Application of six-degree-of-freedom (DOF) robotic arms in industrial automation and intelligent manufacturing is rapidly expanding, especially in offshore operations, where high path planning accuracy and energy efficiency are critical. The complexity and uncertainty of offshore environments pose significant challenges to traditional path planning methods, making it difficult to achieve an effective balance between path precision and energy consumption. To address these challenges, this study proposes a path optimization method based on multi-objective optimization techniques, aimed at simultaneously improving path planning accuracy and energy efficiency for six-DOF robotic arms operating in offshore environments. Specifically, the proposed method seeks to minimize terminal error while reducing energy consumption, which is particularly important in applications where energy costs and precision are paramount.

First, a positive kinematic model of the robotic arm is developed using the Denavit–Hartenberg (D–H) parameter method, providing a foundation for minimizing terminal errors during path planning. To determine the optimal joint angle trajectories, a particle swarm optimization (PSO) algorithm is employed. The PSO algorithm is well-suited for solving nonlinear optimization problems, as it mimics the social behavior of birds flocking to efficiently search for global optimal solutions. Subsequently, the relation between energy consumption and terminal error is analyzed, focusing on gravitational potential energy and joint rotational motion, which are the primary contributors to energy usage during robotic arm movements. Based on this analysis, a multi-objective optimization model is developed, incorporating 0–1 binary decision variables to represent the selection of joint configurations. To make the problem tractable, the multi-objective model is converted into a single-objective optimization problem using an ε-constraint approach. This strategy simplifies the optimization process while maintaining an appropriate balance between path accuracy and energy efficiency. A genetic algorithm (GA), a powerful global search technique, is utilized to solve the resulting single-objective optimization problem, enabling efficient exploration of the solution space. For environments with obstacles, an obstacle avoidance path planning strategy based on the breadth-first search (BFS) algorithm is incorporated to ensure collision-free motion of the robotic arm along the optimized path.

Simulation results demonstrate the effectiveness of the proposed multi-objective optimization method. Compared with traditional path planning approaches, the proposed method achieves a significant reduction in terminal error and energy consumption. Path planning accuracy is notably improved, and energy efficiency is enhanced, which is crucial for offshore operations where resources are often limited. The proposed method outperforms conventional methods in terms of path optimization and robustness, particularly in environments with obstacles and uncertainties. Furthermore, the method shows considerable improvements in energy efficiency without compromising the accuracy of the path.

By integrating advanced optimization and search techniques, such as PSO, GA, and BFS, this paper successfully addresses the challenges of path planning in offshore robotic applications. The proposed method enhances path-planning accuracy and significantly reduces energy consumption, providing an efficient solution for complex industrial automation tasks. These results highlight the great potential of the proposed approach for enhancing the performance of robotic arms across various applications, especially in offshore operations where energy efficiency and precision are of utmost importance.