3D multi-robot patrolling with a two-level coordination strategy

Teams of UGVs patrolling harsh and complex 3D environments can experience interference and spatial conflicts with one another. Neglecting the occurrence of these events crucially hinders both soundness and reliability of a patrolling process. This work presents a distributed multi-robot patrolling technique, which uses a two-level coordination strategy to minimize and explicitly manage the occurrence of conflicts and interference. The first level guides the agents to single out exclusive target nodes on a topological map. This target selection relies on a shared idleness representation and a coordination mechanism preventing topological conflicts. The second level hosts coordination strategies based on a metric representation of space and is supported by a 3D SLAM system. Here, each robot path planner negotiates spatial conflicts by applying a multi-robot traversability function. Continuous interactions between these two levels ensure coordination and conflicts resolution. Both simulations and real-world experiments are presented to validate the performances of the proposed patrolling strategy in 3D environments. Results show this is a promising solution for managing spatial conflicts and preventing deadlocks

A distributed architecture for unmanned aerial systems based on publish/subscribe messaging and simultaneous localisation and mapping (SLAM) testbed

A dissertation submitted in fulfilment for the degree of Master of Science. School of Computational and Applied Mathematics, University of the Witwatersrand, Johannesburg, South Africa, November 2017The increased capabilities and lower cost of Micro Aerial Vehicles (MAVs) unveil big opportunities for a rapidly growing number of civilian and commercial applications. Some missions require direct control using a receiver in a point-to-point connection, involving one or very few MAVs. An alternative class of mission is remotely controlled, with the control of the drone automated to a certain extent using mission planning software and autopilot systems. For most emerging missions, there is a need for more autonomous, cooperative control of MAVs, as well as more complex data processing from sensors like cameras and laser scanners. In the last decade, this has given rise to an extensive research from both academia and industry. This research direction applies robotics and computer vision concepts to Unmanned Aerial Systems (UASs). However, UASs are often designed for specific hardware and software, thus providing limited integration, interoperability and re-usability across different missions. In addition, there are numerous open issues related to UAS command, control and communication(C3), and multi-MAVs. We argue and elaborate throughout this dissertation that some of the recent standardbased publish/subscribe communication protocols can solve many of these challenges and meet the non-functional requirements of MAV robotics applications. This dissertation assesses the MQTT, DDS and TCPROS protocols in a distributed architecture of a UAS control system and Ground Control Station software. While TCPROS has been the leading robotics communication transport for ROS applications, MQTT and DDS are lightweight enough to be used for data exchange between distributed systems of aerial robots. Furthermore, MQTT and DDS are based on industry standards to foster communication interoperability of “things”. Both protocols have been extensively presented to address many of today’s needs related to networks based on the internet of things (IoT). For example, MQTT has been used to exchange data with space probes, whereas DDS was employed for aerospace defence and applications of smart cities. We designed and implemented a distributed UAS architecture based on each publish/subscribe protocol TCPROS, MQTT and DDS. The proposed communication systems were tested with a vision-based Simultaneous Localisation and Mapping (SLAM) system involving three Parrot AR Drone2 MAVs. Within the context of this study, MQTT and DDS messaging frameworks serve the purpose of abstracting UAS complexity and heterogeneity. Additionally, these protocols are expected to provide low-latency communication and scale up to meet the requirements of real-time remote sensing applications. The most important contribution of this work is the implementation of a complete distributed communication architecture for multi-MAVs. Furthermore, we assess the viability of this architecture and benchmark the performance of the protocols in relation to an autonomous quadcopter navigation testbed composed of a SLAM algorithm, an extended Kalman filter and a PID controller.XL201

Micro Aerial Vehicles (MAV) Assured Navigation in Search and Rescue Missions Robust Localization, Mapping and Detection

This Master's Thesis describes the developments on robust localization, mapping and detection algorithms for Micro Aerial Vehicles (MAVs). The localization method proposes a seamless indoor-outdoor multi-sensor architecture. This algorithm is capable of using all or a subset of its sensor inputs to determine a platform's position, velocity and attitude (PVA). It relies on the Inertial Measurement Unit as the core sensor and monitors the status and observability of the secondary sensors to select the most optimum estimator strategy for each situation. Furthermore, it ensures a smooth transition between filters structures. This document also describes the integration mechanism for a set of common sensors such as GNSS receivers, laser scanners and stereo and mono cameras. The mapping algorithm provides a fully automated fast aerial mapping pipeline. It speeds up the process by pre-selecting the images using the flight plan and the onboard localization. Furthermore, it relies on Structure from Motion (SfM) techniques to produce an optimized 3D reconstruction of camera locations and sparse scene geometry. These outputs are used to compute the perspective transformations that project the raw images on the ground and produce a geo-referenced map. Finally, these maps are fused with other domains in a collaborative UGV and UAV mapping algorithms. The real-time aerial detection of victims is based on a thermal camera. The algorithm is composed by three steps. Firstly, a normalization of the image is performed to get rid of the background and to extract the regions of interest. Later the victim detection and tracking steps produce the real-time geo-referenced locations of the detections. The thesis also proposes the concept of a MAV Copilot, a payload composed by a set of sensors and algorithm the enhances the capabilities of any commercial MAV. To develop and validate these contributions, a prototype of a search and rescue MAV and the Copilot has been developed. These developments have been validated in three large-scale demonstrations of search and rescue operations in the context of the European project ICARUS: a shipwreck in Lisbon (Portugal), an earthquake in Marche (Belgium), and the Fukushima nuclear disaster in the euRathlon 2015 competition in Piombino (Italy)

Navigation of Autonomous Light Vehicles Using an Optimal Trajectory Planning Algorithm

[EN] Autonomous navigation is a complex problem that involves different tasks, such as location of the mobile robot in the scenario, robotic mapping, generating the trajectory, navigating from the initial point to the target point, detecting objects it may encounter in its path, etc. This paper presents a new optimal trajectory planning algorithm that allows the assessment of the energy efficiency of autonomous light vehicles. To the best of our knowledge, this is the first time in the literature that this is carried out by minimizing the travel time while considering the vehicle's dynamic behavior, its limitations, and with the capability of avoiding obstacles and constraining energy consumption. This enables the automotive industry to design environmentally sustainable strategies towards compliance with governmental greenhouse gas (GHG) emission regulations and for climate change mitigation and adaptation policies. The reduction in energy consumption also allows companies to stay competitive in the marketplace. The vehicle navigation control is efficiently implemented through a middleware of component-based software development (CBSD) based on a Robot Operating System (ROS) package. It boosts the reuse of software components and the development of systems from other existing systems. Therefore, it allows the avoidance of complex control software architectures to integrate the different hardware and software components. The global maps are created by scanning the environment with FARO 3D and 2D SICK laser sensors. The proposed algorithm presents a low computational cost and has been implemented as a new module of distributed architecture. It has been integrated into the ROS package to achieve real time autonomous navigation of the vehicle. The methodology has been successfully validated in real indoor experiments using a light vehicle under different scenarios entailing several obstacle locations and dynamic parameters.This work has been partially funded by FEDER-CICYT project with reference DPI2017-84201-R financed by Ministerio de Economia, Industria e Innovacion (Spain).Valera Fernández, Á.; Valero Chuliá, FJ.; Vallés Miquel, M.; Besa Gonzálvez, AJ.; Mata Amela, V.; Llopis-Albert, C. (2021). Navigation of Autonomous Light Vehicles Using an Optimal Trajectory Planning Algorithm. Sustainability. 13(3):1-23. https://doi.org/10.3390/su1303123312313

Distributed multi-agent target search and tracking with Gaussian process and reinforcement learning

Deploying multiple robots for target search and tracking has many practical applications, yet the challenge of planning over unknown or partially known targets remains difficult to address. With recent advances in deep learning, intelligent control techniques such as reinforcement learning have enabled agents to learn autonomously from environment interactions with little to no prior knowledge. Such methods can address the exploration-exploitation tradeoff of planning over unknown targets in a data-driven manner, eliminating the reliance on heuristics typical of traditional approaches and streamlining the decision-making pipeline with end-to-end training. In this paper, we propose a multi-agent reinforcement learning technique with target map building based on distributed Gaussian process. We leverage the distributed Gaussian process to encode belief over the target locations and efficiently plan over unknown targets. We evaluate the performance and transferability of the trained policy in simulation and demonstrate the method on a swarm of micro unmanned aerial vehicles with hardware experiments.Comment: 10 pages, 6 figures; preprint submitted to IJCAS; first two authors contributed equall

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

Natural language-based human-robot control

With the increase in the number of robots arriving at our homes, it is important to find an easy and efficient way to communicate with them. Natural language is the most natural form of communication between humans, so its use by users to communicate with robots, with no knowledge of how they work, is an added value to control them. We present a solution that allows us to interact with a robot in a natural way, so it is easier for human-robot cooperation to happen. This solution includes a method of transforming the information in natural language into controls that a robot can perform. We implement this solution in the specific case of indoor navigation. Finally, we evaluate the performance and interactivity of our approach, by having users, without previous knowledge of robotic control, controlling a robot in a simulated environment.Com o aumento do número de robôs a chegar às nossas casas, é importante encontrar uma forma fácil e eficiente de comunicar com eles. A língua natural é a forma mais natural de comunicação entre humanos, pelo que a sua utilização em comunicação com robôs, sem conhecimentos de como os mesmos funcionam, para os controlar é uma mais valia. Apresentamos uma metodologia que nos permite interagir com um robô de uma forma natural, desta forma é mais fácil que a interação humano-robô aconteça. Esta metodologia inclui um método para transformar informação de língua natural em controlos que um robô pode realizar. Demonstramos esta metodologia no caso específico da navegação dentro de casas. Em seguida, avaliamos o desempenho e a interactividade da nossa abordagem, com utilizadores, sem conhecimento de controlo robótico, a controlar um robô num ambiente simulado