7 research outputs found
The landscape of Collective Awareness in multi-robot systems
The development of collective-aware multi-robot systems is crucial for
enhancing the efficiency and robustness of robotic applications in multiple
fields. These systems enable collaboration, coordination, and resource sharing
among robots, leading to improved scalability, adaptability to dynamic
environments, and increased overall system robustness. In this work, we want to
provide a brief overview of this research topic and identify open challenges.Comment: Submitted to workshop titled "Designing Aware Robots: The EIC
Pathfinder Challenge - Explore Awareness Inside" at the European Robotics
Forum 202
Aerostack2: A Software Framework for Developing Multi-robot Aerial Systems
In recent years, the robotics community has witnessed the development of
several software stacks for ground and articulated robots, such as Navigation2
and MoveIt. However, the same level of collaboration and standardization is yet
to be achieved in the field of aerial robotics, where each research group has
developed their own frameworks. This work presents Aerostack2, a framework for
the development of autonomous aerial robotics systems that aims to address the
lack of standardization and fragmentation of efforts in the field. Built on ROS
2 middleware and featuring an efficient modular software architecture and
multi-robot orientation, Aerostack2 is a versatile and platform-independent
environment that covers a wide range of robot capabilities for autonomous
operation. Its major contributions include providing a logical level for
specifying missions, reusing components and sub-systems for aerial robotics,
and enabling the development of complete control architectures. All major
contributions have been tested in simulation and real flights with multiple
heterogeneous swarms. Aerostack2 is open source and community oriented,
democratizing the access to its technology by autonomous drone systems
developers
Multi S-graphs: A Collaborative Semantic SLAM architecture
peer reviewedCollaborative Simultaneous Localization and Mapping (CSLAM) is a critical
capability for enabling multiple robots to operate in complex environments.
Most CSLAM techniques rely on the transmission of low-level features for visual
and LiDAR-based approaches, which are used for pose graph optimization.
However, these low-level features can lead to incorrect loop closures,
negatively impacting map generation.Recent approaches have proposed the use of
high-level semantic information in the form of Hierarchical Semantic Graphs to
improve the loop closure procedures and overall precision of SLAM algorithms.
In this work, we present Multi S-Graphs, an S-graphs [1] based distributed
CSLAM algorithm that utilizes high-level semantic information for cooperative
map generation while minimizing the amount of information exchanged between
robots. Experimental results demonstrate the promising performance of the
proposed algorithm in map generation tasks
Multi S-Graphs: an Efficient Real-time Distributed Semantic-Relational Collaborative SLAM
Collaborative Simultaneous Localization and Mapping (CSLAM) is critical to
enable multiple robots to operate in complex environments. Most CSLAM
techniques rely on raw sensor measurement or low-level features such as
keyframe descriptors, which can lead to wrong loop closures due to the lack of
deep understanding of the environment. Moreover, the exchange of these
measurements and low-level features among the robots requires the transmission
of a significant amount of data, which limits the scalability of the system. To
overcome these limitations, we present Multi S-Graphs, a decentralized CSLAM
system that utilizes high-level semantic-relational information embedded in the
four-layered hierarchical and optimizable situational graphs for cooperative
map generation and localization while minimizing the information exchanged
between the robots. To support this, we present a novel room-based descriptor
which, along with its connected walls, is used to perform inter-robot loop
closures, addressing the challenges of multi-robot kidnapped problem
initialization. Multiple experiments in simulated and real environments
validate the improvement in accuracy and robustness of the proposed approach
while reducing the amount of data exchanged between robots compared to other
state-of-the-art approaches.
Software available within a docker image:
https://github.com/snt-arg/multi_s_graphs_docke
Bridging the Gap between Simulation and Real Autonomous UAV Flights in Industrial Applications
The utilization of autonomous unmanned aerial vehicles (UAVs) has increased rapidly due to their ability to perform a variety of tasks, including industrial inspection. Conducting testing with actual flights within industrial facilities proves to be both expensive and hazardous, posing risks to the system, the facilities, and their personnel. This paper presents an innovative and reliable methodology for developing such applications, ensuring safety and efficiency throughout the process. It involves a staged transition from simulation to reality, wherein various components are validated at each stage. This iterative approach facilitates error identification and resolution, enabling subsequent real flights to be conducted with enhanced safety after validating the remainder of the system. Furthermore, this article showcases two use cases: wind turbine inspection and photovoltaic plant inspection. By implementing the suggested methodology, these applications were successfully developed in an efficient and secure manner