93 research outputs found

    Active SLAM: A Review On Last Decade

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    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

    Enhanced teleoperation interfaces for multi-second latency conditions: System design and evaluation

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    Adding human cognitive skills to planetary exploration through remote teleoperation can lead to more effective and valuable scientific data acquisition. Still, even small amounts of latency can significantly affect real-time operations, often leading to compromised robot safety, goal overshoot, and high levels of human mental fatigue and cognitive workload. Thus, novel operational strategies are necessary to cope with these effects. This paper proposes three augmented teleoperation interfaces that allow the user to operate a robot subject to 3 seconds of latency: (1) Avatar-Aided Interface (AAI), a semi-autonomous approach based on a virtual element; (2) Predictive Interface (PI), an approach with direct control and predictive elements; and (3) Hybrid Interface (HI), where the operator can easily switch between PI and AAI. We conducted a systematic within-subject experiment to evaluate the proposed interfaces in a realistic virtual environment with frequent traction losses. The user study compared AAI and PI to a Control Interface (CI), which did not display any augmented elements. The main results of this comparison showed that: (1) AAI led to a significant reduction in workload and a significant increase in usability and robot safety; (2) the use of the PI caused a significant increase in path length, indicating that operators overshoot their goals more often with this approach; (3) PI and AAI had lower reported effort; and (4) AAI is more flexible and effortless than PI and CI. Finally, the presented results show the need to consider uncertainty (e.g., traction loss) in future interface design.info:eu-repo/semantics/publishedVersio

    Apprenticeship Bootstrapping for Autonomous Aerial Shepherding of Ground Swarm

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    Aerial shepherding of ground vehicles (ASGV) musters a group of uncrewed ground vehicles (UGVs) from the air using uncrewed aerial vehicles (UAVs). This inspiration enables robust uncrewed ground-air coordination where one or multiple UAVs effectively drive a group of UGVs towards a goal. Developing artificial intelligence (AI) agents for ASGV is a non-trivial task due to the sub-tasks, multiple skills, and their non-linear interaction required to synthesise a solution. One approach to developing AI agents is Imitation learning (IL), where humans demonstrate the task to the machine. However, gathering human data from complex tasks in human-swarm interaction (HSI) requires the human to perform the entire job, which could lead to unexpected errors caused by a lack of control skills and human workload due to the length and complexity of ASGV. We hypothesise that we can bootstrap the overall task by collecting human data from simpler sub-tasks to limit errors and workload for humans. Therefore, this thesis attempts to answer the primary research question of how to design IL algorithms for multiple agents. We propose a new learning scheme called Apprenticeship Bootstrapping (AB). In AB, the low-level behaviours of the shepherding agents are trained from human data using our proposed hierarchical IL algorithms. The high-level behaviours are then formed using a proposed gesture demonstration framework to collect human data from synthesising more complex controllers. The transferring mechanism is performed by aggregating the proposed IL algorithms. Experiments are designed using a mixed environment, where the UAV flies in a simulated robotic Gazebo environment, while the UGVs are physical vehicles in a natural environment. A system is designed to allow switching between humans controlling the UAVs using low-level actions and humans controlling the UAVs using high-level actions. The former enables data collection for developing autonomous agents for sub-tasks. At the same time, in the latter, humans control the UAV by issuing commands that call the autonomous agents for the sub-tasks. We baseline the learnt agents against Str\"{o}mbom scripted behaviours and show that the system can successfully generate autonomous behaviours for ASGV

    Robots learn to behave: improving human-robot collaboration in flexible manufacturing applications

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    L'abstract è presente nell'allegato / the abstract is in the attachmen

    Route Planning and Operator Allocation in Robot Fleets

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    In this thesis, we address various challenges related to optimal planning and task allocation in a robot fleet supervised by remote human operators. The overarching goal is to enhance the performance and efficiency of the robot fleets by planning routes and scheduling operator assistance while accounting for limited human availability. The thesis consists of three main problems, each of which focuses on a specific aspect of the system. The first problem pertains to optimal planning for a robot in a collaborative human-robot team, where the human supervisor is intermittently available to assist the robot to complete its tasks faster. Specifically, we address the challenge of computing the fastest route between two configurations in an environment with time constraints on how long the robot can wait for assistance at intermediate configurations. We consider the application of robot navigation in a city environment, where different routes can have distinct speed limits and different time constraints on how long a robot is allowed to wait. Our proposed approach utilizes the concepts of budget and critical departure times, enabling optimal solution and enhanced scalability compared to existing methods. Extensive comparisons with baseline algorithms on a city road network demonstrate its effectiveness and ability to achieve high-quality solutions. Furthermore, we extend the problem to the multi-robot case, where the challenge lies in prioritizing robots when multiple service requests arrive simultaneously. To address this challenge, we present a greedy algorithm that efficiently prioritizes service requests in a batch and has a remarkably good performance compared to the optimal solution. The next problem focuses on allocating human operators to robots in a fleet, considering each robot's specified route and the potential for failures and getting stuck. Conventional techniques used to solve such problems face scalability issues due to exponential growth of state and action spaces with the number of robots and operators. To overcome these, we derive conditions for a technical requirement called indexability, thereby enabling the use of the Whittle index heuristic. Our key insight is to leverage the structure of the value function of individual robots, resulting in conditions that can be easily verified separately for each state of each robot. We apply these conditions to two types of transitions commonly seen in supervised robot fleets. Through numerical simulations, we demonstrate the efficacy of Whittle index policy as a near-optimal scalable approach that outperforms existing scalable methods. Finally, we investigate the impact of interruptions on human supervisors overseeing a fleet of robots. Human supervisors in such systems are primarily responsible for monitoring robots, but can also be assigned with secondary tasks. These tasks can act as interruptions and can be categorized as either intrinsic, i.e., being directly related to the monitoring task, or extrinsic, i.e., being unrelated. Through a user study involving 3939 participants, the findings reveal that task performance remains relatively unaffected by interruptions, and is primarily dependent on the number of robots being monitored. However, extrinsic interruptions led to a significant increase in perceived workload, creating challenges in switching between tasks. These results highlight the importance of managing user workload by limiting extrinsic interruptions in such supervision systems. Overall, this thesis contributes to the field of robot planning and operator allocation in collaborative human-robot teams. By incorporating human assistance, addressing scalability challenges, and understanding the impact of interruptions, we aim to enhance the performance and usability of robot fleets. Our work introduces optimal planning methods and efficient allocation strategies, empowering the seamless operation of robot fleets in real-world scenarios. Additionally, we provide valuable insights into user workload, shedding light on the interactions between humans and robots in such systems. We hope that our research promotes the widespread adoption of robot fleets and facilitates their integration into various domains, ultimately driving advancements in the field

    Dense Visual Simultaneous Localisation and Mapping in Collaborative and Outdoor Scenarios

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    Dense visual simultaneous localisation and mapping (SLAM) systems can produce 3D reconstructions that are digital facsimiles of the physical space they describe. Systems that can produce dense maps with this level of fidelity in real time provide foundational spatial reasoning capabilities for many downstream tasks in autonomous robotics. Over the past 15 years, mapping small scale, indoor environments, such as desks and buildings, with a single slow moving, hand-held sensor has been one of the central focuses of dense visual SLAM research. However, most dense visual SLAM systems exhibit a number of limitations which mean they cannot be directly applied in collaborative or outdoors settings. The contribution of this thesis is to address these limitations with the development of new systems and algorithms for collaborative dense mapping, efficient dense alternation and outdoors operation with fast camera motion and wide field of view (FOV) cameras. We use ElasticFusion, a state-of-the-art dense SLAM system, as our starting point where each of these contributions is implemented as a novel extension to the system. We first present a collaborative dense SLAM system that allows a number of cameras starting with unknown initial relative positions to maintain local maps with the original ElasticFusion algorithm. Visual place recognition across local maps results in constraints that allow maps to be aligned into a common global reference frame, facilitating collaborative mapping and tracking of multiple cameras within a shared map. Within dense alternation based SLAM systems, the standard approach is to fuse every frame into the dense model without considering whether the information contained within the frame is already captured by the dense map and therefore redundant. As the number of cameras or the scale of the map increases, this approach becomes inefficient. In our second contribution, we address this inefficiency by introducing a novel information theoretic approach to keyframe selection that allows the system to avoid processing redundant information. We implement the procedure within ElasticFusion, demonstrating a marked reduction in the number of frames required by the system to estimate an accurate, denoised surface reconstruction. Before dense SLAM techniques can be applied in outdoor scenarios we must first address their reliance on active depth cameras, and their lack of suitability to fast camera motion. In our third contribution we present an outdoor dense SLAM system. The system overcomes the need for an active sensor by employing neural network-based depth inference to predict the geometry of the scene as it appears in each image. To address the issue of camera tracking during fast motion we employ a hybrid architecture, combining elements of both dense and sparse SLAM systems to perform camera tracking and to achieve globally consistent dense mapping. Automotive applications present a particularly important setting for dense visual SLAM systems. Such applications are characterised by their use of wide FOV cameras and are therefore not accurately modelled by the standard pinhole camera model. The fourth contribution of this thesis is to extend the above hybrid sparse-dense monocular SLAM system to cater for large FOV fisheye imagery. This is achieved by reformulating the mapping pipeline in terms of the Kannala-Brandt fisheye camera model. To estimate depth, we introduce a new version of the PackNet depth estimation neural network (Guizilini et al., 2020) adapted for fisheye inputs. To demonstrate the effectiveness of our contributions, we present experimental results, computed by processing the synthetic ICL-NUIM dataset of Handa et al. (2014) as well as the real-world TUM-RGBD dataset of Sturm et al. (2012). For outdoor SLAM we show the results of our system processing the autonomous driving KITTI and KITTI-360 datasets of Geiger et al. (2012a) and Liao et al. (2021) respectively
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