Enhancing AUV Autonomy With Model Predictive Path Integral Control
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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
VIMPPI: Enhancing Model Predictive Path Integral Control with Variational Integration for Underactuated Systems
This paper presents VIMPPI, a novel control approach for underactuated double pendulum systems developed for the AI Olympics competition. We enhance the Model Predictive Path Integral framework by incorporating variation…
Low-pass sampling in Model Predictive Path Integral Control
Model Predictive Path Integral (MPPI) control is a widely used sampling-based approach for real-time control, offering flexibility in handling arbitrary dynamics and cost functions. However, the original MPPI suffers fro…
Autonomous RacingSafe Importance Sampling in Model Predictive Path Integral Control
We introduce the notion of importance sampling under embedded barrier state control, titled Safety Controlled Model Predictive Path Integral Control (SC-MPPI). For robotic systems operating in an environment with multipl…
Reservoir Predictive Path Integral Control for Unknown Nonlinear Dynamics
Neural networks have found extensive application in data-driven control of nonlinear dynamical systems, yet fast online identification and control of unknown dynamics remain central challenges. To meet these challenges, …
Control of Legged Robots using Model Predictive Optimized Path Integral
Legged robots possess a unique ability to traverse rough terrains and navigate cluttered environments, making them well-suited for complex, real-world unstructured scenarios. However, such robots have not yet achieved th…