We consider typical scenarios where an autonomous multi-robot team is used for surveying a large region. The desired output is a spatial map of the physical values of interest. Accounting for spatial correlation and uncertainty, the map is modeled using a Gaussian Process. Considering real-world constraints such as limited time budget and collision avoidance, we model team’s mission as a joint informative path planning problem which is tackled using a leader-follower architecture balancing centralized and fully distributed computation of plans. The leader first identifies a convex containment region that is to be sampled by the team. Next, through a combination of Bayesian optimization and Monte Carlo simulation, distinct sampling locations are identified and assigned to the followers. Each follower independently solves an orienteering problem to find a collision-free path maximizing information gain. A team-level adaptive replanning criterion is designed to keep redirecting sampling towards the most informative regions. The algorithm has been validated in computational experiments for map estimation. Compared to a baseline reference algorithm, it has shown a significantly higher accuracy. Moreover, the approach has shown good ability to support network connectivity, as well as good scalability in computation.

Thymio is a small, inexpensive, mass-produced mobile robot with widespread use in primary and secondary education. In order to make it more versatile and effectively use it in later educational stages, including university levels, we have expanded Thymio's capabilities by adding off-the-shelf hardware and open software components. The resulting robot, that we call Mighty Thymio, provides additional sensing functionalities, increased computing power, networking, and full ROS integration. We present the architecture of Mighty Thymio and show its application in advanced educational activities.

We propose a model of artificial emotions for adaptation and implicit coordination in multi-robot systems. Artificial emotions play two roles, which resemble their function in animals and humans: modulators of individual behavior, and means of communication for social coordination. Emotions are modeled as compressed representations of the internal state, and are subject to a dynamics depending on internal and external conditions. Being a compressed representation, they can be efficiently exposed to nearby robots, allowing to achieve local group-level communication. The model is instantiated for a navigation task, with the aim of showing how coordination can effectively emerge by adding artificial emotions on top of an existing navigation framework. We show the positive effects of emotion-mediated group behaviors in a few challenging scenarios that would otherwise require ad hoc strategies: preventing deadlocks in crowded conditions; enabling efficient navigation of agents with time-critical tasks; assisting robots with faulty sensors. Two performance measures, throughput and number of collisions, are used to quantify the contribution of emotions for modulation and coordination.

In the context of coordination of mobile multi-robot/agent networked teams, we present an integrated model that simultaneously addresses two problems arising in multi-robot missions: (i) task allocation and task scheduling; and (ii) communication provisioning in the multi-hop mobile ad hoc network built by the team. The integrated model is based on a mixed integer linear programming (MILP) formulation, which is solved in a centralized mode. For the communication part, the model solution outputs data routing policies and data transmission schedules that are aimed to maximize data delivery throughput to/from control centers. The trade-off between task and network performance optimization is strategically controlled. A refinement procedure is defined that allows to further improve communications by also minimizing network delays. We report a computational analysis of the integrated MILP model and an evaluation of the impact of a number of parameters on the trade-off between computational load and quality of the output. Results show that the model is computationally affordable for reasonably sized scenarios, and can effectively balance different performance trade-offs.

This work focuses on how to compute trajectories for an autonomous wheelchair based on indoor GIS maps, in particular on IndoorGML maps, which set the standard in this context. Good wheelchair trajectories are safe and comfortable for the user and the people sharing the space with him, turn gently, are high legible, and smooth (at least G2continuos). We derive a navigation graph from a given IndoorGML map. We define and solve an optimization problem to find the desired path: given a succession of cells to traverse, the path corresponds to the best composite Bézier trajectory for the wheelchair. We discuss a related multi-objective path planning problem. Experimental results and an implementation on real robots show the planner performance.

Cooperative Multi-Robot Observation of Multiple Moving Targets (CMOMMT) denotes a class of problems in which a set of autonomous mobile robots equipped with limited-range sensors are used to keep under observation a (possibly larger) set of mobile targets. Robots cooperatively plan their motion in order to maximize the time during which each target lies within the sensing range of at least one robot.

Human-swarm interaction (HSI) is a developing field of research in which the problem of gesture-based control has been attracting an increasing attention, being at the same time a natural form of interaction and an effective way to point and select individual or groups of robots in the swarm. Gesture-based interaction usually requires vision-based recognition and classification of the gesture from the swarm. At this aim, existing methods for cooperative sensing and recognition make use of distributed consensus algorithms, which include for instance averaging and frequency counting. In this work we present a distributed consensus protocol that allows robot swarms to learn efficiently gestures from online interactions with a human teacher. The protocol also facilitates the integration of different consensus algorithms. Experiments have been performed in emulation using on real data acquired by a swarm of robots. The results indicate that effectively exploiting the collective decision-making of the swarm is a viable way to rapidly achieve good learning performance.

No abstract available.

As a first step towards human and multiple-UAV interaction, we present a novel method for humans to interact with flying UAVs using locally on-board video cameras. Using machine vision techniques, our approach enables human operators to command and control Parrot drones by giving them directions to move, using simple hand gestures. When a direction to move is given, the robot controller estimates the angle and distance to move with the help of a face score system and the estimated hand direction. This approach offers mobile robots the ability localize with human operators and provides UAVs/UGVs with a better perception of the environment around the human.

No abstract available.

Human localization is fundamental in human centered computing and human-robot interaction (HRI), as human operators should be localized by robots before being actively serviced. This paper proposes a simple and efficient approach for estimating the distance and orientation of an human, from a single robot-acquired image. We adopt a simple combination of multiple Haar feature-based classifiers to compute face scores, that represent the probability that the detected face is acquired from each of a predefined set of poses. Using the Locally Weighted Projectron Regression (LWPR), an online incremental regression-based learning scheme, we can reliably learn and predict the pose of a human face in real-time at a low computational cost. The accuracy, robustness, and scalability of the obtained solutions have been verified through emulation experiments performed on a large data set of real data acquired by a networked swarm of robots.

The performance of a team of robots executing a coordination task is, to a large extent, determined by the reliability of the communications between the robots. In wireless networks, one way to improve this reliability is to choose the best among the available wireless links. For this purpose, an accurate link quality model is required. We show how a group of robots can exploit their mobility to effectively and rapidly learn such a model directly from an unknown environment. The LQE (Link Quality Estimation) protocol, which is used by the robots to cooperatively collect labeled link quality samples, and learn out of them, is presented in the paper. The accuracy and the robustness of the LQE approach are validated through a set of real-world experiments, performed with mobile robots operating in different network environments. Moreover, in simulation, we study a multi-robot coordination problem, and show the benefits of using the link quality learning approach, at the expenses of devoting little time for learning the model before executing the task.

We present a novel approach for the online learning of hand gestures in swarm robotic (multi-robot) systems. We address the problem of online feature learning by proposing Convolutional Max-Pooling (CMP), a simple feed-forward two-layer network derived from the deep hierarchical Max-Pooling Convolutional Neural Network (MPCNN). To learn and classify gestures in an online and incremental fashion, we employ a 2nd order online learning method, namely the Soft-Confidence Weighted (SCW) learning scheme. In order for all robots to collectively take part in the learning and recognition task and obtain a swarm-level classification, we build a distributed consensus by fusing the individual decision opinions of robots together with the individual weights generated from multiple classifiers. Accuracy, robustness, and scalability of obtained solutions have been verified through emulation experiments performed on a large data set of real data acquired by a networked swarm of robots.

We consider the problem of the joint mission planning in teams of heterogeneous physical agents. The type of missions that we consider are composed of spatially distributed tasks that need to be selected and assigned to the agents for dealing with them. A plan consists of a set of directives specifying who does what, where, when, and for how long. The aim is to optimize system-level performance by explicitly taking into account and exploiting the different sensory-motor characteristics of the agents. We tackle this general problem by proposing a mixed integer linear formulation of it which includes several aspects/constraints that closely model features and requests of realistic scenarios. We present a solution approach based on the combination of a metaheuristic and mathematical programming method that allows to compute high-quality plans within short time, and with formal guarantees on their optimality. The application of the framework is validated in the context of search and rescue missions.

Once a multi-robot system is implemented on real hardware and tested in the real world, analyzing its evolution and debugging unexpected behaviors is often a very difficult task. We present a tool for aiding this activity, by visualizing an Augmented Reality overlay on a live video feed acquired by a fixed camera overlooking the robot environment. Such overlay displays live information exposed by each robot, which may be textual (state messages), symbolic (graphs, charts), or, most importantly, spatially-situated; spatially-situated information is related to the environment surrounding the robot itself, such as for example the perceived position of neighboring robots, the perceived extent of obstacles, the path the robot plans to follow. We show that, by directly representing such information on the environment it refers to, our proposal removes a layer of indirection and significantly eases the process of understanding complex multi-robot systems. We describe how the system is implemented, discuss application examples in different scenarios, and provide supplementary material including demonstration videos and a functional implementation.

Small-footprint mobile ground robots, such as the popular Turtlebot and Kobuki platforms, are by necessity equipped with sensors which lie close to the ground. Reliably detecting and tracking people from this viewpoint is a challenging problem, whose solution is a key requirement for many applications involving sharing of common spaces and close human-robot interaction. We present a robust solution for cluttered indoor environments, using an inexpensive RGB-D sensor such as the Microsoft Kinect or Asus Xtion. Even in challenging scenarios with multiple people in view at once and occluding each other, our system solves the person detection problem significantly better than alternative approaches, reaching a precision, recall and F1-score of 0.85, 0.81 and 0.83, respectively. Evaluation datasets, a real-time ROS-enabled implementation and demonstration videos are provided as supplementary material.

This paper presents a machine vision based approach for human operators to select individual and groups of autonomous robots from a swarm of UAVs. The angular distance between the robots and the human is estimated using measures of the detected human face, which aids to determine human and multi-UAV localization and positioning. In turn, this is exploited to effectively and naturally make the human select the spatially situated robots. Spatial gestures for selecting robots are presented by the human operator using tangible input devices (i.e., colored gloves). To select individuals and groups of robot we formulate a vocabulary of two-handed spatial pointing gestures. With the use of a Support Vector Machine (SVM) trained in a cascaded multi-binary-class configuration, the spatial gestures are effectively learned and recognized by a swarm of UAVs.

The vision-based mechanisms that pedestrians in social groups use to navigate in dynamic environments, avoiding obstacles and each others, have been subject to a large amount of research in social anthropology and biological sciences. We build on recent results in these fields to develop a novel fully-distributed algorithm for robot local navigation, which implements the same heuristics for mutual avoidance adopted by humans. The resulting trajectories are human-friendly, because they can intuitively be predicted and interpreted by humans, making the algorithm suitable for the use on robots sharing navigation spaces with humans. The algorithm is computationally light and simple to implement. We study its efficiency and safety in presence of sensing uncertainty, and demonstrate its implementation on real robots. Through extensive quantitative simulations we explore various parameters of the system and demonstrate its good properties in scenarios of different complexity. When the algorithm is implemented on robot swarms, we could observe emergent collective behaviors similar to those observed in human crowds.

Most local robot navigation algorithms are based on the concept of velocity obstacle, a mechanistic approach to the navigation problem in which a solution is engineered from scratch. Over the years, a number of different velocity obstacle variants have been developed to effectively handle multi-robot systems. In parallel, an alternative, human-inspired approach for robot navigation has been recently proposed, which derives from the observation and modeling of crowds of pedestrians. We discuss similarities and differences among two broadly used obstacle-velocity variants, namely Hybrid Reciprocal Velocity Obstacle and Optimal Reciprocal Collision Avoidance, and the human-inspired approach. How do these differences (which are often subtle) impact performance, and why? We answer these questions through extensive simulation experiments, wherein we evaluate the the algorithms for safety, trajectory efficiency, and emergence of collective behaviors, in different challenging multi-robot scenarios using both ideal and realistic models for robots and sensing.

No abstract available.

We introduce Convolutional Neural Support Vector Machines (CNSVMs), a combination of two heterogeneous supervised classification techniques, Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs). CNSVMs are trained using a Stochastic Gradient Descent approach, that provides the computational capability of online incremental learning and is robust for typical learning scenarios in which training samples arrive in mini-batches. This is the case for visual learning and recognition in multi-robot systems, where each robot acquires a different image of the same sample. The experimental results indicate that the CNSVM can be successfully applied to visual learning and recognition of hand gestures as well as to measure learning progress.

We present an approach for distributed real-time recognition tasks using a swarm of mobile robots. We focus on the visual recognition of hand gestures, but the solutions that we provide have general applicability and address a number of challenges common to many distributed sensing and classification problems. In our approach, robots acquire and process hand images from multiple points of view, most of which do not allow for a satisfactory classification. Each robot is equipped with a statistical classifier, which is used to generate an opinion for the sensed gesture. Using a low-bandwidth wireless channel, the robots locally exchange their opinions. They also exploit mobility to adapt their positions to maximize the mutual information collectively gathered by the swarm. A distributed consensus protocol is implemented, to allow to rapidly settle on a decision once enough evidence is available. The system is implemented and demonstrated on real robots. In addition, extensive quantitative results of emulation experiments, based on a real image dataset, are reported. We consider different scenarios and study the scalability and the robustness of the swarm performance for distributed recognition.

Networked robotics is an area that integrates multi-robot and network technology. The characteristics and the reliability of the communication environment play a fundamental role shaping and affecting behavior and performance of a mobile multi-robot system. In this context, two basic questions arise: how much the overall performance is affected and how can we investigate this influence? Addressing these two questions, in this paper we present the architecture of an integrated simulation environment that allows for realistic simulation of networked robotic systems. The proposed framework integrates two simulators: a network simulator and a multi-robot simulator. We present two implementations based on the ARGoS simulator for the robotic side, and with ns-2 and ns-3 employed as network simulators. We evaluate the proposed tools, both in isolation and integration, and show that they are able to efficiently simulate systems consisting of hundreds of robots. Moreover, we use the proposed framework to demonstrate the effects of communication on the performance of a mobile multi-robot system performing distributed coordination and task assignment. We compare realistic network simulation with simplified communication models and we study the resulting behavior and performance of the robotic system.

In this paper we consider a human-swarm interaction scenario based on hand gestures. We study how the swarm can incrementally learn hand gestures through the interaction with a human instructor providing training gestures and correction feedback. The main contribution of the paper is a novel incremental machine learning approach that makes the robot swarm learn and recognize the gestures in a distributed and decentralized fashion using binary (i.e., yes/no) feedback. It exploits cooperative information exchange and swarm's intrinsic parallelism and redundancy. We perform extensive tests using real gesture images, showing that good classification accuracies are obtained even with rather few training samples and relatively small swarms. We also show the good scalability of the approach and its relatively low requirements in terms of communication overhead.

We present a communication based navigation algorithm for robotic swarms. It lets robots guide each other's navigation by exchanging messages containing navigation information through the wireless network formed among the swarm. We study the use of this algorithm in two different scenarios. In the first scenario, the swarm guides a single robot to a target, while in the second, all robots of the swarm navigate back and forth between two targets. In both cases, the algorithm provides efficient navigation, while being robust to failures of robots in the swarm. Moreover, we show that in the latter case, the system lets the swarm self-organize into a robust dynamic structure. This self-organization further improves navigation efficiency, and is able to find shortest paths in cluttered environments. We test our system both in simulation and on real robots.

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.

We study how a swarm robotic system consisting of two different types of robots can solve a foraging task. The first type of robots are small wheeled robots, called foot-bots, and the second type are flying robots that can attach to the ceiling, called eye-bots. While the foot-bots perform the actual foraging, i.e. they move back and forth between a source and a target location, the eye-bots are deployed in stationary positions against the ceiling, with the goal of guiding the foot-bots. The key component of our approach is a process of mutual adaptation, in which foot-bots execute instructions given by eye-bots, and eye-bots observe the behavior of foot-bots to adapt the instructions they give. Through a simulation study, we show that this process allows the system to find a path for foraging in a cluttered environment. Moreover, it is able to converge onto the shortest of two paths, and spread over different paths in case of congestion. Since our approach involves mutual adaptation between two sub-swarms of different robots, we refer to it as cooperative self-organization. This is to our knowledge the first work that investigates such a system in swarm robotics.

We propose a new approach to integrate proactive and reactive routing in mobile ad hoc networks. Our work deploys a lightweight proactive algorithm that runs in the background offering a basic routing service, and a reactive algorithm that can be called on demand offering a connection-oriented service. The reactive algorithm uses the routing information from the proactive algorithm in its working, so that there is a synergy between the two parts of the system. An important property of our system is that it allows the choice between proactive and reactive routing to be made for each session individually, by their source nodes. This gives network nodes a very fine-grained level of control over the routing process, and allows them to exploit the complementary properties of proactive and reactive routing, e.g. by matching the choice of the routing approach to the needs of individual sessions. In a range of simulation tests, we evaluate the validity of our approach.

We study through simulation the performance of two swarm intelligence MANET routing algorithms in a realistic urban environment. The two algorithms, ANSI and AntHocNet, implement the swarm intelligence paradigm for routing in different ways: while ANSI applies a reactive approach in which ants are only sent out when no route is available between the source and destination of a communication session, AntHocNet integrates reactive and proactive mechanisms whereby the algorithm sends out ants at regular intervals during the entire duration of running sessions in order to continuously adapt and improve existing routes. The two swarm intelligence routing algorithms are compared to AODV, a state-of-the-art reactive algorithm, and OLSR, a state-of-the-art proactive algorithm. Our objective is to investigate the usefulness of the different approaches adopted by the algorithms when confronted with the peculiarities of urban environments and the requirements of real-world applications. At this aim we define a detailed and realistic simulation setup. We model node mobility by limiting node movements to the streets and open spaces of town, use a ray-tracing approach to model the propagation of radio waves, and investigate different kinds of interactive data traffic patterns, ranging from SMS messaging to VoIP communications.

Recent developments in information technology have brought about important changes in distributed computing. New environments such as massively large-scale, wide-area computer networks and mobile ad hoc networks have emerged. Common characteristics of these environments include extreme dynamicity, unreliability, and large scale. Traditional approaches to designing distributed applications in these environments based on central control, small scale, or strong reliability assumptions are not suitable for exploiting their enormous potential. Based on the observation that living organisms can effectively organize large numbers of unreliable and dynamically-changing components (cells, molecules, individuals, etc.) into robust and adaptive structures, it has long been a research challenge to characterize the key ideas and mechanisms that make biological systems work and to apply them to distributed systems engineering. In this article we propose a conceptual framework that captures several basic biological processes in the form of a family of design patterns. Examples include plain diffusion, replication, chemotaxis, and stigmergy. We show through examples how to implement important functions for distributed computing based on these patterns. Using a common evaluation methodology, we show that our bio-inspired solutions have performance comparable to traditional, state-of-the-art solutions while they inherit desirable properties of biological systems including adaptivity and robustness.

We present AntNet-QoS, a novel approach that taking inspiration from the Ant Colony Optimization (ACO) framework, and in particular from the AntNet routing algorithm, allows dynamic scheduling, forwarding and link sharing among multiple packet classes in a differentiated service (DiffServ) network.

Mobile ad hoc networks are communication networks built up of a collection of mobile devices, which can communicate through wireless connections. Routing is the task of directing data packets from a source node to a given destination. This task is particularly hard in mobile ad hoc networks: due to the mobility of the network elements and the lack of central control, routing algorithms should be robust, adaptive, and work in a decentralized and self-organizing way. In this paper, we describe an algorithm, which draws inspiration from swarm intelligence to obtain these characteristics. More specifically, we borrow ideas from ant colonies and from the ant colony optimization framework. In an extensive set of simulation tests, we compare our routing algorithm with a state-of-the-art algorithm, and show that it gets better performance over a wide range of different scenarios and for a number of different evaluation measures. In particular, we show that it scales better with the number of nodes in the network.

This paper describes AntHocNet, an algorithm for routing in mobile ad-hoc networks based on ideas from the ant colony optimisation framework. In AntHocNet a source node reactively sets up a path to a destination node at the start of each communication session. During the course of the session, the source node uses ant agents to proactively search for alternatives and improvements of the original path. This allows to adapt to changes in the network, and to construct a mesh of alternative paths between source and destination. The proactive behaviour is supported by a lightweight information bootstrapping process. Paths are represented in the form of distance-vector routing tables called pheromone tables. An entry of a pheromone table contains the estimated goodness of going over a certain neighbour to reach a certain destination. Data are routed stochastically over the different paths of the mesh according to these goodness estimates. In an extensive set of simulation tests, we compare AntHocNet to AODV, a reactive algorithm which is an important reference in this research area. We show that AntHocNet can outperform AODV for different evaluation criteria in a wide range of different scenarios. AntHocNet is also shown to scale well with respect to the number of nodes.

Recently, a number of algorithms inspired by the foraging behavior of ant colonies have been applied to the solution of difficult discrete optimization problems. We put these algorithms in a common framework by defining the Ant Colony Optimization (ACO) meta-heuristic. A couple of paradigmatic examples of applications of these novel meta-heuristic are given, as well as a brief overview of existing applications.

This article presents an overview of recent work on ant algorithms, that is, algorithms for discrete optimization that took inspiration from the observation of ant colonies' foraging behavior, and introduces the ant colony optimization (ACO) metaheuristic. In the first part of the article the basic biological findings on real ants are reviewed and their artificial counterparts as well as the ACO metaheuristic are defined. In the second part of the article a number of applications of ACO algorithms to combinatorial optimization and routing in communications networks are described. We conclude with a discussion of related work and of some of the most important aspects of the ACO metaheuristic.

This paper introduces AntNet, a novel approach to the adaptive learning of routing tables in communications networks. AntNet is a distributed, mobile agents based Monte Carlo system that was inspired by recent work on the ant colony metaphor for solving optimization problems. AntNet's agents concurrently explore the network and exchange collected information. The communication among the agents is indirect and asynchronous, mediated by the network itself. This form of communication is typical of social insects and is called stigmergy. We compare our algorithm with six state-of-the-art routing algorithms coming from the telecommunications and machine learning fields. The algorithms' performance is evaluated over a set of realistic testbeds. We run many experiments over real and artificial IP datagram networks with increasing number of nodes and under several paradigmatic spatial and temporal traffic distributions. Results are very encouraging. AntNet showed superior performance under all the experimental conditions with respect to its competitors. We analyze the main characteristics of the algorithm and try to explain the reasons for its superiority.

This paper deals with the development of a navigation system for a mobile robot designed to accomplish missions inside the building of big organisations, such as hospitals or malls. Useful terminology is first introduced, and followed by a rather detailed presentation of the architecture and overall functioning of the system. The most recent realization of the system is then described in some detail, and its behaviours and functionalities are reported and discussed. Possible directions for future work are outlined, along with open problems that are regarded as the most interesting and challenging for the further development of the system.>