End-to-End Design for Self-Reconfigurable Heterogeneous Robotic Swarms

More widespread adoption requires swarms of robots to be more flexible for real-world applications. Multiple challenges remain in complex scenarios where a large amount of data needs to be processed in real-time and high degrees of situational awareness are required. The options in this direction are limited in existing robotic swarms, mostly homogeneous robots with limited operational and reconfiguration flexibility. We address this by bringing elastic computing techniques and dynamic resource management from the edge-cloud computing domain to the swarm robotics domain. This enables the dynamic provisioning of collective capabilities in the swarm for different applications. Therefore, we transform a swarm into a distributed sensing and computing platform capable of complex data processing tasks, which can then be offered as a service. In particular, we discuss how this can be applied to adaptive resource management in a heterogeneous swarm of drones, and how we are implementing the dynamic deployment of distributed data processing algorithms. With an elastic drone swarm built on reconfigurable hardware and containerized services, it will be possible to raise the self-awareness, degree of intelligence, and level of autonomy of heterogeneous swarms of robots. We describe novel directions for collaborative perception, and new ways of interacting with a robotic swarm

Efficient Dispersion of Mobile Robots on Graphs

The dispersion problem on graphs requires $k$ robots placed arbitrarily at the $n$ nodes of an anonymous graph, where $k \leq n$, to coordinate with each other to reach a final configuration in which each robot is at a distinct node of the graph. The dispersion problem is important due to its relationship to graph exploration by mobile robots, scattering on a graph, and load balancing on a graph. In addition, an intrinsic application of dispersion has been shown to be the relocation of self-driven electric cars (robots) to recharge stations (nodes). We propose three efficient algorithms to solve dispersion on graphs. Our algorithms require $O(k \log \Delta)$ bits at each robot, and $O(m)$ steps running time, where $m$ is the number of edges and $\Delta$ is the degree of the graph. The algorithms differ in whether they address the synchronous or the asynchronous system model, and in what, where, and how data structures are maintained.Comment: 14 page

Adaptive Algorithms for Coverage Control and Space Partitioning in Mobile Robotic Networks

This paper considers deployment problems where a mobile robotic network must optimize its configuration in a distributed way in order to minimize a steady-state cost function that depends on the spatial distribution of certain probabilistic events of interest. Moreover, it is assumed that the event location distribution is a priori unknown, and can only be progressively inferred from the observation of the actual event occurrences. Three classes of problems are discussed in detail: coverage control problems, spatial partitioning problems, and dynamic vehicle routing problems. In each case, distributed stochastic gradient algorithms optimizing the performance objective are presented. The stochastic gradient view simplifies and generalizes previously proposed solutions, and is applicable to new complex scenarios, such as adaptive coverage involving heterogeneous agents. Remarkably, these algorithms often take the form of simple distributed rules that could be implemented on resource-limited platforms.Comment: 16 pages, 4 figures. Long version of a manuscript to appear in the Transactions on Automatic Contro

Artificial Intelligence-Based Techniques for Emerging Robotics Communication: A Survey and Future Perspectives

This paper reviews the current development of artificial intelligence (AI) techniques for the application area of robot communication. The study of the control and operation of multiple robots collaboratively toward a common goal is fast growing. Communication among members of a robot team and even including humans is becoming essential in many real-world applications. The survey focuses on the AI techniques for robot communication to enhance the communication capability of the multi-robot team, making more complex activities, taking an appreciated decision, taking coordinated action, and performing their tasks efficiently.Comment: 11 pages, 6 figure

Network Functions Virtualization Architecture for Gateways for Virtualized Wireless Sensor and Actuator Networks

Virtualization enables multiple applications to share the same wireless sensor and actuator network (WSAN). However, in heterogeneous environments, virtualized wireless sensor and actuator networks (VWSAN) raise new challenges, such as the need for on-the-fly, dynamic, elastic, and scalable provisioning of gateways. Network Functions Virtualization (NFV) is a paradigm emerging to help tackle these new challenges. It leverages standard virtualization technology to consolidate special-purpose network elements on commodity hardware. This article presents NFV architecture for VWSAN gateways, in which software instances of gateway modules are hosted in NFV infrastructure operated and managed by a VWSAN gateway provider. We consider several VWSAN providers, each with its own brand or combination of brands of sensors and actuators/robots. These sensors and actuators can be accessed by a variety of applications, each may have different interface and QoS (i.e., latency, throughput, etc.) requirements. The NFV infrastructure allows dynamic, elastic, and scalable deployment of gateway modules in this heterogeneous VWSAN environment. Furthermore, the proposed architecture is flexible enough to easily allow new sensors and actuators integration and new application domains accommodation. We present a prototype that is built using the OpenStack platform. Besides, the performance results are discusse

A Fog Robotics Approach to Deep Robot Learning: Application to Object Recognition and Grasp Planning in Surface Decluttering

The growing demand of industrial, automotive and service robots presents a challenge to the centralized Cloud Robotics model in terms of privacy, security, latency, bandwidth, and reliability. In this paper, we present a `Fog Robotics' approach to deep robot learning that distributes compute, storage and networking resources between the Cloud and the Edge in a federated manner. Deep models are trained on non-private (public) synthetic images in the Cloud; the models are adapted to the private real images of the environment at the Edge within a trusted network and subsequently, deployed as a service for low-latency and secure inference/prediction for other robots in the network. We apply this approach to surface decluttering, where a mobile robot picks and sorts objects from a cluttered floor by learning a deep object recognition and a grasp planning model. Experiments suggest that Fog Robotics can improve performance by sim-to-real domain adaptation in comparison to exclusively using Cloud or Edge resources, while reducing the inference cycle time by 4\times to successfully declutter 86% of objects over 213 attempts.Comment: IEEE International Conference on Robotics and Automation, ICRA, 201

AutoSOS: Towards Multi-UAV Systems Supporting Maritime Search and Rescue with Lightweight AI and Edge Computing

Rescue vessels are the main actors in maritime safety and rescue operations. At the same time, aerial drones bring a significant advantage into this scenario. This paper presents the research directions of the AutoSOS project, where we work in the development of an autonomous multi-robot search and rescue assistance platform capable of sensor fusion and object detection in embedded devices using novel lightweight AI models. The platform is meant to perform reconnaissance missions for initial assessment of the environment using novel adaptive deep learning algorithms that efficiently use the available sensors and computational resources on drones and rescue vessel. When drones find potential objects, they will send their sensor data to the vessel to verity the findings with increased accuracy. The actual rescue and treatment operation are left as the responsibility of the rescue personnel. The drones will autonomously reconfigure their spatial distribution to enable multi-hop communication, when a direct connection between a drone transmitting information and the vessel is unavailable

Graph Neural Networks for Learning Robot Team Coordination

This paper shows how Graph Neural Networks can be used for learning distributed coordination mechanisms in connected teams of robots. We capture the relational aspect of robot coordination by modeling the robot team as a graph, where each robot is a node, and edges represent communication links. During training, robots learn how to pass messages and update internal states, so that a target behavior is reached. As a proxy for more complex problems, this short paper considers the problem where each robot must locally estimate the algebraic connectivity of the team's network topology.Comment: Presented at the Federated AI for Robotics Workshop, IJCAI-ECAI/ICML/AAMAS 201

AI Challenges in Human-Robot Cognitive Teaming

Among the many anticipated roles for robots in the future is that of being a human teammate. Aside from all the technological hurdles that have to be overcome with respect to hardware and control to make robots fit to work with humans, the added complication here is that humans have many conscious and subconscious expectations of their teammates - indeed, we argue that teaming is mostly a cognitive rather than physical coordination activity. This introduces new challenges for the AI and robotics community and requires fundamental changes to the traditional approach to the design of autonomy. With this in mind, we propose an update to the classical view of the intelligent agent architecture, highlighting the requirements for mental modeling of the human in the deliberative process of the autonomous agent. In this article, we outline briefly the recent efforts of ours, and others in the community, towards developing cognitive teammates along these guidelines

Workspace Partitioning and Topology Discovery Algorithms for Heterogeneous Multi-Agent Networks

In this paper, we consider a class of workspace partitioning problems that arise in the context of area coverage and spatial load balancing for spatially distributed heterogeneous multi-agent networks. It is assumed that each agent has certain directions of motion or directions for sensing and exploration that are more preferable than others. These preferences are measured by means of convex and anisotropic (direction-dependent) quadratic proximity metrics which are, in general, different for each agent. These proximity metrics induce Voronoi-like partitions of the network's workspace that are comprised of cells which may not always be convex (or even connected) sets but are necessarily contained in ellipsoids that are known to their corresponding agents. The main contributions of this work are 1) a distributed algorithm for the computation of a Voronoi-like partition of the workspace of a heterogeneous multi-agent network and 2) a systematic process to discover the network topology induced by the latter Voronoi-like partition. Numerical simulations that illustrate the efficacy of the proposed algorithms are also presented.Comment: 20 pages, 8 figure