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The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

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Energy-efficient path planning for Robotic Bulkhead Inspection using Residual-Enhanced UKF and Hierarchical MPC.

Jiexin Wang¹, Lei Li¹, Runlin Gao¹

¹College of Mechanical Engineering, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu, China.

|March 19, 2026

Summary

This study introduces an autonomous robotic arm control framework for challenging ship inspections. The novel system enhances safety and efficiency through advanced navigation and energy-saving strategies.

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Area of Science:

Robotics and Automation
Marine Engineering
Control Systems

Background:

Ship bulkhead inspection is difficult due to confined spaces and unpredictable environments.
Existing robotic systems struggle with real-time adaptation and energy efficiency in these conditions.

Purpose of the Study:

To develop a robust autonomous robotic arm control framework for ultrasonic thickness inspection of ship bulkheads.
To enhance navigation, obstacle avoidance, and energy efficiency in dynamic marine environments.

Main Methods:

Implemented a residual-driven adaptive noise covariance Unscented Kalman Filter (RD-ANC UKF) with a Huber penalty function (HP-UKF) for robust sensor data processing.
Designed a Three-Layer Energy-Efficient Model Predictive Control (TLE-MPC) strategy integrating Differential Dynamic Programming (DDP), Sequential Quadratic Programming (SQP), and Explicit MPC (eMPC).

Main Results:

The framework demonstrated real-time obstacle avoidance and stable path tracking in simulations.
Achieved up to 15% energy reduction during inspection tasks in semi-structured and unpredictable ship environments.
The HP-UKF improved robustness against sensor noise and outliers.

Conclusions:

The proposed framework offers a robust, scalable, and energy-efficient solution for autonomous robotic inspection in challenging marine settings.
This research provides a foundation for advanced multi-arm cooperation and extended energy-aware robotic deployments.