Obstacle avoidance and trajectory optimisation for an autonomous vessel utilising MILP path planning, computer vision based perception and feedback control Article Swipe
Vittorio Garofano
,
Mike Hepworth
,
R. Shahin
,
Yusong Pang
,
Rudy R. Negenborn
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1080/20464177.2024.2330172
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1080/20464177.2024.2330172
In this study, we investigated autonomous vessel obstacle avoidance using advanced techniques within the Guidance, Navigation, and Control (GNC) framework. We propose a Mixed Integer Linear Programming (MILP) based Guidance system for robust path planning avoiding static and dynamic obstacles. For Navigation, we suggest a multi-modal neural network for perception, demonstrating the identification of obstacle type, position, and orientation using imaging sensors. Additionally, the paper compares an error-based PID control strategy and a Model Predictive Control (MPC) scheme as well. This evaluation aids in better evaluating their performance and determining their applicability within the GNC scheme. We detail the implementation of these systems, present simulation results, and offer a performance evaluation using an experimental dataset. Our findings, analysed through qualitative discussion and quantitative performance indicators, contribute to advancements in autonomous navigation and the control strategies to achieve it.
Related Topics
Concepts
Obstacle avoidance
Obstacle
Computer science
Motion planning
Collision avoidance
Scheme (mathematics)
Model predictive control
Trajectory
PID controller
Path (computing)
Control engineering
Artificial intelligence
Control (management)
Control theory (sociology)
Engineering
Robot
Mobile robot
Collision
Astronomy
Programming language
Physics
Temperature control
Mathematical analysis
Political science
Computer security
Mathematics
Law
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1080/20464177.2024.2330172
- https://www.tandfonline.com/doi/pdf/10.1080/20464177.2024.2330172?needAccess=true
- OA Status
- bronze
- Cited By
- 2
- References
- 27
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4394014866
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4394014866Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1080/20464177.2024.2330172Digital Object Identifier
- Title
-
Obstacle avoidance and trajectory optimisation for an autonomous vessel utilising MILP path planning, computer vision based perception and feedback controlWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-04-06Full publication date if available
- Authors
-
Vittorio Garofano, Mike Hepworth, R. Shahin, Yusong Pang, Rudy R. NegenbornList of authors in order
- Landing page
-
https://doi.org/10.1080/20464177.2024.2330172Publisher landing page
- PDF URL
-
https://www.tandfonline.com/doi/pdf/10.1080/20464177.2024.2330172?needAccess=trueDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
- OA URL
-
https://www.tandfonline.com/doi/pdf/10.1080/20464177.2024.2330172?needAccess=trueDirect OA link when available
- Concepts
-
Obstacle avoidance, Obstacle, Computer science, Motion planning, Collision avoidance, Scheme (mathematics), Model predictive control, Trajectory, PID controller, Path (computing), Control engineering, Artificial intelligence, Control (management), Control theory (sociology), Engineering, Robot, Mobile robot, Collision, Astronomy, Programming language, Physics, Temperature control, Mathematical analysis, Political science, Computer security, Mathematics, LawTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 2Per-year citation counts (last 5 years)
- References (count)
-
27Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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