51 research outputs found

    Planning Algorithms for Multi-Robot Active Perception

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    A fundamental task of robotic systems is to use on-board sensors and perception algorithms to understand high-level semantic properties of an environment. These semantic properties may include a map of the environment, the presence of objects, or the parameters of a dynamic field. Observations are highly viewpoint dependent and, thus, the performance of perception algorithms can be improved by planning the motion of the robots to obtain high-value observations. This motivates the problem of active perception, where the goal is to plan the motion of robots to improve perception performance. This fundamental problem is central to many robotics applications, including environmental monitoring, planetary exploration, and precision agriculture. The core contribution of this thesis is a suite of planning algorithms for multi-robot active perception. These algorithms are designed to improve system-level performance on many fronts: online and anytime planning, addressing uncertainty, optimising over a long time horizon, decentralised coordination, robustness to unreliable communication, predicting plans of other agents, and exploiting characteristics of perception models. We first propose the decentralised Monte Carlo tree search algorithm as a generally-applicable, decentralised algorithm for multi-robot planning. We then present a self-organising map algorithm designed to find paths that maximally observe points of interest. Finally, we consider the problem of mission monitoring, where a team of robots monitor the progress of a robotic mission. A spatiotemporal optimal stopping algorithm is proposed and a generalisation for decentralised monitoring. Experimental results are presented for a range of scenarios, such as marine operations and object recognition. Our analytical and empirical results demonstrate theoretically-interesting and practically-relevant properties that support the use of the approaches in practice

    Multi-Robot Multi-Room Exploration with Geometric Cue Extraction and Spherical Decomposition

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    This work proposes an autonomous multi-robot exploration pipeline that coordinates the behaviors of robots in an indoor environment composed of multiple rooms. Contrary to simple frontier-based exploration approaches, we aim to enable robots to methodically explore and observe an unknown set of rooms in a structured building, keeping track of which rooms are already explored and sharing this information among robots to coordinate their behaviors in a distributed manner. To this end, we propose (1) a geometric cue extraction method that processes 3D map point cloud data and detects the locations of potential cues such as doors and rooms, (2) a spherical decomposition for open spaces used for target assignment. Using these two components, our pipeline effectively assigns tasks among robots, and enables a methodical exploration of rooms. We evaluate the performance of our pipeline using a team of up to 3 aerial robots, and show that our method outperforms the baseline by 36.6% in simulation and 26.4% in real-world experiments

    A measurement of the millimetre emission and the Sunyaev-Zel'dovich effect associated with low-frequency radio sources

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    We present a statistical analysis of the millimetre-wavelength properties of 1.4GHz-selected sources and a detection of the Sunyaev–Zel’dovich (SZ) effect associated with the haloes that host them. We stack data at 148, 218 and 277GHz from the Atacama Cosmology Telescope at the positions of a large sample of radio AGN selected at 1.4GHz. The thermal SZ effect associated with the haloes that host the AGN is detected at the 5σ level through its spectral signature, representing a statistical detection of the SZ effect in some of the lowest mass haloes (average M 200 ≈ 10 13 M. h −1 70 ) studied to date. The relation between the SZ effect and mass (based on weak lensing measurements of radio galaxies) is consistent with that measured by Planck for local bright galaxies. In the context of galaxy evolution models, this study confirms that galaxies with radio AGN also typically support hot gaseous haloes. Adding Herschel observations allows us to show that the SZ signal is not significantly contaminated by dust emission. Finally, we analyse the contribution of radio sources to the angular power spectrum of the cosmic microwave background

    A prenylated dsRNA sensor protects against severe COVID-19

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    Inherited genetic factors can influence the severity of COVID-19, but the molecular explanation underpinning a genetic association is often unclear. Intracellular antiviral defenses can inhibit the replication of viruses and reduce disease severity. To better understand the antiviral defenses relevant to COVID-19, we used interferon-stimulated gene (ISG) expression screening to reveal that OAS1, through RNase L, potently inhibits SARS-CoV-2. We show that a common splice-acceptor SNP (Rs10774671) governs whether people express prenylated OAS1 isoforms that are membrane-associated and sense specific regions of SARS-CoV-2 RNAs, or only express cytosolic, nonprenylated OAS1 that does not efficiently detect SARS-CoV-2. Importantly, in hospitalized patients, expression of prenylated OAS1 was associated with protection from severe COVID-19, suggesting this antiviral defense is a major component of a protective antiviral response

    Effect of angiotensin-converting enzyme inhibitor and angiotensin receptor blocker initiation on organ support-free days in patients hospitalized with COVID-19

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    IMPORTANCE Overactivation of the renin-angiotensin system (RAS) may contribute to poor clinical outcomes in patients with COVID-19. Objective To determine whether angiotensin-converting enzyme (ACE) inhibitor or angiotensin receptor blocker (ARB) initiation improves outcomes in patients hospitalized for COVID-19. DESIGN, SETTING, AND PARTICIPANTS In an ongoing, adaptive platform randomized clinical trial, 721 critically ill and 58 non–critically ill hospitalized adults were randomized to receive an RAS inhibitor or control between March 16, 2021, and February 25, 2022, at 69 sites in 7 countries (final follow-up on June 1, 2022). INTERVENTIONS Patients were randomized to receive open-label initiation of an ACE inhibitor (n = 257), ARB (n = 248), ARB in combination with DMX-200 (a chemokine receptor-2 inhibitor; n = 10), or no RAS inhibitor (control; n = 264) for up to 10 days. MAIN OUTCOMES AND MEASURES The primary outcome was organ support–free days, a composite of hospital survival and days alive without cardiovascular or respiratory organ support through 21 days. The primary analysis was a bayesian cumulative logistic model. Odds ratios (ORs) greater than 1 represent improved outcomes. RESULTS On February 25, 2022, enrollment was discontinued due to safety concerns. Among 679 critically ill patients with available primary outcome data, the median age was 56 years and 239 participants (35.2%) were women. Median (IQR) organ support–free days among critically ill patients was 10 (–1 to 16) in the ACE inhibitor group (n = 231), 8 (–1 to 17) in the ARB group (n = 217), and 12 (0 to 17) in the control group (n = 231) (median adjusted odds ratios of 0.77 [95% bayesian credible interval, 0.58-1.06] for improvement for ACE inhibitor and 0.76 [95% credible interval, 0.56-1.05] for ARB compared with control). The posterior probabilities that ACE inhibitors and ARBs worsened organ support–free days compared with control were 94.9% and 95.4%, respectively. Hospital survival occurred in 166 of 231 critically ill participants (71.9%) in the ACE inhibitor group, 152 of 217 (70.0%) in the ARB group, and 182 of 231 (78.8%) in the control group (posterior probabilities that ACE inhibitor and ARB worsened hospital survival compared with control were 95.3% and 98.1%, respectively). CONCLUSIONS AND RELEVANCE In this trial, among critically ill adults with COVID-19, initiation of an ACE inhibitor or ARB did not improve, and likely worsened, clinical outcomes. TRIAL REGISTRATION ClinicalTrials.gov Identifier: NCT0273570

    An evaluation of multi-modal user interface elements for tablet-based robot teleoperation

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    For robot teleoperation systems, tablet and smart phone user interfaces provide portability and accessibility to allow anyone anywhere to connect to the system quickly and easily. This can be highly advantageous for disaster-relief robot systems where timing is critical. However, the small screen size and unconventional input methods mean that traditional teleoperation user interface elements, such as multiple visual windows, keyboards and mice, are no longer effective. In this paper we propose and investigate multi-modal user interface design principles as a solution for effective tablet-based teleoperation. We present our tablet user interface that features multiple complementary modalities of control and feedback mechanisms. It allows the operator to perceive the robot's environment and state by exciting their senses of vision, hearing and touch with overlaying visual displays, virtual 3-D audio and vibration feedback. The operator commands actions to the robot through the use of intuitive input methods utilising the touch screen and accelerometer. We analyse these design principles by performing a user study that focuses on finding what aspects of the multi-modal user interface affect the system performance during a navigation task. The study highlights advantages of multimodal user interfaces for robot teleoperation and makes several findings to be considered when developing similar systems
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