Brandon Luders

1.2k total citations
18 papers, 804 citations indexed

About

Brandon Luders is a scholar working on Computer Vision and Pattern Recognition, Aerospace Engineering and Control and Systems Engineering. According to data from OpenAlex, Brandon Luders has authored 18 papers receiving a total of 804 indexed citations (citations by other indexed papers that have themselves been cited), including 16 papers in Computer Vision and Pattern Recognition, 9 papers in Aerospace Engineering and 6 papers in Control and Systems Engineering. Recurrent topics in Brandon Luders's work include Robotic Path Planning Algorithms (16 papers), Robotics and Sensor-Based Localization (6 papers) and Autonomous Vehicle Technology and Safety (4 papers). Brandon Luders is often cited by papers focused on Robotic Path Planning Algorithms (16 papers), Robotics and Sensor-Based Localization (6 papers) and Autonomous Vehicle Technology and Safety (4 papers). Brandon Luders collaborates with scholars based in United States and Sweden. Brandon Luders's co-authors include Jonathan P. How, Georges S. Aoude, Nicholas Roy, Joshua Joseph, Daniel S. Levine, Mangal Kothari, Sertaç Karaman, Emilio Frazzoli, KK Lee and David S. Levine and has published in prestigious journals such as Journal of Guidance Control and Dynamics, Autonomous Robots and Journal of Field Robotics.

In The Last Decade

Brandon Luders

18 papers receiving 771 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Brandon Luders United States 14 530 319 250 213 180 18 804
Nazım Kemal Üre Türkiye 17 268 0.5× 417 1.3× 185 0.7× 385 1.8× 199 1.1× 70 1.0k
Gaston Fiore United States 6 814 1.5× 395 1.2× 523 2.1× 453 2.1× 103 0.6× 7 1.1k
Alain Lambert France 15 346 0.7× 346 1.1× 187 0.7× 188 0.9× 176 1.0× 62 721
Jan Becker Germany 4 405 0.8× 184 0.6× 411 1.6× 204 1.0× 167 0.9× 6 948
Michael Sokolsky United States 4 389 0.7× 174 0.5× 407 1.6× 178 0.8× 169 0.9× 6 927
Chris Urmson United States 12 491 0.9× 172 0.5× 473 1.9× 187 0.9× 90 0.5× 21 822
Edward Schmerling United States 10 453 0.9× 265 0.8× 143 0.6× 241 1.1× 138 0.8× 23 675
Nico Kaempchen Germany 15 329 0.6× 218 0.7× 674 2.7× 258 1.2× 225 1.3× 20 1.0k
Mihail Pivtoraiko United States 12 1.0k 2.0× 505 1.6× 241 1.0× 604 2.8× 195 1.1× 19 1.3k

Countries citing papers authored by Brandon Luders

Since Specialization
Citations

This map shows the geographic impact of Brandon Luders'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 Brandon Luders with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Brandon Luders more than expected).

Fields of papers citing papers by Brandon Luders

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Brandon Luders. 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 Brandon Luders. The network helps show where Brandon Luders may publish in the future.

Co-authorship network of co-authors of Brandon Luders

This figure shows the co-authorship network connecting the top 25 collaborators of Brandon Luders. A scholar is included among the top collaborators of Brandon Luders 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 Brandon Luders. Brandon Luders is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

18 of 18 papers shown
1.
Luders, Brandon, et al.. (2016). Wind Uncertainty Modeling and Robust Trajectory Planning for Autonomous Parafoils. Journal of Guidance Control and Dynamics. 39(7). 1614–1630. 26 indexed citations
2.
Luders, Brandon & Jonathan P. How. (2014). An optimizing sampling-based motion planner with guaranteed robustness to bounded uncertainty. 1. 771–777. 7 indexed citations
3.
Walter, Matthew R., Matthew Antone, Ekapol Chuangsuwanich, et al.. (2014). A Situationally Aware Voice‐commandable Robotic Forklift Working Alongside People in Unstructured Outdoor Environments. Journal of Field Robotics. 32(4). 590–628. 28 indexed citations
4.
Luders, Brandon, Sertaç Karaman, & Jonathan P. How. (2013). Robust Sampling-based Motion Planning with Asymptotic Optimality Guarantees. AIAA Guidance, Navigation, and Control (GNC) Conference. 76 indexed citations
5.
Luders, Brandon, et al.. (2013). Robust Trajectory Planning for Autonomous Parafoils under Wind Uncertainty. DSpace@MIT (Massachusetts Institute of Technology). 18 indexed citations
6.
Aoude, Georges S., Brandon Luders, Joshua Joseph, Nicholas Roy, & Jonathan P. How. (2013). Probabilistically safe motion planning to avoid dynamic obstacles with uncertain motion patterns. Autonomous Robots. 35(1). 51–76. 203 indexed citations
7.
Levine, Daniel S., Brandon Luders, & Jonathan P. How. (2013). Information-Theoretic Motion Planning for Constrained Sensor Networks. Journal of Aerospace Information Systems. 10(10). 476–496. 17 indexed citations
8.
Luders, Brandon, Daniel S. Levine, Sameera Ponda, & Jonathan P. How. (2011). Information-rich Task Allocation and Motion Planning for Heterogeneous Sensor Platforms. 2 indexed citations
9.
Ponda, Sameera, et al.. (2011). Decentralized Information-Rich Planning and Hybrid Sensor Fusion for Uncertainty Reduction in Human-Robot Missions. AIAA Guidance, Navigation, and Control Conference. 8 indexed citations
10.
Luders, Brandon & Jonathan P. How. (2011). Probabilistic Feasibility for Nonlinear Systems with Non-Gaussian Uncertainty using RRT. 13 indexed citations
11.
Luders, Brandon, Georges S. Aoude, Joshua Joseph, Nicholas Roy, & Jonathan P. How. (2011). Probabilistically Safe Avoidance of Dynamic Obstacles with Uncertain Motion Patterns. DSpace@MIT (Massachusetts Institute of Technology). 14 indexed citations
12.
Aoude, Georges S., Brandon Luders, David S. Levine, & Jonathan P. How. (2010). Threat-aware path planning in uncertain urban environments. 39 indexed citations
13.
Aoude, Georges S., Brandon Luders, KK Lee, Daniel S. Levine, & Jonathan P. How. (2010). Threat assessment design for driver assistance system at intersections. 1855–1862. 77 indexed citations
14.
Levine, Daniel S., Brandon Luders, & Jonathan P. How. (2010). Information-Rich Path Planning with General Constraints Using Rapidly-Exploring Random Trees. 55 indexed citations
15.
Teller, Seth, Matthew R. Walter, Matthew Antone, et al.. (2010). A voice-commandable robotic forklift working alongside humans in minimally-prepared outdoor environments. Scholarworks@UNIST (Ulsan National Institute of Science and Technology). 526–533. 47 indexed citations
16.
Luders, Brandon, Mangal Kothari, & Jonathan P. How. (2010). Chance Constrained RRT for Probabilistic Robustness to Environmental Uncertainty. AIAA Guidance, Navigation, and Control Conference. 119 indexed citations
17.
Aoude, Georges S., Brandon Luders, Jonathan P. How, & T. Pilutti. (2010). Sampling-Based Threat Assessment Algorithms for Intersection Collisions Involving Errant Drivers. IFAC Proceedings Volumes. 43(16). 581–586. 15 indexed citations
18.
Luders, Brandon, et al.. (2010). Bounds on tracking error using closed-loop rapidly-exploring random trees. 5406–5412. 40 indexed citations

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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