As global manufacturing transitions toward digitalization, networking, and intelligentization, robotic arms have become critical execution units within smart manufacturing systems.
Their structural design and control capabilities have emerged as pivotal breakthroughs for industrial upgrading.
Traditional robotic arms primarily serve fixed processes and repetitive tasks, offering high stability yet demonstrating limitations in flexible production, personalized manufacturing, and complex process handling.
Against this backdrop, next-generation robotic arm designs for intelligent manufacturing urgently require breakthroughs across three dimensions:
First, structural lightweighting and modularization to reduce energy consumption and enhance motion flexibility.
Second, combined optimization of drive and reduction systems to simultaneously meet heavy-load and high-precision operational demands.
Third, intelligent and real-time control systems integrating advanced algorithms with high-speed communication architectures to achieve precise task scheduling for complex operations.
Industry has explored advancements in material applications, dynamic modeling, and intelligent control.
However, the challenge remains in systematically integrating these optimization approaches.
This integration is necessary to deliver holistic solutions tailored to real-world operational conditions.
Structural Composition of Multi-Degree-of-Freedom Robotic Arms
Multi-degree-of-freedom robotic arms are typically composed of a base, joints, linkages, drive units, transmission mechanisms, and end effectors.
These components are integrated through serial or parallel connections to form a complete spatial kinematic chain.
The base provides fixed support.
Joints and linkages determine the robotic arm’s degrees of freedom and operational range.
Drive units and transmission mechanisms deliver power and precise motion output.
End effectors perform tasks such as gripping, welding, or inspection according to process requirements.
The overall structure must balance rigidity and stability with lightweight and modular design to enhance motion efficiency and application flexibility.
The primary structural components and parameters of the six-degree-of-freedom robotic arm designed in this paper are shown in Table 1.
| Module | Function Description | Example Parameters | Materials / Configuration |
|---|---|---|---|
| Base | Provides overall support and stability | Weight approx. 15 kg; base diameter 300 mm | Aluminum alloy frame + steel fixed base plate |
| Joint 1 (Rotation) | Controls horizontal rotation of the arm | Rotation angle ±170°; rated torque 120 Nm | Servo motor + harmonic reducer |
| Joint 2 (Pitch) | Controls upward and downward movement of the upper arm | Rotation angle 0° to +135°; rated torque 90 Nm | Servo motor + RV reducer |
| Joint 3 (Elbow) | Controls forearm extension and pitching | Rotation angle ±135°; torque 70 Nm | Harmonic reducer + lightweight linkage |
| Joint 4 (Wrist 1) | Controls wrist pitching movement | Rotation angle ±180°; torque 30 Nm | Servo motor + precision bearings |
| Joint 5 (Wrist 2) | Controls wrist side-to-side swing | Rotation angle ±180°; torque 25 Nm | Harmonic drive unit |
| Joint 6 (End) | Controls rotation of the end tool | Rotation angle ±360°; torque 20 Nm | High-speed servo + encoder |
| End Effector | Process tool interface (gripping / welding) | Load capacity ≤ 10 kg; quick-change interface design | Modular gripper / tool changing device |
In a typical six-degree-of-freedom robotic arm, the first three joints primarily handle spatial positioning, while the last three joints ensure end-effector posture adjustment, enabling manipulation in any desired configuration.
Linkage structures connecting joints require a design balance between stiffness and weight reduction.
To minimize inertial loads, modern robotic arms predominantly utilize lightweight, high-strength materials like aluminum alloys and carbon fiber.
Topology optimization through finite element analysis further enhances overall dynamic performance.
Drive systems typically employ servo motors paired with harmonic reducers or RV reducers to meet high-torque output and low backlash requirements.
For high-speed small joints, encoders and precision bearings are incorporated to ensure angular resolution and repeatability.
Subsequently, the end-effector serves as the critical interface for task execution.
Its modular design enables rapid switching between different process tasks, accommodating components like grippers, welding torches, spray applicators, or vision sensors.
To enhance system adaptability, end-effector interfaces are predominantly designed with quick-change standards, allowing robotic arms to fulfill multi-process, multi-task operational demands within smart manufacturing environments.
Structural Improvements for Multi-Degree-of-Freedom Robotic Arms in Smart Manufacturing
Lightweight Materials and Topology Optimization Design
In smart manufacturing environments, robotic arms often require frequent high-speed operations and prolonged continuous operation.
Their structural weight directly impacts energy consumption and control performance.
Given this context, structural enhancements can incorporate lightweight materials and employ topological optimization techniques.
For linkages and joint housings, carbon fiber composites can replace traditional aluminum alloys, reducing material density from 2.7 g/cm³ to 1.6 g/cm³.
While introducing lightweight materials, weight can be reduced by approximately 35% while ensuring structural rigidity meets requirements.
Furthermore, utilizing finite element analysis and topology optimization techniques, a skeletonized design is implemented for the stress-bearing regions of the linkages, retaining only high-stress load-bearing areas.
This approach reduces redundant material while maintaining overall structural strength.
Practical application results demonstrate that under a 10kg rated load, the lightweight robotic arm design reduces joint motor power requirements by 12% while increasing acceleration by approximately 18%.
This optimizes dynamic performance and effectively lowers energy consumption during long-term operation.
Optimization of Joint Drive and Reduction Systems
The motion precision and power output performance of robotic arms are closely tied to the capabilities of their joint drive and reduction systems.
Traditional harmonic reducers often exhibit backlash issues and reduced transmission efficiency under high-torque conditions.
To address this, an improved solution combines RV reducers with harmonic reducers:
RV reducers are deployed at high-load joints, such as the upper and lower arms.
They achieve rated transmission efficiencies exceeding 85% and rated lifespans surpassing 20,000 hours.
Meanwhile, the wrist and end-effector joints continue to utilize precision harmonic reducers. This ensures high accuracy in small-angle movements.
For the drive motor, permanent magnet synchronous servo motors can be introduced, combined with absolute encoders featuring 20-bit resolution, to achieve ±0.02° angular control precision.
Experimental validation demonstrates that under dynamic load conditions, the optimized solution reduces positional error to ±0.25 mm.
Compared to conventional designs, this achieves approximately 40% higher motion precision and 15% shorter joint response times, meeting the demands of high-speed assembly and precision operations in smart manufacturing.
The optimization results for the joint drive and reduction system are summarized in Table 2.
| Item | Traditional Solution (Harmonic Reducer) | Optimized Solution (RV + Harmonic Combination) | Improvement Range |
|---|---|---|---|
| Upper arm / forearm reducer type | Harmonic reducer | RV reducer | — |
| Wrist / end-effector reducer type | Harmonic reducer | Precision harmonic reducer | 10% |
| Rated transmission efficiency (%) | 75–78 | ≥ 85 | 80% |
| Rated service life (hours) | 10,000–12,000 | ≥ 20,000 | — |
| Motor type | Standard servo motor | Permanent-magnet synchronous servo motor | — |
| Encoder resolution | 16 bit | 20 bit | 25% |
| Angular control accuracy (°) | ±0.05 | ±0.02 | 60% |
| Dynamic load positioning error (mm) | ±0.45 | ±0.25 | −44% |
| Response time reduction (%) | — | Reduced by 15% | — |
Modular Joints and End-Effector Quick-Change System
To meet the demands of multi-tasking and flexible production in smart manufacturing, robotic arm structures can be upgraded to modular joints and end-effector quick-change systems.
In joint structure design, each joint unit employs unified interfaces and electrical bus standards such as EtherCAT, enabling rapid replacement and maintenance of joint units.
This reduces average maintenance time from 3 hours to under 1 hour.
The end-effector design incorporates a quick-change system utilizing pneumatic or electromagnetic clamping mechanisms, enabling tool replacement in 10 seconds or less.
This facilitates flexible switching between diverse tasks such as material handling, welding, and spraying.
Additionally, to ensure the reliability of the quick-change interface, the design incorporates position self-calibration and torque detection modules.
This enables closed-loop adjustment of the end-effector gripping force within the range of 0 to 200N, guaranteeing stability for different process tasks.
Actual measurements on production lines show that the modular quick-change system increases the overall utilization rate of robotic arms by more than 20%, significantly enhancing their adaptability and scalability within flexible manufacturing cells.
Design of Motion Control Systems for Multi-Degree-of-Freedom Robotic Arms in Smart Manufacturing
System Requirements Analysis and Control Objective Definition
Before commencing motion control system design, it is essential to conduct a comprehensive and thorough analysis of operational requirements within smart manufacturing scenarios.
Robotic arms must execute multiple tasks—including material handling, assembly, welding, and inspection—within complex production processes.
Control objectives encompass positional and postural accuracy while also addressing real-time performance, stability, and interference resistance.
Specifically, the system must achieve repeatability accuracy ≤ ±0.05mm, maintain trajectory tracking error within ±0.3mm, and ensure response latency below 1ms.
Additionally, to meet flexible production demands in smart manufacturing, the control system requires task switching capabilities and compatibility with multiple processes.
For instance, during material handling tasks, it must operate at high speeds with stability, achieving end-effector speeds up to 1.5m/s.
Conversely, in precision assembly tasks, it must ensure end-effector angular control accuracy better than ±0.02°.
During the requirements analysis phase, modeling of environmental disturbances—such as load variations, vibrations, and temperature rises—is also necessary to ensure the robustness of the control algorithms.
System Architecture Design and Hardware Selection
Once the overall requirements are clearly defined, a layered control architecture must be established and an appropriate hardware platform selected.
This paper presents a design based on a master-slave distributed control architecture:
the host computer handles task scheduling and trajectory planning, while the slave units utilize embedded controllers combined with FPGAs as the core to achieve real-time servo control.
The communication layer employs EtherCAT bus technology, ensuring joint control cycles remain under 1ms and feedback latency for each joint is less than 500μs.
The drive system employs permanent magnet synchronous servo motors paired with RV or harmonic reducers, delivering output torque ranging from 20Nm to 120Nm.
Each joint incorporates a 20-bit resolution absolute encoder, while the end-effector integrates a six-axis force/torque sensor to enable real-time force control feedback.
Control Algorithm Design and Implementation
Control algorithms occupy a central position in motion control systems, directly determining the motion accuracy and dynamic performance of robotic arms.
This paper presents a hybrid control strategy combining enhanced PID with model predictive control (MPC):
For low-speed, heavy-load joints, enhanced PID control is employed.
By adapting proportional and integral parameters, it compensates for errors caused by friction and load variations, reducing steady-state error to 0.05 mm.
For high-speed joints and complex trajectory tasks, the MPC method predicts future states over several sampling cycles based on the robotic arm’s dynamic model, then optimizes control inputs in real time to achieve smooth trajectory tracking.
The MPC control cycle is set to 1 ms, with a 12-dimensional optimization space and a solution time of approximately 0.5 ms, ensuring real-time performance.
System Testing and Performance Validation
Following the implementation of control algorithms and system integration, experimental validation is required to assess performance and stability.
This paper conducted tests on a six-degree-of-freedom robotic arm using a flexible production line experimental platform, covering tasks such as material handling, welding, and precision assembly.
Experimental results demonstrate that under a 10kg rated load, the robotic arm achieves repeatability accuracy of ±0.04mm and maintains stable trajectory tracking error within ±0.25mm—exceeding design specifications.
After 8 hours of continuous high-intensity operation, joint temperature rise remained below 45°C while drive system efficiency stayed above 87%, validating hardware selection reliability.
Furthermore, when subjected to an external disturbance force of ±10N, the system regained stable trajectory within 0.8 seconds, demonstrating robust disturbance rejection capability of the control algorithm.
Performance comparison tests demonstrate that this system outperforms traditional single PID solutions.
It achieves a 15% improvement in response speed.It reduces energy consumption by 12%.
Additionally, task switching time is reduced to 60% of the original duration.
These test results validate the system’s feasibility under complex operating conditions and provide robust data support for its promotion and application in smart manufacturing scenarios.
Conclusion
In summary, the demands of intelligent manufacturing are driving a critical transition in multi-degree-of-freedom robotic arms.
Their structural and motion control systems are moving away from traditional designs.
They are evolving toward intelligent, modular, and high-precision solutions.
Through lightweight materials and topology optimization, the robotic arm achieves a balance between energy consumption and dynamic performance.
In the drive and reduction stages, hybrid configurations combining RV and harmonic drives are introduced.High-resolution encoders are also incorporated.
These measures not only ensure stability under high-torque conditions but also significantly enhance precision during small-angle movements.
In control system design, a distributed hardware architecture is used in combination with hybrid control algorithms.
This approach balances real-time performance and robustness.
It effectively addresses challenges in complex operating conditions and multi-task flexible manufacturing.
Overall, this research not only enhances the adaptability and scalability of robotic arms at the structural level but also expands their precision and intelligent application boundaries at the control level.
With the further integration of artificial intelligence and intelligent sensing, robotic arms will demonstrate greater autonomy and coordination.
They will operate more effectively within future intelligent manufacturing systems.
As a result, they will become key equipment supporting the upgrade of production models.