250 research outputs found
Fast Biconnectivity Restoration in Multi-Robot Systems for Robust Communication Maintenance
Maintaining a robust communication network plays an important role in the
success of a multi-robot team jointly performing an optimization task. A key
characteristic of a robust multi-robot system is the ability to repair the
communication topology itself in the case of robot failure. In this paper, we
focus on the Fast Biconnectivity Restoration (FBR) problem, which aims to
repair a connected network to make it biconnected as fast as possible, where a
biconnected network is a communication topology that cannot be disconnected by
removing one node. We develop a Quadratically Constrained Program (QCP)
formulation of the FBR problem, which provides a way to optimally solve the
problem. We also propose an approximation algorithm for the FBR problem based
on graph theory. By conducting empirical studies, we demonstrate that our
proposed approximation algorithm performs close to the optimal while
significantly outperforming the existing solutions
Robust area coverage with connectivity maintenance
Robot swarms herald the ability to solve complex tasks using a large collection of simple devices. However, engineering a robotic swarm is far from trivial, with a major hurdle being the definition of the control laws leading to the desired globally coordinated behavior. Communication is a key element for coordination and it is considered one of the current most important challenges for swarm robotics. In this paper, we study the problem of maintaining robust swarm connectivity while performing a coverage task based on the Voronoi tessellation of an area of interest. We implement our methodology in a team of eight Khepera IV robots. With the assumptions that robots have a limited sensing and communication range - and cannot rely on centralized processing - we propose a tri-objective control law that outperforms other simpler strategies (e.g. a potential-based coverage) in terms of network connectivity, robustness to failure, and area coverage
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
OptSample: A Resilient Buffer Management Policy for Robotic Systems based on Optimal Message Sampling
Modern robotic systems have become an alternative to humans to perform risky
or exhausting tasks. In such application scenarios, communications between
robots and the control center have become one of the major problems. Buffering
is a commonly used solution to relieve temporary network disruption. But the
assumption that newer messages are more valuable than older ones is not true
for many application scenarios such as explorations, rescue operations, and
surveillance. In this paper, we proposed a novel resilient buffer management
policy named OptSample. It can uniformly sampling messages and dynamically
adjust the sample rate based on run-time network situation. We define an
evaluation function to estimate the profit of a message sequence. Based on the
function, our analysis and simulation shows that the OptSample policy can
effectively prevent losing long segment of continuous messages and improve the
overall profit of the received messages. We implement the proposed policy in
ROS. The implementation is transparent to user and no user code need to be
changed. Experimental results on several application scenarios show that the
OptSample policy can help robotic systems be more resilient against network
disruption
Collaborative autonomy in heterogeneous multi-robot systems
As autonomous mobile robots become increasingly connected and widely deployed in different domains, managing multiple robots and their interaction is key to the future of ubiquitous autonomous systems. Indeed, robots are not individual entities anymore. Instead, many robots today are deployed as part of larger fleets or in teams. The benefits of multirobot collaboration, specially in heterogeneous groups, are multiple. Significantly higher degrees of situational awareness and understanding of their environment can be achieved when robots with different operational capabilities are deployed together. Examples of this include the Perseverance rover and the Ingenuity helicopter that NASA has deployed in Mars, or the highly heterogeneous robot teams that explored caves and other complex environments during the last DARPA Sub-T competition.
This thesis delves into the wide topic of collaborative autonomy in multi-robot systems, encompassing some of the key elements required for achieving robust collaboration: solving collaborative decision-making problems; securing their operation, management and interaction; providing means for autonomous coordination in space and accurate global or relative state estimation; and achieving collaborative situational awareness through distributed perception and cooperative planning. The thesis covers novel formation control algorithms, and new ways to achieve accurate absolute or relative localization within multi-robot systems. It also explores the potential of distributed ledger technologies as an underlying framework to achieve collaborative decision-making in distributed robotic systems.
Throughout the thesis, I introduce novel approaches to utilizing cryptographic elements and blockchain technology for securing the operation of autonomous robots, showing that sensor data and mission instructions can be validated in an end-to-end manner. I then shift the focus to localization and coordination, studying ultra-wideband (UWB) radios and their potential. I show how UWB-based ranging and localization can enable aerial robots to operate in GNSS-denied environments, with a study of the constraints and limitations. I also study the potential of UWB-based relative localization between aerial and ground robots for more accurate positioning in areas where GNSS signals degrade. In terms of coordination, I introduce two new algorithms for formation control that require zero to minimal communication, if enough degree of awareness of neighbor robots is available. These algorithms are validated in simulation and real-world experiments. The thesis concludes with the integration of a new approach to cooperative path planning algorithms and UWB-based relative localization for dense scene reconstruction using lidar and vision sensors in ground and aerial robots
Network connectivity tracking for a team of unmanned aerial vehicles
Algebraic connectivity is the second-smallest eigenvalue of the Laplacian matrix and can be used as a metric for the robustness and efficiency of a network. This connectivity concept applies to teams of multiple unmanned aerial vehicles (UAVs) performing cooperative tasks, such as arriving at a consensus. As a UAV team completes its mission, it often needs to control the network connectivity. The algebraic connectivity can be controlled by altering edge weights through movement of individual UAVs in the team, or by adding and deleting edges. The addition and deletion problem for algebraic connectivity, however, is NP-hard. The contributions of this work are 1) a comparison of four heuristic methods for modifying algebraic connectivity through the addition and deletion of edges, 2) a rule-based algorithm for tracking a connectivity profile through edge weight modification and the addition and deletion of edges, 3) a new, hybrid method for selecting the best edge to add or remove, 4) a distributed method for estimating the eigenvectors of the Laplacian matrix and selecting the best edge to add or remove for connectivity modification and tracking, and 5) an implementation of the distributed connectivity tracking using a consensus controller and double-integrator dynamics
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