-solving of distributed intelligent systems. The environment roles and responsibilities are transformed into and deployed as a problem-solving mechanism for distributed intelligent systems. A swarm-inspired search strategy is proposed as a behaviour-based cooperative search solution. It is applied to the cooperative search problem of Unmanned Aerial Vehicles (UAVs) with a series of experiments implemented for evaluation. The search environment represents the specification and requirements of the search problem; defines tasks to be achieved and maintained; and it is where targets are locally observable and accessible to UAVs. Therefore, the information provided through the search environment is used to define rules of behaviour for UAVs. The initial detection of target signal refers to modified configurations of the search environment, which mediates local communications among UAVs and is used as a means of coordination. The experimental results indicate that, the swarm-inspired search strategy is a valuable alternative solution to current approaches of cooperative search problem of UAVs. In the proposed search solution, the diagonal formation of two UAVs is able to produce superior performance than the triangular formation of three UAVs for the average detection time and the number of targets located within the maximum time length

The matching law and melioration learning: From individual decision-making to social interactions

Das Thema dieser Dissertation ist die Anwendung des „Matching Law” als Verhaltensannahme bei der Erklärung sozialer Phänomene. Das „Matching Law” ist ein Modell der behavioristischen Lerntheorie und sagt aus, dass die relative Häufigkeit der Wahl einer Handlung mit der relativen Häufigkeit der Belohnung dieser Handlung übereinstimmt. In der Dissertation werden verschiedene Probleme in Bezug auf die soziologische Anwendung des „Matching Law” erörtert. Aufbauend auf diesen Erkenntnissen wird das Entsprechungsgesetz in die ökonomische Entscheidungstheorie integriert und mit bestehenden Verhaltensprognosen theoretisch verglichen. Anschließend wird das Entsprechungsgesetz auf mehrere soziale Situationen angewandt. Dabei kommt ein Lernmodell zum Einsatz, welches als „Melioration Learning” bezeichnet wird und unter bestimmten Bedingungen zum Entsprechungsgesetz führt. Mit Hilfe dieses Lernmodells und agentenbasierter Simulationen werden Hypothesen zu sozialem Verhalten hergeleitet. Zunächst werden einfache Situationen mit nur zwei interagierenden Akteuren betrachtet. Dabei lassen sich durch das Entsprechungsgesetz einige Lösungskonzepte der Spieltheorie replizieren, obwohl weniger Annahmen bezüglich der kognitiven Fähigkeiten der Akteure und der verfügbaren Informationen gesetzt werden. Außerdem werden Interaktionen zwischen beliebig vielen Akteuren untersucht. Erstens lässt sich die Entstehung sozialer Konventionen über das Entsprechungsgesetz erklären. Zweitens wird dargestellt, dass die Akteure lernen, in einem Freiwilligendilemma oder einem Mehrpersonen-Gefangenendilemma zu kooperieren