A Switching Nonlinear Model Predictive Control Strategy for Safe Collision Handling by an Underwater Vehicle-Manipulator System
Ioannis G. Polyzos, Konstantinos J. Kyriakopoulos
TL;DR
This work addresses safe collision handling for an Underwater Vehicle-Manipulator System (UVMS) by proposing a three-mode switching Nonlinear Model Predictive Control (NMPC) framework. The approach switches between nominal obstacle avoidance (Mode I), adaptive-timestep contact initiation (Mode II), and manipulator-driven deflection (Mode III) to either avoid contact or use contact to deflect away safely. The UVMS is modeled with Kane's method and superellipsoid-based geometry to compute distances and closing speeds, with dynamics and contact interactions integrated into the NMPC. Simulation results in MATLAB validate the method under diverse scenarios, including ocean currents and thruster failures, showing improved safety and extended operational envelope. The work contributes a robust, multi-mode control strategy that can be attached to existing UVMS controllers to enhance emergency collision handling without compromising nominal performance.
Abstract
For active intervention tasks in underwater environments, the use of autonomous vehicles is just now emerging as an active area of research. During operation, for various reasons, the robot might find itself on a collision course with an obstacle in its environment. In this paper, a switching Nonlinear Model Predictive Control (NMPC) strategy is proposed to safely handle collisions for an Underwater Vehicle-Manipulator System (UVMS). When avoiding the collision is impossible, the control algorithm takes advantage of the manipulator, using it to push against the obstacle, and deflect away from the collision. Virtual experiments are performed to demonstrate the algorithm's capability to successfully detect collisions and either avoid them, or use the manipulator to handle them appropriately without damaging sensitive areas of the vehicle.