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

Late-breaking results on Multi S-graphs: A Collaborative Semantic SLAM architecture

peer reviewe

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