---
title: Agnes Ngina Mwange君学位論文公聴会スライド　On the Empirical Inference of Ship Maneuvering Characteristics for Autonomous Berthing and Unberthing
tags:  #naval architecture #自動運航船 #船舶操縦性 #船舶海洋工学  
author: [大阪大学　船舶知能化領域](https://www.docswell.com/user/naoe5th_OU)
site: [Docswell](https://www.docswell.com/)
thumbnail: https://bcdn.docswell.com/page/3EK93YDMED.jpg?width=480
description: Agnes Ngina Mwangeさんの学位論文の説明スライドです
published: July 30, 26
canonical: https://www.docswell.com/s/naoe5th_OU/ZDM379-2026-07-30-074459
---
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DOCTORAL DISSERTATION
On the Empirical Inference of Ship Maneuvering
Characteristics for Autonomous Berthing and
Unberthing
Trajectory Planning, Low-Speed Maneuvering Modeling and Adaptive Ship Domain
July 2026
Department of Naval Architecture & Ocean Engineering
Division of Global Architecture
Graduate School of Engineering
The University of Osaka
Agnes Ngina Mwange
Supervisor: Professor Atsuo Maki


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OUTLINE
PART 00
CONTRIBUTION 1
CONTRIBUTION 2
CONTRIBUTION 3
PART 04
00
01
02
03
04
Introduction
Trajectory planner
Low-speed model
Adaptive ship domain
Concluding remarks
o The berthing and/or
o Overview
o Overview
o Overview
o Concluding remarks
o OCP → NLP → SQP
o Model derivation
o Spatial
o Limitations
o Deceleration
o System
unberthing problem
o Degrees of autonomy
o The safety stakes
o Research scope
guidelines
o Collision avoidance
o Feasibility and
robustness
identification
o Model validation
maneuvering
o Future work
conditions
o Publications
o Model definition
o Model validation
o Empirical vs
analytical domain
02


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PART 00
Introduction
03


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BERTHING AND UNBERTHING
UNBERTHING — DEPARTURE
BERTHING — ARRIVAL
• Removal of mooring ropes
• Departure pose
• Unberthing approach phase:
o acceleration,
o reduced steerability,
o reduced disturbances counterattacking capacity
o highly nonlinear dynamics,
o constrained maneuvering space
• Port exit
• Port entry
• Berthing approach phase:
o deceleration,
o reduced steerability,
o reduced disturbances counterattacking capacity
o highly nonlinear dynamics,
o constrained maneuvering space
• Docking pose
• Alignment phase:
o crabbing
o mooring
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MARINE INCIDENTS AND ACCIDENTS
Key challenges associated with traditional berthing and unberthing operations:
~50%
~90%
~20%
of marine accidents & incidents are
of marine incidents involve some
of maritime accidents are collisions[2].
attributed to human error [1].
form of loss of control[2] .
1. European Maritime Safety Agency (EMSA). “Annual Overview of Marine Casualties and Incidents2023.” In: (2023). url: https://emsa.europa.eu/publications/download/7639/5052/23.html (visited
on 12/08/2024)
2. Japan Transport Safety Board (JTSB). JTSB Annual Report 2021. 2021. url: https://www.mlit.go.jp/jtsb/jtsbannualreport2021.html
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AUTONOMOUS BERTHING AND UNBERTHING
Degrees of autonomy[3]
DEGREE 1
DEGREE 2
DEGREE 3
DEGREE 4
Decision support
Remote, crewed
Remote, uncrewed
Fully autonomous
• Automated processes
• Controlled from ashore,
• No crew on board
• The operating system
• Seafarers on board
with crew aboard as a
• An operations center
operating and
controlling systems.
fallback.
controls the ship
remotely.
decides and acts entirely
by itself.
• This is the focus of this
research.
3. International Maritime Organization (IMO). Outcome of the regulatory scoping exercise for the use of Maritime Autonomous Surface Ships (MASS). 2021. url:
https://wwwcdn.imo.org/localresources/en/MediaCentre/PressBriefings/Documents/MSC.1-Circ.1638%20%20Outcome%20Of%20The%20Regulatory%20Scoping%20ExerciseFor%20The%20Use%20Of%20Maritime%20Autonomous%20Surface%20Ships...%20(Secretariat).pdf
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AUTONOMOUS BERTHING AND UNBERTHING PROBLEM
01
03
Low-speed dynamics
02
Confined water
Rudder effectiveness collapses with surge velocity;
Vertical limits from shallow water (under-keel
the ship becomes underactuated and needs thrusters
clearance) and horizontal limits from quays, buoys
to control surge, sway and yaw independently.
and traffic must be respected simultaneously.
Disturbances dominate
04
Safety is regulated
At near-zero speed, wind and current forces can rival
COLREGs Rule 6 mandates a safe speed at all
or exceed the ship's own control capacity.
times[4], preserving the ability to execute emergency
maneuvers. Ports worldwide also implement reduced
speed zones (RSZs).
4. International Maritime Organization (IMO). COLREG: Convention on the International Regulations for Preventing Collisions at Sea, 1972. IMO Publication. International Maritime Organization, 2003.
isbn:9789280141672. url: https://books.google.co.jp/books?id=%5C_ZkZAQAAIAAJ
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AUTONOMOUS BERTHING AND UNBERTHING RESEARCH AREAS
Trajectory planning
Perception and sensing
Focus: Path generation from approach
fairway to berth position.
Key Challenges: Obstacle avoidance, realtime replanning, curved trajectories
Motion control
Focus: Low-speed maneuverability
and tracking control.
Key Challenges: Actuator saturation,
counteracting environmental
disturbances, propeller reversal
Maneuvering models
Focus: Low-speed maneuvering prediction
Key Challenges: Shallow water/bank
effects, propeller-hull-rudder interaction
Focus: Ship-shore relative positioning and
environmental detection
Key Challenges: GPS-denied harbors, allweather reliability, multi-sensor fusion
KEY
RESEARCH
AREAS
Human-ship interaction
Focus: Remote monitoring and
takeover systems
Key Challenges: Trust calibration,
situation awareness, minimal crew
contexts
Verification and validation
Focus: Simulation-to-full-scale transfer
Key Challenges: Standardized scenarios,
performance metrics, data acquisition
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RESEARCH GAPS
Trajectory
planning
Maneuvering
models
• Only about 1% of path/trajectory-planning algorithms address berthing [5]
• Berthing trajectory planning does not address deceleration profiles
• Low-speed models are often identified from data far narrower than berthing conditions [6,7]
• Low-speed models based on model tests introduce model-to-full-scale translation errors
• Complex models limit the real-time implementation of trajectory planners
Collision
avoidance
• Most safety envelop models (ship domains) focus on open sea or channel navigation
• Analytical ship domains lower collision probability but overlook ship-specific and port-specific
maneuvering behavior.
Verification and
validation
• Model-to-full-scale translation uncertainties associated with model-based models and algorithms
5. Ülkü Öztürk, Melih Akdağ, and Tarık Ayabakan. “A review of path planning algorithms in maritime autonomous surface ships: Navigation safety perspective.” In: Ocean Engineering 251 (2022), p. 111010.issn:
0029-8018. doi: https://doi.org/10.1016/j.oceaneng.2022.111010
6. Yoshiki Miyauchi, Atsuo Maki, Naoya Umeda, Dimas M. Rachman, and Youhei Akimoto. “System parameter exploration of ship maneuvering model for automatic docking/berthing using CMA-ES.” In: Journal of
Marine Science and Technology (June 2022). issn: 0948-4280. doi: 10.1007/s00773-022-00889-3. url: http://arxiv.org/abs/2111.06124%20https://link.springer.com/article/10.1007/s00773-022-008893%20https://link.springer.com/10.1007/s00773-022-00889-3
7. Agnes N. Mwange, Yoshiki Miyauchi, Taichi Kambara, Hiroaki Koike, Kazuyoshi Hosogaya, Atsushi Ishibashi, and Atsuo Maki. “Quantitative evaluation of full-scale ship maneuvering characteristics during
berthing and unberthing.” In: Journal of Marine Science and Technology (2025). issn: 09484280. doi:10.1007/s00773-025-01098-4
09


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HYPOTHESIS:
Key challenges in autonomous berthing and unberthing can be addressed by
systematically understanding manual navigation , then replicating and/or
enhancing those practices in autonomous operations.
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PART 01 · CONTRIBUTION 1
Trajectory Planner
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OVERVIEW
TRAJECTORY PLANNER
Berthing approach phase:
1. Ship dynamics
2. Actuation capacity
3. Environmental disturbances
4. Ship deceleration
5. Collision avoidance
6. Berthing time
Successful trajectory planning:
generation of dynamically and
practically feasible, safe
trajectories that meet
predetermined objectives.
Difference between path planning and trajectory planning:
Path planning: geometric determination of a spatial route
from start to goal without considering time or dynamics
(kinematic feasibility)
Trajectory planning: extends path planning by incorporating
temporal information such as velocities and dynamic
constraints along the path.
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SUBJECT SHIP – PRINCIPAL PARTICULARS
Ship A
TRAJECTORY PLANNER
Ship B
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SUBJECT SHIP – COORDINATE SYSTEMS
Ship A
TRAJECTORY PLANNER
Ship B
Relationship between the
two coordinate systems:
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SUBJECT SHIP – EQUATIONS OF MOTION (Factor 1)
TRAJECTORY PLANNER
8. Yasuo Yoshimura, Ikao Nakao, and Atsushi Ishibashi. “Unified Mathematical Model for Ocean and Harbour Manoeuvring.” In: International Conference on Marine Simulation and Ship Maneuverability, Aug.
2009, pp. 116–124. url: http://hdl.handle.net/2115/42969
9. H. Yasukawa and Y. Yoshimura. “Introduction of MMG standard method for ship maneuvering pre-dictions.” In: Journal of Marine Science and Technology (Japan) 20 (1 Mar. 2015), pp. 37–52. issn:09484280.
doi: 10.1007/s00773-014-0293-y
10. Donghoon Kang, Vishwanath Nagarajan, Kazuhiko Hasegawa, and Masaaki Sano. “Mathematical model of single-propeller twin-rudder ship.” In: Journal of marine science and technology 13 (2008), pp. 207–
222.
11. Toshifumi Fujiwara, Michio Ueno, and Tadashi Nimura. “Estimation of wind forces and moments acting on ships.” In: Journal of the Society of Naval Architects of Japan 1998.183 (1998), pp. 77–90.
15


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TRAJECTORY OPTIMIZATION
TRAJECTORY PLANNER
Trajectory optimization
Analytical / indirect
Direct numerical
Stochastic / heuristic
Optimality conditions
Discretize, then solve NLP
Search, no gradients needed
Pontryagin's maximum
principle
Direct collocation
Genetic algorithms
Calculus of variations
Direct shooting
Particle swarm
Dynamic programming
Pseudospectral methods
Ant colony / A* search
Isochrone method
Model predictive control
Simulated annealing
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TRAJECTORY OPTIMIZATION – OPTIMAL CONTROL PROBLEM
TRAJECTORY PLANNER
where,
12. Atsuo Maki, Youhei Akimoto, and Umeda Naoya. “Application of optimal control theory based on the evolution strategy (CMA-ES) to automatic berthing (part: 2).” In: Journal of Marine Science and
Technology 26 (2021), pp. 835–845
13. Dimas M Rachman, Atsuo Maki, Yoshiki Miyauchi, and Naoya Umeda. “Warm-started semi online trajectory planner for ship’ s automatic docking (berthing).” In: Ocean Engineering 252 (2022), p. 111127
17


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OCP – DIRECT MULTIPLE SHOOTING
TRAJECTORY PLANNER
• Time grid 𝑡0 , 𝑡f is discretized into 𝑁 segments:
𝑡0 , 𝑡1 , … , 𝑡𝑁−1 , 𝑡f . Let 𝑖 denote the 𝑖th segment
such that, 𝑖 = 1,2, … 𝑁.
• Let 𝑘 denote the end point of the segments, also
denoted as knots, such that 𝑘 = 1,2, … . 𝑁𝑘 . 𝑁𝑘 =
𝑁+1
• Discretize states and control variables
• Integrate along each segment and introduce
constraints to reduce defects between segments
• For any consecutive segments, constraints
require:
𝐱𝑘− = 𝐱𝑘+
• Pro: breaks sensitivity present in single shooting
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ACTUATOR LIMITS (Factor 2 and 3)
TRAJECTORY PLANNER
Actuator (control limits) (𝑢min , 𝑢max ):
(i) Port rudder angles: δp,min = −1050 , δp,max = −450
(ii) Starboard rudder angles: δs,min = 450 , δs,max = 1050
(iii) Propeller revolution: 𝑛p,min = 0, 𝑛p,max = 10rps
(iv) Bow-thruster revolution: 𝑛bt,min = −30rps, 𝑛bt,max = 30rps
7. Agnes N. Mwange, Yoshiki Miyauchi, Taichi Kambara, Hiroaki Koike, Kazuyoshi Hosogaya, Atsushi Ishibashi, and Atsuo Maki. “Quantitative evaluation of full-scale ship maneuvering characteristics during berthing
and unberthing.” In: Journal of Marine Science and Technology (2025). issn: 09484280. doi:10.1007/s00773-025-01098-4
19


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SHIP DECELERATION (Factor 4)
TRAJECTORY PLANNER
Maritime ports worldwide implement reduced speed zones
(RSZs) where ship should maintain a set speed or less.
~10 knots
14. K Inoue, SETA Hiroaki, and MASUDA Kenji. “Guidelines for Speed Reduction in Berthing Manoeuvre.” In: The Journal of Japan Institute of Navigation (2002), pp. 169–176.
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SHIP DECELERATION
TRAJECTORY PLANNER
Ship deceleration speed limits equation:
12. Atsuo Maki, Youhei Akimoto, and Umeda Naoya. “Application of optimal control theory based on the evolution strategy (CMA-ES) to automatic berthing (part: 2).” In: Journal of Marine Science and
Technology 26 (2021), pp. 835–845
13. Dimas M Rachman, Atsuo Maki, Yoshiki Miyauchi, and Naoya Umeda. “Warm-started semi online trajectory planner for ship’ s automatic docking (berthing).” In: Ocean Engineering 252 (2022), p. 111127
21


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COLLISION AVOIDANCE (Factor 5)
TRAJECTORY PLANNER
Point in polygon problem
13. Dimas M Rachman, Atsuo Maki, Yoshiki Miyauchi, and Naoya Umeda. “Warm-started semionline trajectory planner for ship’ s automatic docking
(berthing).” In: Ocean Engineering 252 (2022), p. 111127
15. Yoshiki Miyauchi, Ryohei Sawada, Youhei Akimoto, Naoya Umeda, and Atsuo Maki. “Optimization on planning of trajectory and control of autonomous
berthing and unberthing for the realistic port geometry.” In: Ocean Engineering 245 (2022), p. 110390. issn: 0029-8018. doi:
https://doi.org/10.1016/j.oceaneng.2021.110390
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OCP
NONLINEAR PROGRAMMING PROBLEM (NLP)
TRAJECTORY PLANNER
23(a)


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OCP
NONLINEAR PROGRAMMING PROBLEM (NLP)
TRAJECTORY PLANNER
23(b)


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TRAJECTORY PLANNER EVALUATION
TRAJECTORY PLANNER
(i) Comparison with an existing method –
Validation
(ii) Six scenario trajectories, with vs without speed
reduction. Wind disturbances (UT up to ~0.75
m/s) applied throughout; solved with SQP in
MATLAB– Simulation results
(iii) Feasibility study: - Planner evaluation
a) Grid-based simulations
b) Stochastic simulation conditions
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TRAJECTORY PLANNER - VALIDATION
TRAJECTORY PLANNER
Ship A
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TRAJECTORY PLANNER - VALIDATION
TRAJECTORY PLANNER
Ship B
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TRAJECTORY PLANNER – SIMULATION RESULTS
Case 1
TRAJECTORY PLANNER
Case 2
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TRAJECTORY PLANNER – SIMULATION RESULTS
Case 3
TRAJECTORY PLANNER
Case 4
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TRAJECTORY PLANNER – SIMULATION RESULTS
Case 5
TRAJECTORY PLANNER
Case 6
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TRAJECTORY PLANNER – EVALUATION
1. Grid-based simulations
TRAJECTORY PLANNER
Simulation results
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TRAJECTORY PLANNER – EVALUATION
2. Stochastic simulation conditions
TRAJECTORY PLANNER
Computation time
50%
62%
71%
75%
initial
recomputation 1
recomputation 2
recomputation 3
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CONTRIBUTION 1 - TAKEAWAYS
TRAJECTORY PLANNER
Linear guess, single layer
Prospective improvement/ future research focus
Matches an expensive warm-started baseline
➢ What is the role of maneuvering model in
while removing the global-optimization step.
planner’s computation time?
➢ Besides control inputs guess, what other
variables can be adjusted before
Practical & robust
Actuator limits, wind & ship domain included; 75%
feasibility with cheap recomputation.
recomputation?
➢ Does the safety envelop (ship domain) match
actual ship navigation?
Deceleration guidelines = safety
Embedding empirical speed limits keeps the
approach in the empirically recommended region.
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PART 02 · CONTRIBUTION 2
A linear, low-speed
maneuvering model
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LOW-SPEED MODEL – OVERVIEW
LOW-SPEED MODEL
PROBLEM A
PROBLEM B
Nonlinear – considerably accurate but expensive
Scale effects & narrow data
The MMG model gives the planner accuracy but possibly
Tank-test coefficients need uncertain model-to-full-scale
increases computation cost — from Contribution 1.
correction [16,17]; many low-speed models are extrapolated
from too-narrow ranges [6,7].
R E SP ON SE
Make the model deliberately simple — and identify it directly from full-scale data,
so there is nothing to re-scale.
16. Michio Ueno, Yoshiaki Tsukada, and Yasushi Kitagawa. “Rudder effectiveness correction for scale model ship testing.” In: Ocean Engineering 92 (2014), pp. 267–284. issn: 00298018. doi:
10.1016/j.oceaneng.2014.10.006.
17. Michio Ueno and Yoshiaki Tsukada. “Rudder effectiveness and speed correction for scale model ship testing.” In: Ocean Engineering 109 (2015), pp. 495–506. issn: 0029-8018. doi:
10.1016/J.OCEANENG.2015.09.041.
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SUBJECT SHIP
LOW-SPEED MODEL
A full-scale
coastal ship
Full-scale logs from this
vessel ground both the
low-speed model and the
ship domain
(Contribution 3).
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MANEUVERING MODEL
Linearize about hover, not zero speed
LOW-SPEED MODEL
The VecTwin rudder can redirect propeller thrust so net surge
force is zero — the ship is stationary and can perform linearized
motions, including crabbing (pure lateral translation) [18] .
THE HOVER EQUILIBRIUM
Rudder angles |δhover|
Propeller n₀
Net surge force
70°–80°
constant rps
≈0
Conventional
Equilibrium at straight ahead motion; constant forward
speed.
This contribution
Equilibrium at hover condition (stationary); zero forward
18. Dimas M Rachman, Yusuke Aoki, Yoshiki Miyauchi, Naoya Umeda, and Atsuo Maki.
“Experimental low-speed positioning system with VecTwin rudder for automatic docking
(berthing).” In: Journal of Marine Science and Technology 28.3 (2023), pp. 689–703.
speed.
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MANEUVERING MODEL
LOW-SPEED MODEL
Relationship between the
two coordinate systems:
Nonlinear
Linear
Hydrodynamic forces and moment:
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MANEUVERING MODEL – TAYLOR EXPANSION
LOW-SPEED MODEL
First order Taylor expansion about the hover (initial
equilibrium) condition:
When hovering,
are zero.
Additionally, since the ship is stationary,
By denoting the partial derivatives as
we have:
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MANEUVERING MODEL
Wind-induced forces and moment:
LOW-SPEED MODEL
Rigid - body kinetics
[19]
11. Toshifumi Fujiwara, Michio Ueno, and Tadashi Nimura. “Estimation of wind forces and moments acting on ships.” In: Journal of the Society of Naval Architects of Japan 1998.183 (1998), pp. 77–90.
19. Thor I. Fossen. “Handbook of Marine Craft Hydrodynamics and Motion Control.” In: (2011). doi: 10.1002/9781119575016
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MODEL PARAMETERS IDENTIFICATION
LOW-SPEED MODEL
Data
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MODEL PARAMETERS IDENTIFICATION
LOW-SPEED MODEL
Data
(a)
(c)
(b)
(d)
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MODEL PARAMETERS IDENTIFICATION
LOW-SPEED MODEL
Training Data vs Testing Data
States
Control inputs
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OPTIMAL MODEL PARAMETERS
LOW-SPEED MODEL
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MODEL TESTING AND VALIDATION
LOW-SPEED MODEL
Port 1
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MODEL TESTING AND VALIDATION
LOW-SPEED MODEL
Port 2
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MODEL TESTING AND VALIDATION
LOW-SPEED MODEL
Port 3
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MODEL TESTING AND VALIDATION
LOW-SPEED MODEL
Port 4
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MODEL TESTING AND VALIDATION
LOW-SPEED MODEL
Port 5
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CONTRIBUTION 2 - TAKEAWAYS
LOW-SPEED MODEL
Hover-based formulation
Full-scale, no scaling
Linearizing about the VecTwin hover state — not straight-
Identified directly from operational data — no tank tests,
ahead motion — is a novel, physically apt reference for
no model-to-full-scale correction.
berthing.
Simple, yet reliably accurate
Ready for the loop
Tracks full-scale motion closely across ports.
Lightweight and linear — a promising candidate to replace
the MMG model in the planner.
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PART 03 · CONTRIBUTION 3
An adaptive ship
domain
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OVERVIEW
SHIP DOMAIN
• A ship domain is the keep-clear region around a hull [20,21].
• Analytical and AIS-based domains dominate existing
PORTS CONSTRAIN IN TWO PLANES
Vertical
Shallow water limits under-keel
clearance.
research.
Quays, buoys, leading lines & traffic
• Existing research focus on open sea and restricted waters
Horizontal
bound the space — even with no
moving obstacle.
(channels) navigation.
Objectives:
i
Environment-aware boundary
Redefine navigable space with
vertical (shallow-water) +
horizontal (infrastructure)
constraints.
ii
Anisotropic, speed-scaled
domain
Four semi-axes that differ from
each other and vary with ship size
iii
Identify from full-scale data
Identify & validate parameters on
real berthing/unberthing records
and speed.
20. Rafał Szłapczyński and Joanna Szłapczyńska. “Review of ship safety domains: Models and applications.” In: Ocean Engineering (2017). doi: 10.1016/J.OCEANENG.2017.09.020.
21. Ahmet Baran, Remzi Fışkın, and Hakkġ Kiźi. “A Research on Concept of Ship Safety Domain.” In: TransNav: International Journal on Marine Navigation and Safety of Sea Transportation (2018).
doi:10.12716/1001.12.01.04.
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SUBJECT SHIP
SHIP DOMAIN
A full-scale
coastal ship
Full-scale logs from this
vessel ground both the
low-speed model and the
ship domain
(Contribution 3).
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SUBJECT SHIP
SHIP DOMAIN
Closest Point of Approach (CPA)
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REDEFINED NAVIGABLE SPACE
SHIP DOMAIN
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REDEFINED NAVIGABLE SPACE
SHIP DOMAIN
Port navigation
limit (red buoy)
Starboard limit
(green buoy)
Obstruction
(yellow)
Other aids /
leading lines
The ship is measured against the operationally navigable harbor — not the raw
geometry.
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SHIP DOMAIN MODEL
SHIP DOMAIN
Model equations:
where,
Coordinates along the quaternion curve of the elliptical
domain:
where,
Ship speed influence coefficient, unique for each axis.
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MODEL PARAMETERS IDENTIFICATION
SHIP DOMAIN
Data acquisition
At each point, distance to the nearest boundary (dCPA) is
measured fore, starboard, aft, and port — four half-extents
that become the domain's semi-axes.
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MODEL PARAMETERS IDENTIFICATION
SHIP DOMAIN
Data
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MODEL PARAMETERS IDENTIFICATION
SHIP DOMAIN
Training data vs Testing data
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OPTIMAL MODEL PARAMETERS
SHIP DOMAIN
Ship domain at Port A
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MODEL TESTING AND VALIDATION
Ship domain at Port B
SHIP DOMAIN
Ship domain at Port C
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MODEL TESTING AND VALIDATION
Ship domain at Port D
SHIP DOMAIN
Ship domain at Port E
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MODEL TESTING AND VALIDATION
SHIP DOMAIN
Analytical vs Empirical
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CONTRIBUTION 3 - TAKEAWAYS
SHIP DOMAIN
Empirical, not analytical
Speed-dependent & anisotropic
Four semi-axes learned from 152 full-scale maneuvers
Axes anisotropically expand and elongate with ship speed.
across five ports.
Environment-aware
Implementation in the planner?
Measured against a boundary that folds in depth &
Redefine the safety envelop empirically — the collision
infrastructure; pilot-bounded.
limit in Contribution 1.
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PART 04
Concluding remarks
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SUMMARY
CONTRIBUTION 1
CONCLUSION
First berthing planner to embed empirical deceleration guidelines as constraints — and to show a
linear guess replaces double-layered warm-starting.
CONTRIBUTION 2 A low-speed model linearized about the hover condition, identified from full-scale data, with the
parameter-cancellation problem handled explicitly.
CONTRIBUTION 3
∑
An empirical, adaptive, anisotropic ship domain for berthing — environment-aware and validated
across ports.
A coherent methodology for leveraging full-scale operational data for autonomous berthing algorithms.
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LIMITATIONS
CONCLUSION
Not yet integrated end-to-end
Planner edge cases
The model & domain are validated separately — not yet run
The linear guess still struggles to converge on some hard
live inside a/the planner.
configurations; recomputation mitigates, not eliminates.
Model scope
Domain size
Identified for one ship — generalization needs model
Current - one ship, five ports. Standardization require
modifications and re-identification.
validation across many ship types & environments.
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FUTURE WORK
CONCLUSION
PLANNER
LINEAR MODEL
SHIP DOMAIN
➢ Adaptively tune the linear guess for
➢ Quantify the from full-scale effects
➢ Many ships & ports; find invariants
complex and real port
of trim and sinkage in ship motions;
➢ Quantify a unified safety criterion
environments
extend validity into shallow water.
linking actuation capacity to
➢ Swap in the linear model for the
MMG.
➢ Explore generalization across
domain size.
different ship types
➢ Compare analytical vs proposed
empirical domain in trajectory
planning
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PUBLICATIONS
CONCLUSION
A practical and online trajectory planner for autonomous ships' berthing, incorporating speed control
JOURNAL
Agnes N Mwange, Dimas M Rachman, Rin Suyama, and Atsuo Maki. In: Journal of Marine Science and Technology (2025), pp. 238 –
254.DOI: 10.1007/S00773-025-01048-0
Quantitative evaluation of full-scale ship maneuvering characteristics during berthing and unberthing
JOURNAL
Agnes N. Mwange, Yoshiki Miyauchi, Taichi Kambara, Hiroaki Koike, Kazuyoshi Hosogaya, Atsushi Ishibashi, and Atsuo Maki. In:
Journal of Marine Science and Technology (2025). ISSN: 09484280. DOI: 10.1007/s00773-025-01098-4.
Online Trajectory Planner that Enhances Berthing Safety through Incorporating Speed Reduction Guidelines
CONFERENCE
Agnes N. Mwange, Dimas M. Rachman, and Atsuo Maki. In: Conference Proceedings The Japan Society of Naval Architects and Ocean
Engineers 37 (2023), pp. 61 – 63.
Development and Identification of a Linear Low-Speed Ship Maneuvering Model from Full-Scale Data
SUBMITTED
TO BE
SUBMITTED
Agnes N. Mwange, Taichi Kambara, Kouki Wakita, Kazuyoshi Hosogaya, and Atsuo Maki. 2026. arXiv:2607.01739 [eess.SY]. url:
https://arxiv.org/abs/2607.01739.
Empirical Modeling of a Ship Domain for Berthing and Unberthing Using Full-Scale Data
Agnes N. Mwange, Kazuyoshi Hosogaya, Atsushi Ishibashi, and Atsuo Maki.
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DOCTORAL DISSERTATION DEFENSE
Thank you…
THE UNIVERSITY OF OSAKA · JULY 2026