# A Kinodynamic Model for Dubins-Based Trajectory Planning in Precision Oyster Harvesting

## TL;DR

A new model for boat motion planning improves precision in oyster harvesting by combining steering input with spatial coordinates.

## Contribution

A hybrid kinodynamic model integrating Dubins and Nomoto models for direct steering-to-coordinate mapping in underactuated boats.

## Key findings

- Field experiments achieved turning radius errors within 1.5% and sub-meter trajectory accuracy.
- Average trajectory offsets of 0.01 m, 1.35 m, and 0.42 m were observed across varied path complexities.
- The model enables efficient motion planning for autonomous oyster harvesting under real-world constraints.

## Abstract

What are the main findings?
We developed a novel hybrid kinodynamic model combining the Dubins and Nomoto models to map steering input directly to spatial coordinates for underactuated boats.Field experiments in oyster aquaculture environments showed turning radius errors within 1.5% and trajectory following accuracy with sub-meter precision across various path complexities.

We developed a novel hybrid kinodynamic model combining the Dubins and Nomoto models to map steering input directly to spatial coordinates for underactuated boats.

Field experiments in oyster aquaculture environments showed turning radius errors within 1.5% and trajectory following accuracy with sub-meter precision across various path complexities.

What are the implications of the main findings?
Enable efficient and precise motion planning for autonomous oyster harvesting vessels under real-world constraints like starboard-turn-only paths.Provide a scalable foundation for integrating control systems and MIMO-compatible models in future aquaculture automation frameworks.

Enable efficient and precise motion planning for autonomous oyster harvesting vessels under real-world constraints like starboard-turn-only paths.

Provide a scalable foundation for integrating control systems and MIMO-compatible models in future aquaculture automation frameworks.

Oyster aquaculture in the U.S. faces severe inefficiencies due to the absence of precise path planning tools, resulting in environmental degradation and resource waste. Current dredging techniques lack trajectory planning, often leading to redundant seabed disturbance and suboptimal shell distribution. Existing vessel models—such as the Nomoto or Dubins models—are not designed to map steering inputs directly to spatial coordinates, presenting a research gap in maneuver planning for underactuated boats. This research fills that gap by introducing a novel hybrid vessel kinetics model that integrates the Nomoto model with Dubins motion primitives. Our approach links steering inputs directly to the vessel motion, enabling Cartesian coordinate path generation without relying on intermediate variables like yaw velocity. Field trials in the Chesapeake Bay demonstrate consistent trajectory following performance across varied path complexities, with average offsets of 0.01 m, 1.35 m, and 0.42 m. This work represents a scalable, efficient step toward real-time, constraint-aware automation in oyster harvesting, with broader implications for sustainable aquaculture operations.