Estimation of Thruster Configurations for Reconfigurable Modular Underwater Robots

Abstract

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.

Modular Hydraulic Propulsion: A robot that moves by routing fluid through itself

“…The algorithm is tested using a robot with configurable thrusters; translation and rotation are both achieved. This work is followed up in [20], which proposes an algorithm to learn the thruster configuration of the robot. In [21], a system of underwater modular robots is described; their body shapes and controllers are co-evolved.…”

Path Planning Algorithms for Robotic Underwater Sensing in a Network of Sensors

“…While this work presents simulated results, it is motivated by our prior work on underwater sensor network with depth adjustment capabilities, called AquaNodes [20], and an underwater robot that can communicate and collect data from the sensor nodes [7,8]. Figure 1 shows a picture of the system.…”

Modular Fluidic Propulsion Robots

“…An inertial measurement unit provides input to the controller. In [38], the AMOUR robot can learn its own thruster configuration.…”