Enhancing AUV Autonomy With Model Predictive Path Integral Control
This work addresses control challenges for AUVs in marine environments, but it is incremental as it applies an existing method to a specific domain.
The paper tackled the problem of controlling autonomous underwater vehicles (AUVs) under changing environmental conditions and constraints by investigating Model Predictive Path Integral Control (MPPI), demonstrating its superiority over classical PID and Cascade PID approaches in handling constraints through cost function incorporation.
Autonomous underwater vehicles (AUVs) play a crucial role in surveying marine environments, carrying out underwater inspection tasks, and ocean exploration. However, in order to ensure that the AUV is able to carry out its mission successfully, a control system capable of adapting to changing environmental conditions is required. Furthermore, to ensure the robotic platform's safe operation, the onboard controller should be able to operate under certain constraints. In this work, we investigate the feasibility of Model Predictive Path Integral Control (MPPI) for the control of an AUV. We utilise a non-linear model of the AUV to propagate the samples of the MPPI, which allow us to compute the control action in real time. We provide a detailed evaluation of the effect of the main hyperparameters on the performance of the MPPI controller. Furthermore, we compared the performance of the proposed method with a classical PID and Cascade PID approach, demonstrating the superiority of our proposed controller. Finally, we present results where environmental constraints are added and show how MPPI can handle them by simply incorporating those constraints in the cost function.