Docking Control of a Fully-Actuated Autonomous Vessel using Model Predictive Path Integral Control

Homburger, Hannes; Wirtensohn, Stefan; Reuter, Johannes

doi:10.23919/ecc55457.2022.9838090

Cited by 4 publications

(7 citation statements)

References 11 publications

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“…Simulation Other Jia et al [6] The issue of excessive heading deviation was addressed through neural networks by berthing training data. Simulation Homburger et al [24] MPPI methodology was utilized to implement berthing control and achieve optimal control in a nonlinear system. Simulation Sawada et al [25] An automated berthing control system was propounded and trajectory tracking was performed through numerical simulation.…”

Section: Miyauchi Et Al [23]mentioning

confidence: 99%

“…In Table 2, the problems that have been disregarded or not thoroughly explored in the studies above are summarized, and the corresponding solutions are provided in this paper. Homburger et al [24] MPPI methodology was utilized to implement berthing control and achieve optimal control in a nonlinear system. Simulation Sawada et al [25] An automated berthing control system was propounded and trajectory tracking was performed through numerical simulation.…”

Section: Introductionmentioning

confidence: 99%

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Ship Autonomous Berthing Simulation Based on Covariance Matrix Adaptation Evolution Strategy

Chen

Yin

Shuzi

2023

JMSE

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Existing research on auto-berthing of ships has mainly focused on the design and implementation of controllers for automatic berthing. For the real automatic docking processes, not only do external environmental perturbations need to be taken into account but also motion paths, docking strategies and ship mechanical constraints, which are important influential factors to measure autonomous docking methods. Through a literature review of ship path planning and motion control for automatic berthing, it is found that many studies ignore the interference of the actual navigational environment, especially for ships sailing at slow speed when berthing, or do not consider the physical constraints of the steering gear and the main engine. In this paper, we propose a hybrid approach for autonomous berthing control systems based on a Linear Quadratic Regulator (LQR) and Covariance Matrix Adaptation Evolution Strategy (CMA-ES), which systematically addresses the problems involved in the berthing process, such as path planning, optimal control, adaptive berthing strategies, dynamic environmental perturbations and physically enforced structural constraints. The berthing control system based on the LQR and modified LQR-CMA-ES have been validated by simulation work. The simulation results show that the proposed method is able to achieve the automatic docking of the ship well and the system is robust and well adapted to environmental disturbances at slow speed when docking.

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Section: Miyauchi Et Al [23]mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Ship Autonomous Berthing Simulation Based on Covariance Matrix Adaptation Evolution Strategy

Chen

Yin

Shuzi

2023

JMSE

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“…It addresses the challenges posed by nonlinearity through advanced techniques such as nonlinear model formulation and optimization algorithms, enabling effective control of nonlinear systems. NMPC was used in 14 publications [71], [88], [120], [134], [140], [142], [155], [156], [166], [183], [184], [192], [193], [212].…”

Section: Mpc (Model Predictive Control)mentioning

confidence: 99%

Automated Docking for Marine Surface Vessels—A Survey

Lexau,

Breivik,

Lekkas

2023

IEEE Access

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Research on motion control systems for marine surface vessels has generated a vast academic literature and many industrial applications since the early 20th century. The recent focus on autonomous ships has sparked intensive research and innovation activities of dock-to-dock operations, including the final docking phase. Specifically, automated docking involves systems that enable vessels to dock safely, independently, and energy-efficiently at designated locations with a specific heading. Challenges include managing large sideslip angles, static and dynamic obstacles, and navigation in complex port geometries. Despite its complexity, the docking problem has received less attention compared to dynamic positioning or course-keeping systems. This paper therefore provides a thorough overview of the research on automated docking for marine surface vessels, from 1980 to June 2023. The paper introduces the Docking Characteristic Index (DCI) as a metric to identify the scope and approach of systems addressing various docking challenges, not as an evaluation of their overall quality. Using the calculated DCI-Scores, we discuss some of the works in more detail. The survey reveals a rising trend in publications and an increasing emphasis on experimental verification. Despite advancements, current methodologies exhibit trade-offs and limitations, particularly in handling dynamic obstacles, robustness against external forces, and situational awareness. The paper identifies the need for more comprehensive and integrated solutions to these challenges. As the demand for fully autonomous operations grows, the results suggest that future research should focus on developing holistic and robust docking strategies, that are verified experimentally, to achieve safe, efficient, and effective automated docking systems.

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“…Moreover, algorithms in the context of RL such as Deep Q-learning [21], Deep Deterministic Policy Gradients [22] or Actor-Critic [23] are increasingly used, which achieve good results for various applications, but the majority of them are only validated in simulation. While MPPI control was used to dock a fully actuated vessel in full-scale autonomously [24], a gap in this research area is the feature-based MPPI control of an autonomous vessel for collision avoidance. In order to also being able to perform autonomous maneuvers, such as heading for a defined target outside the port, it must be ensured that the hazards during these maneuvers are minimized.…”

Section: Maritime Application Scenariomentioning

confidence: 99%

“…According to [27], the vessel's state vector is given by a combination of the 2D pose η = (x y ψ) and the velocity vector ν = (u v r) in body-fixed coordinates, shown in Figure 2b. This model was extended in [24] to also consider the dynamics of the actuators. Thus, the transfer properties of the i = 1, 2, 3 controlled actuators can be modeled in each case by a first-order low-pass filter with an equivalent time constant T E , limited slope ã, and dead time T T represented by…”

Section: Dynamics Of the Vesselmentioning

confidence: 99%

Feature-Based MPPI Control with Applications to Maritime Systems

et al. 2022

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In this paper, a novel feature-based sampling strategy for nonlinear Model Predictive Path Integral (MPPI) control is presented. Using the MPPI approach, the optimal feedback control is calculated by solving a stochastic optimal control (OCP) problem online by evaluating the weighted inference of sampled stochastic trajectories. While the MPPI algorithm can be excellently parallelized, the closed-loop performance strongly depends on the information quality of the sampled trajectories. To draw samples, a proposal density is used. The solver’s and thus, the controller’s performance is of high quality if the sampled trajectories drawn from this proposal density are located in low-cost regions of state-space. In classical MPPI control, the explored state-space is strongly constrained by assumptions that refer to the control value’s covariance matrix, which are necessary for transforming the stochastic Hamilton–Jacobi–Bellman (HJB) equation into a linear second-order partial differential equation. To achieve excellent performance even with discontinuous cost functions, in this novel approach, knowledge-based features are introduced to constitute the proposal density and thus the low-cost region of state-space for exploration. This paper addresses the question of how the performance of the MPPI algorithm can be improved using a feature-based mixture of base densities. Furthermore, the developed algorithm is applied to an autonomous vessel that follows a track and concurrently avoids collisions using an emergency braking feature. Therefore, the presented feature-based MPPI algorithm is applied and analyzed in both simulation and full-scale experiments.

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Docking Control of a Fully-Actuated Autonomous Vessel using Model Predictive Path Integral Control

Cited by 4 publications

References 11 publications

Ship Autonomous Berthing Simulation Based on Covariance Matrix Adaptation Evolution Strategy

Ship Autonomous Berthing Simulation Based on Covariance Matrix Adaptation Evolution Strategy

Automated Docking for Marine Surface Vessels—A Survey

Feature-Based MPPI Control with Applications to Maritime Systems

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