99 research outputs found

    Multi-Robot Patrol Algorithm with Distributed Coordination and Consciousness of the Base Station's Situation Awareness

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    Multi-robot patrolling is the potential application for robotic systems to survey wide areas efficiently without human burdens and mistakes. However, such systems have few examples of real-world applications due to their lack of human predictability. This paper proposes an algorithm: Local Reactive (LR) for multi-robot patrolling to satisfy both needs: (i)patrol efficiently and (ii)provide humans with better situation awareness to enhance system predictability. Each robot operating according to the proposed algorithm selects its patrol target from the local areas around the robot's current location by two requirements: (i)patrol location with greater need, (ii)report its achievements to the base station. The algorithm is distributed and coordinates the robots without centralized control by sharing their patrol achievements and degree of need to report to the base station. The proposed algorithm performed better than existing algorithms in both patrolling and the base station's situation awareness.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

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

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    Multiple vehicle cooperation and collision avoidance in automated vehicles : Survey and an AI‑enabled conceptual framework

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    Prospective customers are becoming more concerned about safety and comfort as the automobile industry swings toward automated vehicles (AVs). A comprehensive evaluation of recent AVs collision data indicates that modern automated driving systems are prone to rear-end collisions, usually leading to multiple-vehicle collisions. Moreover, most investigations into severe traffic conditions are confined to single-vehicle collisions. This work reviewed diverse techniques of existing literature to provide planning procedures for multiple vehicle cooperation and collision avoidance (MVCCA) strategies in AVs while also considering their performance and social impact viewpoints. Firstly, we investigate and tabulate the existing MVCCA techniques associated with single-vehicle collision avoidance perspectives. Then, current achievements are extensively evaluated, challenges and flows are identified, and remedies are intelligently formed to exploit a taxonomy. This paper also aims to give readers an AI-enabled conceptual framework and a decision-making model with a concrete structure of the training network settings to bridge the gaps between current investigations. These findings are intended to shed insight into the benefits of the greater efficiency of AVs set-up for academics and policymakers. Lastly, the open research issues discussed in this survey will pave the way for the actual implementation of driverless automated traffic systems

    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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