Estimation of Thruster Configurations for Reconfigurable Modular Underwater Robots

  • Marek DoniecEmail author
  • Carrick Detweiler
  • Daniela Rus
Part of the Springer Tracts in Advanced Robotics book series (STAR, volume 79)


We present an algorithm for estimating thruster configurations of underwater vehicles with reconfigurable thrusters. The algorithm estimates each thruster’s effect on the vehicle’s attitude and position. The estimated parameters are used to maintain the robot’s attitude and position.

The algorithm operates by measuring impulse response of individual thrusters and thruster combinations. Statistical metrics are used to select data samples. Finally, we compute a Moore-Penrose pseudoinverse, which is used to project the desired attitude and position changes onto the thrusters.

We verify our algorithm experimentally using our robot AMOUR. The robot consists of a main body with a variable number of thrusters that can be mounted at arbitrary locations. It utilizes an IMU and a pressure sensor to continuously compute its attitude and depth. We use the algorithm to estimate different thruster configurations and show that the estimated parameters successfully control the robot. The gathering of samples together with the estimation computation takes approximately 40 seconds. Further, we show that the performance of the estimated controller matches the performance of a manually tuned controller. We also demonstrate that the estimation algorithm can adapt the controller to unexpected changes in thruster positions. The estimated controller greatly improves the stability and maneuverability of the robot when compared to the manually tuned controller.


Inverse Model Underwater Vehicle Autonomous Underwater Vehicle Thrust Vector Modular Robot 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer-Verlag GmbH Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Computer Science and Artificial Intelligence LaboratoryMassachusetts Institute of TechnologyCambridgeUSA

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