David Brandt

About

David Brandt is a robotics researcher whose career spans two complementary domains: modular self-reconfigurable robotics and advanced control of industrial robot manipulators. His early work established foundational knowledge in the field of self-reconfigurable robots — machines built from interchangeable modules capable of autonomously reshaping themselves to adapt to new tasks and environments. His 2010 introductory paper on self-reconfigurable robots has garnered 139 citations, reflecting its role as a key reference in the field. Through contributions to the ATRON robot platform, unified simulation environments, and motion planning algorithms, Brandt helped build the theoretical and practical infrastructure that enabled broader research in modular robotics. His comparative analysis of A* and RRT-Connect planning techniques further shaped how researchers approach reconfiguration challenges. In later work, Brandt pivoted toward precision control of collaborative industrial robots, producing highly cited studies on nonlinear joint dynamics modeling and friction compensation — both accumulating over 80 citations combined by 2020. His vibration suppression research via input shaping rounds out a portfolio that bridges exploratory robotics research with real-world industrial application, making him a versatile and impactful figure in modern robotics engineering.

Research Focus

Key Achievements

Top Papers

1. 1
   Self-reconfigurable robots: an introduction

   139 citations · 2010
2. 2
   Comprehensive modeling and identification of nonlinear joint dynamics for collaborative industrial robot manipulators

   82 citations · 2020
3. 3
   ATRON Robots: Versatility from Self-Reconfigurable Modules

   52 citations · 2007
4. 4
   A unified simulator for Self-Reconfigurable Robots

   44 citations · 2008
5. 5
   Adaptive feedforward control of a collaborative industrial robot manipulator using a novel extension of the Generalized Maxwell-Slip friction model

   44 citations · 2020
6. 6
   Comparison of A and RRT-Connect Motion Planning Techniques for Self-Reconfiguration Planning

   39 citations · 2006
7. 7
   A new meta-module for controlling large sheets of ATRON modules

   27 citations · 2007
8. 8
   Exploit Morphology to Simplify Docking of Self-reconfigurable Robots

   18 citations · 2009
9. 9
   Efficient enumeration of modular robot configurations and shapes

   14 citations · 2013
10. 10
    Smooth online time-varying input shaping with fractional delay FIR filtering

    12 citations · 2019

Key Collaborators

Contact & Links