Frank Mascarich

1.7k citations
25 papers · 999 indexed · 1 hit paper · h-index 16
Topics
Robotics and Sensor-Based Localization (18 papers)Robotic Path Planning Algorithms (13 papers)Modular Robots and Swarm Intelligence (5 papers)
Partner nations
United StatesNorwayIndia

In The Last Decade

Frank Mascarich

25 papers receiving 964 citations

Hit Papers

CERBERUS in the DARPA Subterranean Challenge2022202620232024202250100150

Peers

Frank Mascarich
Comparison fields: 5 of 66
  • Aerospace Engineering 724
  • Computer Vision and Pattern Recognition 634
  • Control and Systems Engineering 158
  • Mechanical Engineering 132
  • Geology 115
Replace Kazuki Otake with:
Kazuki Otake Japan
Tung Dang United States
Huan Nguyen Norway
Korbinian Schmid Germany
Lionel Heng Switzerland
Christoforos Kanellakis Sweden
Shehryar Khattak United States
Jonathan Kelly Canada
Teodor Tomić Germany
Titus Cieslewski Switzerland
Frank Mascarich relative to Kazuki Otake Japan Kazuki Otake's profile →
Citations per field
00.5×7.4×
Kazuki Otake · 1×
Citations per year

Countries citing papers authored by Frank Mascarich

Since Specialization
Citations

This map shows the geographic impact of Frank Mascarich's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Frank Mascarich with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Frank Mascarich more than expected).

Fields of papers citing papers by Frank Mascarich

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Frank Mascarich. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Frank Mascarich. The network helps show where Frank Mascarich may publish in the future.

Co-authorship network of co-authors of Frank Mascarich

This figure shows the co-authorship network connecting the top 25 collaborators of Frank Mascarich. A scholar is included among the top collaborators of Frank Mascarich based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Frank Mascarich. Frank Mascarich is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
#WorkIndexed citations
1
CERBERUS in the DARPA Subterranean Challengebreakdown →
155
2 9
3 22
4 18
5 99
6 157
7 8
8 14
9 44
10 14
11 35
12 114
13 28
14
Field-hardened Robotic Autonomy for Subterranean Exploration
18
15 53
16 14
17 41
18 29
19 10
20 6

About Frank Mascarich

Frank Mascarich is a scholar working on Aerospace Engineering, Computer Vision and Pattern Recognition and Geology, having authored 25 papers that have together received 999 indexed citations. Recurring topics across this work include Robotics and Sensor-Based Localization (18 papers), Robotic Path Planning Algorithms (13 papers) and Modular Robots and Swarm Intelligence (5 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (634 citations), Aerospace Engineering (724 citations) and Geology (115 citations). Frank Mascarich has collaborated with scholars based in United States, Norway and India. Frequent co-authors include Kostas Alexis, Shehryar Khattak, Tung Dang, Christos Papachristos, Huan Nguyen, Marco Hutter, Marco Tranzatto, Mihir Kulkarni, Mihir Dharmadhikari and Olov Andersson. Their work appears in journals such as Science Robotics, Autonomous Robots and Journal of Field Robotics.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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