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Register a new userLow-cost Retina-like Robotic Lidars Based on Incommensurable Scanning
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Added by
Zheng Liu
Publication date
2020
fields
Informatics Engineering
and research's language is
English
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High performance lidars are essential in autonomous robots such as self-driving cars, automated ground vehicles and intelligent machines. Traditional mechanical scanning lidars offer superior performance in autonomous vehicles, but the potential mass application is limited by the inherent manufacturing difficulty. We propose a robotic lidar sensor based on incommensurable scanning that allows straightforward mass production and adoption in autonomous robots. Some unique features are additionally permitted by this incommensurable scanning. Similar to the fovea in human retina, this lidar features a peaked central angular density, enabling in applications that prefers eye-like attention. The incommensurable scanning method of this lidar could also provide a much higher resolution than conventional lidars which is beneficial in robotic applications such as sensor calibration. Examples making use of these advantageous features are demonstrated.
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Standardized evaluation measures have aided in the progress of machine learning approaches in disciplines such as computer vision and machine translation. In this paper, we make the case that robotic learning would also benefit from benchmarking, and present the REPLAB platform for benchmarking vision-based manipulation tasks. REPLAB is a reproducible and self-contained hardware stack (robot arm, camera, and workspace) that costs about 2000 USD, occupies a cuboid of size 70x40x60 cm, and permits full assembly within a few hours. Through this low-cost, compact design, REPLAB aims to drive wide participation by lowering the barrier to entry into robotics and to enable easy scaling to many robots. We envision REPLAB as a framework for reproducible research across manipulation tasks, and as a step in this direction, we define a template for a grasping benchmark consisting of a task definition, evaluation protocol, performance measures, and a dataset of 92k grasp attempts. We implement, evaluate, and analyze several previously proposed grasping approaches to establish baselines for this benchmark. Finally, we also implement and evaluate a deep reinforcement learning approach for 3D reaching tasks on our REPLAB platform. Project page with assembly instructions, code, and videos: https://goo.gl/5F9dP4.
We present MuSHR, the Multi-agent System for non-Holonomic Racing. MuSHR is a low-cost, open-source robotic racecar platform for education and research, developed by the Personal Robotics Lab in the Paul G. Allen School of Computer Science & Engineering at the University of Washington. MuSHR aspires to contribute towards democratizing the field of robotics as a low-cost platform that can be built and deployed by following detailed, open documentation and do-it-yourself tutorials. A set of demos and lab assignments developed for the Mobile Robots course at the University of Washington provide guided, hands-on experience with the platform, and milestones for further development. MuSHR is a valuable asset for academic research labs, robotics instructors, and robotics enthusiasts.
In keyhole interventions, surgeons rely on a colleague to act as a camera assistant when their hands are occupied with surgical instruments. This often leads to reduced image stability, increased task completion times and sometimes errors. Robotic endoscope holders (REHs), controlled by a set of basic instructions, have been proposed as an alternative, but their unnatural handling increases the cognitive load of the surgeon, hindering their widespread clinical acceptance. We propose that REHs collaborate with the operating surgeon via semantically rich instructions that closely resemble those issued to a human camera assistant, such as focus on my right-hand instrument. As a proof-of-concept, we present a novel system that paves the way towards a synergistic interaction between surgeons and REHs. The proposed platform allows the surgeon to perform a bi-manual coordination and navigation task, while a robotic arm autonomously performs various endoscope positioning tasks. Within our system, we propose a novel tooltip localization method based on surgical tool segmentation, and a novel visual servoing approach that ensures smooth and correct motion of the endoscope camera. We validate our vision pipeline and run a user study of this system. Through successful application in a medically proven bi-manual coordination and navigation task, the framework has shown to be a promising starting point towards broader clinical adoption of REHs.
This paper analyzes the meeting time between a pair of pursuer and evader performing random walks on digraphs. The existing bounds on the meeting time usually work only for certain classes of walks and cannot be used to formulate optimization problems and design robotic strategies. First, by analyzing multiple random walks on a common graph as a single random walk on the Kronecker product graph, we provide the first closed-form expression for the expected meeting time in terms of the transition matrices of the moving agents. This novel expression leads to necessary and sufficient conditions for the meeting time to be finite and to insightful graph-theoretic interpretations. Second, based on the closed-form expression, we setup and study the minimization problem for the expected capture time for a pursuer/evader pair. We report theoretical and numerical results on basic case studies to show the effectiveness of the design.
Novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a pandemic of epic proportions and a global response to prepare health systems worldwide is of utmost importance. In addition to its cost-effectiveness in a resources-limited setting, lung ultrasound (LUS) has emerged as a rapid noninvasive imaging tool for the diagnosis of COVID-19 infected patients. Concerns surrounding LUS include the disparity of infected patients and healthcare providers, relatively small number of physicians and sonographers capable of performing LUS, and most importantly, the requirement for substantial physical contact between the patient and operator, increasing the risk of transmission. Mitigation of the spread of the virus is of paramount importance. A 2-dimensional (2D) tele-operative robotic platform capable of performing LUS in for COVID-19 infected patients may be of significant benefit. The authors address the aforementioned issues surrounding the use of LUS in the application of COVID- 19 infected patients. In addition, first time application, feasibility and safety were validated in three healthy subjects, along with 2D image optimization and comparison for overall accuracy. Preliminary results demonstrate that the proposed platform allows for successful acquisition and application of LUS in humans.
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