Reducing Uncertainty in Collective Perception using Self-organized Hierarchy

In collective perception, agents sample spatial data and use the samples to agree on some 8 estimate. In this paper, we identify the sources of statistical uncertainty that occur in collective 9 perception and note that improving the accuracy of fully decentralized approaches, beyond a 10 certain threshold, might be intractable. We propose self-organized hierarchy as an approach 11 to improve accuracy in collective perception, by reducing or eliminating some of the sources of 12 uncertainty. Using self-organized hierarchy, aspects of centralization and decentralization can be 13 combined: robots can understand their relative positions system-wide and fuse their information 14 at one point, without requiring, e.g., a fully connected or static communication network. In this 15 way, multi-sensor fusion techniques that have been designed for fully centralized systems can 16 be applied to a self-organized system for the ﬁrst time, without losing the key practical beneﬁts 17 of decentralization. We implement simple proof-of-concept fusion in a self-organized hierarchy 18 approach and test it against three fully decentralized benchmark approaches. We test the 19 perceptual accuracy of the approaches for time-invariant and time-varying absolute conditions, 20 and test the scalability and fault tolerance of their accuracies. We show that the self-organized 21 hierarchy approach is substantially more accurate, more consistent, and faster than the other 22 approaches, but also that it is comparably scalable and fault-tolerant. problems [see 6], where a robot swarm compares multiple options according to a given criterion. In one 74 common setup, robots sense colors in an environment and compare them according to the criterion of highest 75 representation [8, 14–18]. In another setup, robots compare discrete zones according to a criterion of quality 76 that can be sensed from anywhere in the zone [7, 19, 20]. In similar setups, robots aggregate at a certain 77 type of environmental feature, either by collectively deciding on an appropriate threshold to detect it [21] or 78 sharing detected maximums so robots at a local optimum are triggered to explore further [22]. In [23], robots 79 estimate the density of tiles scattered in the environment by sharing perceptual information, including sample 80 counts and values for time-based decrementation. In [24], robots collaboratively track a moving target by 81 sharing locally-based beliefs about the target position with their neighbors, using incomplete knowledge of 82 robot positions and orientations.

In this work, we present a novel open-source quadrotor platform for research in swarm robotics. In swarm robotics, homogeneous or heterogeneous groups of robots collaborate using local interactions and collectively solve tasks beyond an individual robot’s capabilities. Individual robots must have onboard processing, communication, and sensing capabilities to autonomously react to their neighbors and immediate environment. Most research involving quadrotors in swarm robotics either presents only unrealistic simulation results or uses off-the-shelf commercial quadrotors to test limited aspects of swarm algorithms, using centralized control or communication to supplement insufficient onboard capabilities. In addition, the quadrotors used in existing studies often lack complete documentation or lack extensibility, making it difficult for researchers to modify the hardware and software to expand onboard capabilities. We provide a well-documented, entirely open-source quadrotor platform— S-drone (Swarm-drone)—to foster and support research involving quadrotors in swarm robotics. The S-drone is suitable for a wide range of contexts, supports quad-camera vision-based navigation and a variety of onboard sensing, and is extensible. It is especially suited for swarm robotics research because it can operate using strictly onboard processing and sensing without the need for external infrastructures such as global positioning systems, motion capture systems, or ground stations for off-board sensing.

S-drone: An Open-source Quadrotor for Experimentation in Swarm Robotics

Introduction: Gravity waves are ubiquitous throughout the Mars atmosphere. They play an important role in producing the general circulation pattern and temperature structure in the middle and upper atmosphere of Mars (e.g., Medvedev and Yiğit, 2012) and facilitate cloud formation in the upper mesosphere and lower thermosphere (Yiğit et al., 2018). Expected variations of this thermosphere structure and winds owing to changing solar insolation are significantly modified by upward propagating gravity waves. Indeed, the effects of GWs in the Martian upper atmosphere have been observed by the MAVEN (Mars Atmosphere Volatile EvolutioN) mission up to at least 250 km (Yiğit et al. 2015). Nevertheless, a comprehensive characterization of GWs in the Martian upper atmosphere is still lacking. In this regard, improvements in numerical modeling can serve to address the impacts of GWs on the upper atmosphere. For this, we implement a state-ofthe-art GW scheme into a Martian General Circulation Model and study GW effects in the thermosphere. We provide a possible interpretation of the thermospheric temperatures retrieved from MAVEN.

Modeling impact of gravity waves on the thermosphere of mars with a non-orographic whole atmosphere gravity wave scheme: m-gitm application for interpreting maven measurements

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

S.

Author Tools

Chat about Author

Papers

S-drone: An Open-source Quadrotor for Experimentation in Swarm Robotics

Modeling impact of gravity waves on the thermosphere of mars with a non-orographic whole atmosphere gravity wave scheme: m-gitm application for interpreting maven measurements