A reinforcement learning control approach for underwater manipulation under position and torque constraints
This work is significant for marine operations and robotics engineers who require robust control of underwater manipulators in challenging and uncertain environments.
This paper addresses the challenge of controlling underwater manipulators under position and torque constraints, where dynamic model uncertainties and environmental disturbances are prevalent. The authors propose a novel reinforcement learning low-level controller, based on an actor-critic architecture, which demonstrates advantages in simulation for the Reach Alpha 5 underwater manipulator.
In marine operations underwater manipulators play a primordial role. However, due to uncertainties in the dynamic model and disturbances caused by the environment, low-level control methods require great capabilities to adapt to change. Furthermore, under position and torque constraints the requirements for the control system are greatly increased. Reinforcement learning is a data driven control technique that can learn complex control policies without the need of a model. The learning capabilities of these type of agents allow for great adaptability to changes in the operative conditions. In this article we present a novel reinforcement learning low-level controller for the position control of an underwater manipulator under torque and position constraints. The reinforcement learning agent is based on an actor-critic architecture using sensor readings as state information. Simulation results using the Reach Alpha 5 underwater manipulator show the advantages of the proposed control strategy.