Giovanni Pini

ARGoS: A modular, multi-engine simulator for heterogeneous swarm robotics

We present ARGoS, a novel open source multi-robot simulator. The main design focus of ARGoS is the real-time simulation of large heterogeneous swarms of robots. Existing robot simulators obtain scalability by imposing limitations on their extensibility and on the accuracy of the robot models. By contrast, in ARGoS we pursue a deeply modular approach that allows the user both to easily add custom features and to allocate computational resources where needed by the experiment. A unique feature of ARGoS is the possibility to use multiple physics engines of different types and to assign them to different parts of the environment. Robots can migrate from one engine to another transparently. This feature enables entirely novel classes of optimizations to improve scalability and paves the way for a new approach to parallelism in robotics simulation. Results show that ARGoS can simulate about 10,000 simple wheeled robots 40% faster than real-time.

Self-organized task allocation to sequentially interdependent tasks in swarm robotics

In this article we present a self-organized method for allocating the individuals of a robot swarm to tasks that are sequentially interdependent. Tasks that are sequentially interdependent are common in natural and artificial systems. The proposed method does neither rely on global knowledge nor centralized components. Moreover, it does not require the robots to communicate. The method is based on the delay experienced by the robots working on one subtask when waiting for input from another subtask. We explore the capabilities of the method in different simulated environments. Additionally, we evaluate the method in a proof-of-concept experiment using real robots. We show that the method allows a swarm to reach a near-optimal allocation in the studied environments, can easily be transferred to a real robot setting, and is adaptive to changes in the properties of the tasks such as their duration. Finally, we show that the ideal setting of the parameters of the method does not depend on the properties of the environment.

Task partitioning in swarms of robots: an adaptive method for strategy selection

Task partitioning is the decomposition of a task into two or more sub-tasks that can be tackled separately. Task partitioning can be observed in many species of social insects, as it is often an advantageous way of organizing the work of a group of individuals. Potential advantages of task partitioning are, among others: reduction of interference between workers, exploitation of individuals’ skills and specializations, energy efficiency, and higher parallelism. Even though swarms of robots can benefit from task partitioning in the same way as social insects do, only few works in swarm robotics are dedicated to this subject. In this paper, we study the case in which a swarm of robots has to tackle a task that can be partitioned into a sequence of two sub-tasks. We propose a method that allows the individual robots in the swarm to decide whether to partition the given task or not. The method is self-organized, relies on the experience of each individual, and does not require explicit communication between robots. We evaluate the method in simulation experiments, using foraging as testbed. We study cases in which task partitioning is preferable and cases in which it is not. We show that the proposed method leads to good performance of the swarm in both cases, by employing task partitioning only when it is advantageous. We also show that the swarm is able to react to changes in the environmental conditions by adapting the behavior on-line. Scalability experiments show that the proposed method performs well across all the tested group sizes.

Autonomous task partitioning in robot foraging: an approach based on cost estimation

We propose an approach for autonomous task partitioning in swarms of foraging robots. Task partitioning is the process of decomposing tasks into sub-tasks. Task partitioning impacts tasks execution and associated costs. Our approach is characterized by the use of a cost function, mapping the size of sub-tasks to the overall task cost. The robots model the cost function and use the model to select sub-tasks to perform, aiming to minimize costs. Our approach separates the task partitioning process from task-specific actions and it does not require a priori assumptions to be made about the best partitioning strategy to employ. We study a foraging scenario in which object transportation is performed by different robots, each moving objects for a limited distance. The robots autonomously decide the distance traveled on the basis of our approach. The robots use odometry for navigational purposes; we show that task partitioning reduces the impact of odometry errors and improves performance. We validate our approach using simulation-based experiments. We study how the swarm partitions transportation under a number of experimental conditions characterized by different levels of odometry accuracy, size of the environment and the swarm, and total transportation distance. Our approach leads to partitioning solutions that are appropriate for each condition.

Task Partitioning in Swarms of Robots: Reducing Performance Losses Due to Interference at Shared Resources

The performance of large groups of robots is often limited by a commonly shared resource. This effect, termed interference, can have a large impact on robotic swarms. This article studies the issue of interference in a swarm of robots working on a harvesting task. The environment of the robots is spatially constrained, i.e., there is a commonly shared resource, the nest, which limits the group’s performance when used without any arbitration mechanism. The article studies the use of task partitioning for reducing concurrent accesses to the resource, and therefore limiting the impact of interference on the group’s performance. In our study, we spatially partition the environment into two subparts, thereby partitioning the corresponding harvesting task as well. We employ a simple method to allocate individuals to the partitions. The approach is empirically studied both in an environment with a narrow nest area and an environment without this constraint. The results of the task partitioning strategy are analyzed and compared to the case in which task partitioning is not employed.

The TAM: abstracting complex tasks in swarm robotics research

Research in swarm robotics focuses mostly on how robots interact and cooperate to perform tasks, rather than on the details of task execution. As a consequence, researchers often consider abstract tasks in their experimental work. For example, foraging is often studied without physically handling objects: the retrieval of an object from a source to a destination is abstracted into a trip between the two locations—no object is physically transported. Despite being commonly used, so far task abstraction has only been implemented in an ad hoc fashion. In this paper, we propose a new approach to abstracting complex tasks in swarm robotics research. This approach is based on a physical device called the “task abstraction module” (TAM) that abstracts single-robot tasks to be performed by an e-puck robot. A complex multi-robot task can be abstracted using a group of TAMs by first modeling the task as the set of its constituent single-robot subtasks and then abstracting each subtask with a TAM. We present a collection of tools for modeling complex tasks, and a framework for controlling a group of TAMs such that the behavior of the group implements the model of the task. The TAM enables research on cooperative behaviors and complex tasks with simple, cost-effective robots such as the e-puck—research that would be difficult and costly to conduct using specialized robots or ad hoc task abstraction. We demonstrate how to abstract a complex task with multiple TAMs in an example scenario involving a swarm of e-puck robots.

Task partitioning in a robot swarm: a study on the effect of communication

Task partitioning consists in dividing a task into sub-tasks that can be tackled separately. Partitioning a task might have both positive and negative effects: On the one hand, partitioning might reduce physical interference between workers, enhance exploitation of specialization, and increase efficiency. On the other hand, partitioning may introduce overheads due to coordination requirements. As a result, whether partitioning is advantageous or not has to be evaluated on a case-by-case basis. In this paper we consider the case in which a swarm of robots must decide whether to complete a given task as an unpartitioned task, or utilize task partitioning and tackle it as a sequence of two sub-tasks. We show that the problem of selecting between the two options can be formulated as a multi-armed bandit problem and tackled with algorithms that have been proposed in the reinforcement learning literature. Additionally, we study the implications of using explicit communication between the robots to tackle the studied task partitioning problem. We consider a foraging scenario as a testbed and we perform simulation-based experiments to evaluate the behavior of the system. The results confirm that existing multi-armed bandit algorithms can be employed in the context of task partitioning. The use of communication can result in better performance, but in may also hinder the flexibility of the system.

On the design of neuro-controllers for individual and social learning behaviour in autonomous robots: an evolutionary approach

In biology/psychology, the capability of natural organisms to learn from the observation/interaction with conspecifics is referred to as social learning. Roboticists have recently developed an interest in social learning, since it might represent an effective strategy to enhance the adaptivity of a team of autonomous robots. In this study, we show that a methodological approach based on artifcial neural networks shaped by evolutionary computation techniques can be successfully employed to synthesise the individual and social learning mechanisms for robots required to learn a desired action (i.e. phototaxis or antiphototaxis).

Task partitioning in a robot swarm: Object retrieval as a sequence of subtasks with direct object transfer

We study task partitioning in the context of swarm robotics. Task partitioning is the decomposition of a task into subtasks that can be tackled by different workers. We focus on the case in which a task is partitioned into a sequence of subtasks that must be executed in a certain order. This implies that the subtasks must interface with each other, and that the output of a subtask is used as input for the subtask that follows. A distinction can be made between task partitioning with direct transfer and with indirect transfer. We focus our study on the first case: The output of a subtask is directly transferred from an individual working on that subtask to an individual working on the subtask that follows. As a test bed for our study, we use a swarm of robots performing foraging. The robots have to harvest objects from a source, situated in an unknown location, and transport them to a home location. When a robot finds the source, it memorizes its position and uses dead reckoning to return there. Dead reckoning is appealing in robotics, since it is a cheap localization method and it does not require any additional external infrastructure. However, dead reckoning leads to errors that grow in time if not corrected periodically. We compare a foraging strategy that does not make use of task partitioning with one that does. We show that cooperation through task partitioning can be used to limit the effect of dead reckoning errors. This results in improved capability of locating the object source and in increased performance of the swarm. We use the implemented system as a test bed to study benefits and costs of task partitioning with direct transfer. We implement the system with real robots, demonstrating the feasibility of our approach in a foraging scenario.

Self-organized task partitioning in a swarm of robots

In this work, we propose a method for self-organized adaptive task partitioning in a swarm of robots. Task partitioning refers to the decomposition of a task into less complex subtasks, which can then be tackled separately. Task partitioning can be observed in many species of social animals, where it provides several benefits for the group. Self-organized task partitioning in artificial swarm systems is currently not widely studied, although it has clear advantages in large groups. We propose a fully decentralized adaptive method that allows a swarm of robots to autonomously decide whether to partition a task into two sequential subtasks or not. The method is tested on a simulated foraging problem. We study the methods performance in two different environments. In one environment the performance of the system is optimal when the foraging task is partitioned, in the other case when it is not. We show that by employing the method proposed in this paper, a swarm of autonomous robots can reach optimal performance in both environments.