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

Cooperative heterogeneous robots for autonomous insects trap monitoring system in a precision agriculture scenario

The recent advances in precision agriculture are due to the emergence of modern robotics systems. For instance, unmanned aerial systems (UASs) give new possibilities that advance the solution of existing problems in this area in many different aspects. The reason is due to these platforms’ ability to perform activities at varying levels of complexity. Therefore, this research presents a multiple-cooperative robot solution for UAS and unmanned ground vehicle (UGV) systems for their joint inspection of olive grove inspect traps. This work evaluated the UAS and UGV vision-based navigation based on a yellow fly trap fixed in the trees to provide visual position data using the You Only Look Once (YOLO) algorithms. The experimental setup evaluated the fuzzy control algorithm applied to the UAS to make it reach the trap efficiently. Experimental tests were conducted in a realistic simulation environment using a robot operating system (ROS) and CoppeliaSim platforms to verify the methodology’s performance, and all tests considered specific real-world environmental conditions. A search and landing algorithm based on augmented reality tag (AR-Tag) visual processing was evaluated to allow for the return and landing of the UAS to the UGV base. The outcomes obtained in this work demonstrate the robustness and feasibility of the multiple-cooperative robot architecture for UGVs and UASs applied in the olive inspection scenario.The authors would like to thank the Foundation for Science and Technology (FCT, Portugal) for financial support through national funds FCT/MCTES (PIDDAC) to CeDRI (UIDB/05757/2020 and UIDP/05757/2020) and SusTEC (LA/P/0007/2021). In addition, the authors would like to thank the following Brazilian Agencies CEFET-RJ, CAPES, CNPq, and FAPERJ. In addition, the authors also want to thank the Research Centre in Digitalization and Intelligent Robotics (CeDRI), Instituto Politécnico de Braganca (IPB) - Campus de Santa Apolonia, Portugal, Laboratório Associado para a Sustentabilidade e Tecnologia em Regiões de Montanha (SusTEC), Portugal, INESC Technology and Science - Porto, Portugal and Universidade de Trás-os-Montes e Alto Douro - Vila Real, Portugal. This work was carried out under the Project “OleaChain: Competências para a sustentabilidade e inovação da cadeia de valor do olival tradicional no Norte Interior de Portugal” (NORTE-06-3559-FSE-000188), an operation used to hire highly qualified human resources, funded by NORTE 2020 through the European Social Fund (ESF).info:eu-repo/semantics/publishedVersio

OBJECT PERCEPTION IN UNDERWATER ENVIRONMENTS: A SURVEY ON SENSORS AND SENSING METHODOLOGIES

Underwater robots play a critical role in the marine industry. Object perception is the foundation for the automatic operations of submerged vehicles in dynamic aquatic environments. However, underwater perception encounters multiple environmental challenges, including rapid light attenuation, light refraction, or backscattering effect. These problems reduce the sensing devices’ signal-to-noise ratio (SNR), making underwater perception a complicated research topic. This paper describes the state-of-the-art sensing technologies and object perception techniques for underwater robots in different environmental conditions. Due to the current sensing modalities’ various constraints and characteristics, we divide the perception ranges into close-range, medium-range, and long-range. We survey and describe recent advances for each perception range and suggest some potential future research directions worthy of investigating in this field

UAV or Drones for Remote Sensing Applications in GPS/GNSS Enabled and GPS/GNSS Denied Environments

The design of novel UAV systems and the use of UAV platforms integrated with robotic sensing and imaging techniques, as well as the development of processing workflows and the capacity of ultra-high temporal and spatial resolution data, have enabled a rapid uptake of UAVs and drones across several industries and application domains.This book provides a forum for high-quality peer-reviewed papers that broaden awareness and understanding of single- and multiple-UAV developments for remote sensing applications, and associated developments in sensor technology, data processing and communications, and UAV system design and sensing capabilities in GPS-enabled and, more broadly, Global Navigation Satellite System (GNSS)-enabled and GPS/GNSS-denied environments.Contributions include:UAV-based photogrammetry, laser scanning, multispectral imaging, hyperspectral imaging, and thermal imaging;UAV sensor applications; spatial ecology; pest detection; reef; forestry; volcanology; precision agriculture wildlife species tracking; search and rescue; target tracking; atmosphere monitoring; chemical, biological, and natural disaster phenomena; fire prevention, flood prevention; volcanic monitoring; pollution monitoring; microclimates; and land use;Wildlife and target detection and recognition from UAV imagery using deep learning and machine learning techniques;UAV-based change detection

Intelligent strategies for mobile robotics in laboratory automation

In this thesis a new intelligent framework is presented for the mobile robots in laboratory automation, which includes: a new multi-floor indoor navigation method is presented and an intelligent multi-floor path planning is proposed; a new signal filtering method is presented for the robots to forecast their indoor coordinates; a new human feature based strategy is proposed for the robot-human smart collision avoidance; a new robot power forecasting method is proposed to decide a distributed transportation task; a new blind approach is presented for the arm manipulations for the robots

Autonomous Navigation of Distributed Spacecraft using Graph-based SLAM for Proximity Operations in Small Celestial Bodies

Establishment of a sustainable human presence beyond the cislunar space is a major milestone for mankind. Small celestial bodies (SCBs) like asteroids are known to contain valuable natural resources necessary for the development of space assets essential to the accomplishment of this goal. Consequently, future robotic spacecraft missions to SCBs are envisioned with the objective of commercial in-situ resource utilization (ISRU). In mission design, there is also an increasing interest in the utilization of the distributed spacecraft, to benefit from specialization and redundancy. The ability of distributed spacecraft to navigate autonomously in the proximity of a SCB is indispensable for the successful realization of ISRU mission objectives. Quasi-autonomous methods currently used for proximity navigation require extensive ground support for mapping and model development, which can be an impediment for large scale multi-spacecraft ISRU missions in the future. It is prudent to leverage the advances in terrestrial robotic navigation to investigate the development of novel methods for autonomous navigation of spacecraft. The primary objective of the work presented in this thesis is to evaluate the feasibility and investigate the development of methods based on graph-based simultaneous localization and mapping (SLAM), a popular algorithm used in terrestrial autonomous navigation, for the autonomous navigation of distributed spacecraft in the proximity of SCBs. To this end, recent research in graph-based SLAM is extensively studied to identify strategies used to enable multi-agent navigation. The spacecraft navigation requirement is formulated as a graph-based SLAM problem using metric GraphSLAM or topometric graph-based SLAM. Techniques developed based on the identified strategies namely, map merging, inter-spacecraft measurements and relative localization are then applied to this formulation to enable distributed spacecraft navigation. In each case, navigation is formulated in terms of its application to a proximity operation scenario that best suits the multi-agent navigation technique. Several challenges related to the application of graph-based SLAM for spacecraft navigation, such as computational cost and illumination variation are also identified and addressed in the development of these methods. Experiments are performed using simulated models of asteroids and spacecraft dynamics, comparing the estimated states of the spacecraft and landmarks to the assumed true states. The results from the experiments indicate a consistent and robust state determination process, suggesting the suitability of the application of multi-agent navigation techniques to graph-based SLAM for enabling the autonomous navigation of distributed spacecraft near SCBs

Collaborative Multi-Robot Search and Rescue: Planning, Coordination, Perception, and Active Vision

Search and rescue (SAR) operations can take significant advantage from supporting autonomous or teleoperated robots and multi-robot systems. These can aid in mapping and situational assessment, monitoring and surveillance, establishing communication networks, or searching for victims. This paper provides a review of multi-robot systems supporting SAR operations, with system-level considerations and focusing on the algorithmic perspectives for multi-robot coordination and perception. This is, to the best of our knowledge, the first survey paper to cover (i) heterogeneous SAR robots in different environments, (ii) active perception in multi-robot systems, while (iii) giving two complementary points of view from the multi-agent perception and control perspectives. We also discuss the most significant open research questions: shared autonomy, sim-to-real transferability of existing methods, awareness of victims' conditions, coordination and interoperability in heterogeneous multi-robot systems, and active perception. The different topics in the survey are put in the context of the different challenges and constraints that various types of robots (ground, aerial, surface, or underwater) encounter in different SAR environments (maritime, urban, wilderness, or other post-disaster scenarios). The objective of this survey is to serve as an entry point to the various aspects of multi-robot SAR systems to researchers in both the machine learning and control fields by giving a global overview of the main approaches being taken in the SAR robotics area