332 research outputs found
Decentralized Connectivity-Preserving Deployment of Large-Scale Robot Swarms
We present a decentralized and scalable approach for deployment of a robot
swarm. Our approach tackles scenarios in which the swarm must reach multiple
spatially distributed targets, and enforce the constraint that the robot
network cannot be split. The basic idea behind our work is to construct a
logical tree topology over the physical network formed by the robots. The
logical tree acts as a backbone used by robots to enforce connectivity
constraints. We study and compare two algorithms to form the logical tree:
outwards and inwards. These algorithms differ in the order in which the robots
join the tree: the outwards algorithm starts at the tree root and grows towards
the targets, while the inwards algorithm proceeds in the opposite manner. Both
algorithms perform periodic reconfiguration, to prevent suboptimal topologies
from halting the growth of the tree. Our contributions are (i) The formulation
of the two algorithms; (ii) A comparison of the algorithms in extensive
physics-based simulations; (iii) A validation of our findings through
real-robot experiments.Comment: 8 pages, 8 figures, submitted to IROS 201
Swarm Relays: Distributed Self-Healing Ground-and-Air Connectivity Chains
The coordination of robot swarms - large decentralized teams of robots -
generally relies on robust and efficient inter-robot communication. Maintaining
communication between robots is particularly challenging in field deployments.
Unstructured environments, limited computational resources, low bandwidth, and
robot failures all contribute to the complexity of connectivity maintenance. In
this paper, we propose a novel lightweight algorithm to navigate a group of
robots in complex environments while maintaining connectivity by building a
chain of robots. The algorithm is robust to single robot failures and can heal
broken communication links. The algorithm works in 3D environments: when a
region is unreachable by wheeled robots, the chain is extended with flying
robots. We test the performance of the algorithm using up to 100 robots in a
physics-based simulator with three mazes and different robot failure scenarios.
We then validate the algorithm with physical platforms: 7 wheeled robots and 6
flying ones, in homogeneous and heterogeneous scenarios.Comment: 9 pages, 8 figures, Accepted for publication in Robotics and
Automation Letters (RAL
Robotic Wireless Sensor Networks
In this chapter, we present a literature survey of an emerging, cutting-edge,
and multi-disciplinary field of research at the intersection of Robotics and
Wireless Sensor Networks (WSN) which we refer to as Robotic Wireless Sensor
Networks (RWSN). We define a RWSN as an autonomous networked multi-robot system
that aims to achieve certain sensing goals while meeting and maintaining
certain communication performance requirements, through cooperative control,
learning and adaptation. While both of the component areas, i.e., Robotics and
WSN, are very well-known and well-explored, there exist a whole set of new
opportunities and research directions at the intersection of these two fields
which are relatively or even completely unexplored. One such example would be
the use of a set of robotic routers to set up a temporary communication path
between a sender and a receiver that uses the controlled mobility to the
advantage of packet routing. We find that there exist only a limited number of
articles to be directly categorized as RWSN related works whereas there exist a
range of articles in the robotics and the WSN literature that are also relevant
to this new field of research. To connect the dots, we first identify the core
problems and research trends related to RWSN such as connectivity,
localization, routing, and robust flow of information. Next, we classify the
existing research on RWSN as well as the relevant state-of-the-arts from
robotics and WSN community according to the problems and trends identified in
the first step. Lastly, we analyze what is missing in the existing literature,
and identify topics that require more research attention in the future
Distributed Robotic Systems in the Edge-Cloud Continuum with ROS 2: a Review on Novel Architectures and Technology Readiness
Robotic systems are more connected, networked, and distributed than ever. New
architectures that comply with the \textit{de facto} robotics middleware
standard, ROS\,2, have recently emerged to fill the gap in terms of hybrid
systems deployed from edge to cloud. This paper reviews new architectures and
technologies that enable containerized robotic applications to seamlessly run
at the edge or in the cloud. We also overview systems that include solutions
from extension to ROS\,2 tooling to the integration of Kubernetes and ROS\,2.
Another important trend is robot learning, and how new simulators and cloud
simulations are enabling, e.g., large-scale reinforcement learning or
distributed federated learning solutions. This has also enabled deeper
integration of continuous interaction and continuous deployment (CI/CD)
pipelines for robotic systems development, going beyond standard software unit
tests with simulated tests to build and validate code automatically. We discuss
the current technology readiness and list the potential new application
scenarios that are becoming available. Finally, we discuss the current
challenges in distributed robotic systems and list open research questions in
the field
Adaptive Navigation Control for Swarms of Autonomous Mobile Robots
This paper was devoted to developing a new and general coordinated adaptive navigation scheme for large-scale mobile robot swarms adapting to geographically constrained environments. Our distributed solution approach was built on the following assumptions: anonymity, disagreement on common coordinate systems, no pre-selected leader, and no direct communication. The proposed adaptive navigation was largely composed of four functions, commonly relying on dynamic neighbor selection and local interaction. When each robot found itself what situation it was in, individual appropriate ranges for neighbor selection were defined within its limited sensing boundary and the robots properly selected their neighbors in the limited range. Through local interactions with the neighbors, each robot could maintain a uniform distance to its neighbors, and adapt their direction of heading and geometric shape. More specifically, under the proposed adaptive navigation, a group of robots could be trapped in a dead-end passage,but they merge with an adjacent group to emergently escape from the dead-end passage. Furthermore, we verified the effectiveness of the proposed strategy using our in-housesimulator. The simulation results clearly demonstrated that the proposed algorithm is a simple yet robust approach to autonomous navigation of robot swarms in highlyclutteredenvironments. Since our algorithm is local and completely scalable to any size, it is easily implementable on a wide variety of resource-constrained mobile robots andplatforms. Our adaptive navigation control for mobile robot swarms is expected to be used in many applications ranging from examination and assessment of hazardous environments to domestic applications
Behavior Mixing with Minimum Global and Subgroup Connectivity Maintenance for Large-Scale Multi-Robot Systems
In many cases the multi-robot systems are desired to execute simultaneously
multiple behaviors with different controllers, and sequences of behaviors in
real time, which we call \textit{behavior mixing}. Behavior mixing is
accomplished when different subgroups of the overall robot team change their
controllers to collectively achieve given tasks while maintaining connectivity
within and across subgroups in one connected communication graph. In this
paper, we present a provably minimum connectivity maintenance framework to
ensure the subgroups and overall robot team stay connected at all times while
providing the highest freedom for behavior mixing. In particular, we propose a
real-time distributed Minimum Connectivity Constraint Spanning Tree (MCCST)
algorithm to select the minimum inter-robot connectivity constraints preserving
subgroup and global connectivity that are \textit{least likely to be violated}
by the original controllers. With the employed safety and connectivity barrier
certificates for the activated connectivity constraints and collision
avoidance, the behavior mixing controllers are thus minimally modified from the
original controllers. We demonstrate the effectiveness and scalability of our
approach via simulations of up to 100 robots with multiple behaviors.Comment: To appear in Proceedings of IEEE International Conference on Robotics
and Automation (ICRA) 202
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