Martin Kendal Ackerman

About

Martin Kendal Ackerman is a leading researcher in medical robotics and modular robotic systems, with a focus on sensor calibration and autonomous repair. His most impactful work addresses the fundamental AX=XB sensor calibration problem, critical for image-guided therapy systems like robotic surgical platforms. Ackerman pioneered the use of Euclidean-group invariants to solve this problem without known correspondence, with his 2013 paper earning 27 citations and a companion probabilistic solution receiving 24 citations. These contributions enable more accurate and practical calibration of surgical robots. In modular robotics, Ackerman developed the Hex-DMR II system, a robot capable of autonomous team repair—a significant achievement in creating resilient, self-maintaining robotic teams. His innovative work extends to origami-inspired mechanisms, including compliant flexure hinges that impart continuous rotation to moving platforms, and ultrasound calibration phantoms for image-guided surgery. Ackerman’s research bridges theoretical advances in sensor calibration with practical applications in medical robotics and autonomous systems, making him a notable figure in the field.

Research Focus

Key Achievements

Top Papers

1. 1
   Sensor calibration with unknown correspondence: Solving AX=XB using Euclidean-group invariants

   27 citations · 2013
2. 2
   A Probabilistic Solution to the AX=XB Problem: Sensor Calibration without Correspondence

   24 citations · 2013
3. 3
   A Robot Capable of Autonomous Robotic Team Repair: The Hex-DMR II System

   10 citations · 2015
4. 4
   Origami Rotors: Imparting Continuous Rotation to a Moving Platform Using Compliant Flexure Hinges

   7 citations · 2013
5. 5
   Hex-DMR: A modular robotic test-bed for demonstrating team repair

   5 citations · 2012
6. 6
   Design and development of an ultrasound calibration phantom and system

   4 citations · 2014

Key Collaborators

Contact & Links