UAV first view landmark localization with active reinforcement learning

We present an active reinforcement learning framework for unmanned aerial vehicle (UAV) first view landmark localization. We formulate the problem of landmark localization as that of a Markov decision process and introduce an active landmark-localization network (ALLNet) to address it. The aim of the ALLNet is to locate a bounding box that surrounds the landmark in a first view image sequence. To this end, it is trained in a reinforcement learning fashion. Specifically, it employs support vector machine (SVM) scores on the bounding box patches as rewards and learns the bounding box transformations as actions. Furthermore, each SVM score indicates whether or not the landmark is detected by the bounding box such that it enables the ALLNet to have the capability of judging whether the landmark leaves or re-enters a first view image. Therefore, the operation of the ALLNet is not only dominated by the reinforcement learning process but also supplemented by an active learning motivated manner. Once the landmark is considered to leave the first view image, the ALLNet stops operating until the SVM detects its re-entry to the view. The active reinforcement learning model enables training a robust ALLNet for landmark localization. The experimental results validate the effectiveness of the proposed model for UAV first view landmark localization

Active SLAM: A Review On Last Decade

This article presents a comprehensive review of the Active Simultaneous Localization and Mapping (A-SLAM) research conducted over the past decade. It explores the formulation, applications, and methodologies employed in A-SLAM, particularly in trajectory generation and control-action selection, drawing on concepts from Information Theory (IT) and the Theory of Optimal Experimental Design (TOED). This review includes both qualitative and quantitative analyses of various approaches, deployment scenarios, configurations, path-planning methods, and utility functions within A-SLAM research. Furthermore, this article introduces a novel analysis of Active Collaborative SLAM (AC-SLAM), focusing on collaborative aspects within SLAM systems. It includes a thorough examination of collaborative parameters and approaches, supported by both qualitative and statistical assessments. This study also identifies limitations in the existing literature and suggests potential avenues for future research. This survey serves as a valuable resource for researchers seeking insights into A-SLAM methods and techniques, offering a current overview of A-SLAM formulation.Comment: 34 pages, 8 figures, 6 table

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

Active Mapping and Robot Exploration: A Survey

Simultaneous localization and mapping responds to the problem of building a map of the environment without any prior information and based on the data obtained from one or more sensors. In most situations, the robot is driven by a human operator, but some systems are capable of navigating autonomously while mapping, which is called native simultaneous localization and mapping. This strategy focuses on actively calculating the trajectories to explore the environment while building a map with a minimum error. In this paper, a comprehensive review of the research work developed in this field is provided, targeting the most relevant contributions in indoor mobile robotics.This research was funded by the ELKARTEK project ELKARBOT KK-2020/00092 of the Basque Government

A Comprehensive Review on Autonomous Navigation

The field of autonomous mobile robots has undergone dramatic advancements over the past decades. Despite achieving important milestones, several challenges are yet to be addressed. Aggregating the achievements of the robotic community as survey papers is vital to keep the track of current state-of-the-art and the challenges that must be tackled in the future. This paper tries to provide a comprehensive review of autonomous mobile robots covering topics such as sensor types, mobile robot platforms, simulation tools, path planning and following, sensor fusion methods, obstacle avoidance, and SLAM. The urge to present a survey paper is twofold. First, autonomous navigation field evolves fast so writing survey papers regularly is crucial to keep the research community well-aware of the current status of this field. Second, deep learning methods have revolutionized many fields including autonomous navigation. Therefore, it is necessary to give an appropriate treatment of the role of deep learning in autonomous navigation as well which is covered in this paper. Future works and research gaps will also be discussed

Informative Path Planning for Active Field Mapping under Localization Uncertainty

Information gathering algorithms play a key role in unlocking the potential of robots for efficient data collection in a wide range of applications. However, most existing strategies neglect the fundamental problem of the robot pose uncertainty, which is an implicit requirement for creating robust, high-quality maps. To address this issue, we introduce an informative planning framework for active mapping that explicitly accounts for the pose uncertainty in both the mapping and planning tasks. Our strategy exploits a Gaussian Process (GP) model to capture a target environmental field given the uncertainty on its inputs. For planning, we formulate a new utility function that couples the localization and field mapping objectives in GP-based mapping scenarios in a principled way, without relying on any manually tuned parameters. Extensive simulations show that our approach outperforms existing strategies, with reductions in mean pose uncertainty and map error. We also present a proof of concept in an indoor temperature mapping scenario.Comment: 8 pages, 7 figures, submission (revised) to Robotics & Automation Letters (and IEEE International Conference on Robotics and Automation

A review of UAV autonomous navigation in GPS-denied environments

Unmanned aerial vehicles (UAVs) have drawn increased research interest in recent years, leading to a vast number of applications, such as, terrain exploration, disaster assistance and industrial inspection. Unlike UAV navigation in outdoor environments that rely on GPS (Global Positioning System) for localization, indoor navigation cannot rely on GPS due to the poor quality or lack of signal. Although some reviewing papers particularly summarized indoor navigation strategies (e.g., Visual-based Navigation) or their specific sub-components (e.g., localization and path planning) in detail, there still lacks a comprehensive survey for the complete navigation strategies that cover different technologies. This paper proposes a taxonomy which firstly classifies the navigation strategies into Mapless and Map-based ones based on map usage and then, respectively categorizes the Mapless navigation into Integrated, Direct and Indirect approaches via common characteristics. The Map-based navigation is then split into Known Map/Spaces and Map-building via prior knowledge. In order to analyze these navigation strategies, this paper uses three evaluation metrics (Path Length, Deviation Rate and Exploration Efficiency) according to the common purposes of navigation to show how well they can perform. Furthermore, three representative strategies were selected and 120 flying experiments conducted in two reality-like simulated indoor environments to show their performances against the evaluation metrics proposed in this paper, i.e., the ratio of Successful Flight, the Mean time of Successful Flight, the Mean Length of Successful Flight, the Mean time of Flight, and the Mean Length of Flight. In comparison to the CNN-based Supervised Learning (directly maps visual observations to UAV controls) and the Frontier-based navigation (necessitates continuous global map generation), the experiments show that the CNN-based Distance Estimation for navigation trades off the ratio of Successful Flight and the required time and path length. Moreover, this paper identifies the current challenges and opportunities which will drive UAV navigation research in GPS-denied environments

MOMDP modeling for UAV safe path planning in an urban environment

Path planning is a research domain very active and applied among others on autonomous vehicle such as UAV. In recent years, a lot of progress has been made on path planning under uncertainties issued by a vehicle navigation system, for example in localization or environment mapping. However, such uncertainties are often treated by the path planner in a deterministic way. That is, the navigation system's performance is deterministically given in function of the environment. This paper tackles a more complex problem of UAV safe path planning in an urbain environment, in which UAV is at risks of GPS signal occusion and obstacle collision. The key idea is to make a UAV path planning along with its navigation and guidance mode planning, where each of such mode uses different set of sensors and whose availability and performance are environment-dependent. A partial knowledge on the environment is supposed to be available, in a form of probability maps of obstacles and sensor availabilities. To solve this problem the UAV need to be well represented in the planner model and so do the associated uncertainty propagation. This paper proposes a model based on Mixed Observability Markov Decision Process (MOMDP). The proposed MOMDP model allows the planner to choose the best path-direction with the adapted sensor set for an UAV to reach a mission goal efficiently and safely. This paper only provides a MOMDP model for the planner, and the planning algorithm and preliminary results will be expected in the final paper