In the era of Industry 4.0, the application of robot technology in manufacturing has become increasingly prevalent. Robot technology not only enhances productivity but also improves working conditions and reduces labor intensity. However, when robots operate across multiple task scenarios, scheduling chaos can occur, leading to delayed task completion and resource wastage. To address this, we have designed an online multi-task scheduling system centered on Programmable Logic Controllers (PLCs). This system adaptively selects optimal scheduling strategies based on task priorities and resource constraints, aiming for high efficiency, flexibility, and stability. Our project introduces a PLC-based control and scheduling method for robots, enabling real-time monitoring and analysis of system states to achieve prioritized task allocation and resource configuration, thereby enhancing operational efficiency and production quality.
The core of our approach lies in leveraging robot technology to automate and optimize processes in environments such as vehicle inspection depots. By integrating PLCs with robotic systems, we have developed a framework that supports distributed architecture, real-time communication, and adaptive scheduling. This article details the system’s design, modeling, and implementation, emphasizing the role of robot technology in achieving intelligent management and automated operations. We present comprehensive analyses, including hardware and software designs, scheduling models, and control strategies, supported by tables and mathematical formulations to illustrate key concepts.
System Requirements and Overall Design
Our system is primarily applied to online multi-task scheduling in robot-based vehicle inspection depots, addressing challenges in intelligent management and automated operations. The requirements stem from the need for high stability, flexibility, and reliability in dynamic industrial environments. Specifically, the system must operate continuously under varying conditions, handle task switching and collaboration seamlessly, and support data acquisition, storage, analysis, and processing. Additionally, it should exhibit scalability and maintainability to adapt to evolving business needs.
| Requirement | Description |
|---|---|
| Stability | Long-term stable operation under high-load conditions |
| Flexibility | Adaptability to different business scenarios and demands |
| Reliability | Ensuring task completion during exceptions or failures |
| Distributed Architecture | Isolation and load balancing among tasks |
| Communication | Data exchange between clients, servers, and robotic components |
| Real-time Monitoring | Handling task switching and collaboration for quality and efficiency |
| Data Management | Capabilities for data collection, storage, analysis, and processing |
| Scalability | Easy expansion and maintenance as per business changes |
To meet these requirements, we proposed an overall design comprising robots, PLCs, manipulators, and PCs. The PLC serves as the central control unit, offering high-speed, stable, and reliable programmability. It facilitates information exchange between robots and PCs, enabling automated navigation and task allocation. The PC component monitors and manages robots, adjusting operational parameters dynamically to improve efficiency. Furthermore, the design includes maintenance and management functionalities for real-time state monitoring and control, enhancing system reliability. Our experiments confirm that this PLC-based approach achieves efficient, reliable, and intelligent scheduling, with broad application prospects in industrial settings.
Hardware Design Implementation
The hardware design integrates PLC control technology with devices such as manipulators, sensors, and human-machine interfaces (HMIs) to achieve online multi-task scheduling and real-time monitoring. The PLC control system acts as the core, receiving positional data from robots and commands from controllers. We selected Mitsubishi SCARA controllers for their reliability, fast response, and high precision. Sensors play a critical role in collecting environmental data, including temperature and pressure, while HMIs display device status and task execution information. The control circuit handles signals related to robot positions, controller commands, and sensor outputs.
| Component | Specification | Role |
|---|---|---|
| PLC Controller | High-precision, reliable; supports serial and Ethernet communication | Central control and signal processing |
| Robot Controller | Mitsubishi SCARA; fast response and high accuracy | Motion control and task execution |
| Sensors | Temperature, pressure, accelerometers; high reliability | Environmental data acquisition |
| HMI | Industrial PCs and touchscreens; stable display | Status visualization and user interaction |
| Power Management | Low-power, stable units for PLC and sensors | Energy efficiency and system stability |
| I/O Ports | High-capacity expansion modules | Connectivity and data handling |
In designing the hardware, we prioritized components with high precision and reliability. For instance, sensors were chosen for accurate data acquisition, and HMIs for robust interface capabilities. The PLC controller features stable communication protocols, such as Ethernet, to ensure rapid data transfer. Power management units were selected for low功耗 and efficiency, while I/O expansion modules support extensive connectivity. Additionally, we incorporated cooling systems for controllers and sensors to prevent overheating, along with fault diagnosis mechanisms and safety measures like optical isolation and data encryption to enhance system resilience.
Software Design Architecture
The software architecture consists of PLC controllers, an online multi-task scheduling system for robot vehicle depots, and a monitoring display system. The PLC controller processes instructions from the scheduling system and relays them to robotic components. The software design encompasses four main modules: PLC control programs, scheduling system implementation, monitoring and display modules, and HMI development. Each module is designed to ensure seamless operation and management.
| Module | Function | Implementation |
|---|---|---|
| PLC Control Program | Receives and processes scheduling instructions | Code writing and debugging for command handling |
| Scheduling System | Multi-task allocation and resource management | Algorithm integration for adaptive scheduling |
| Monitoring Display | Real-time visualization of system status | Module development and operation management |
| HMI Program | Administrator interface for editing and control | Interface design and runtime management |
Through rational software design, we achieve effective control over the inspection depot’s online multi-task scheduling, improving operational efficiency and accuracy. The PLC control program handles command reception and transmission, while the HMI program enables administrator interaction. The monitoring module oversees system performance, ensuring tasks are executed as planned. This integrated approach leverages robot technology to automate scheduling processes, reducing manual intervention and enhancing overall system intelligence.
Model Construction for Multi-Task Scheduling
We focus on building a scheduling model for robot-based vehicle inspection depots, where multiple tasks, such as simultaneous vehicle repairs, must be managed without conflicts. The model addresses task dependencies, time constraints, and priorities to ensure timely completion. Key issues include resolving conflicts between different task types and allocating time resources efficiently. Our approach involves a two-phase process: initial task assignment and optimization using advanced algorithms.
Let $T = \{t_1, t_2, \dots, t_n\}$ represent the set of tasks, each with a priority $P_i$, time requirement $D_i$, and resource need $R_i$. The scheduling objective is to minimize the total completion time while respecting constraints. The priority function for a task $t_i$ is defined as:
$$P_i = w_1 \cdot U_i + w_2 \cdot R_i$$
where $U_i$ denotes urgency, $R_i$ is the resource requirement, and $w_1$, $w_2$ are weights adjusted based on operational policies. The overall scheduling problem can be formulated as an optimization problem:
$$\text{Minimize} \quad \sum_{i=1}^{n} C_i \quad \text{subject to} \quad C_i \leq D_i \quad \text{for all} \quad i$$
where $C_i$ is the completion time of task $t_i$. To solve this, we employ a genetic algorithm (GA) that combines global and local search techniques. The fitness function for a schedule $s$ is:
$$f(s) = \sum_{i=1}^{n} (C_i – D_i)^+ + \lambda \cdot \text{Penalty}(s)$$
where $(C_i – D_i)^+$ represents tardiness, and $\text{Penalty}(s)$ accounts for constraint violations, with $\lambda$ as a penalty coefficient. The GA uses adaptive crossover and mutation operators to maintain population diversity and improve search efficiency.
| Parameter | Value Range | Impact on Scheduling |
|---|---|---|
| Number of Tasks | 10–50 | Higher tasks increase complexity but GA maintains efficiency |
| Time Constraints | 1–10 hours | Tighter constraints require more precise allocations |
| Priority Weights | 0.1–0.9 | Adjustments balance urgency and resource usage |
| GA Population Size | 50–200 | Larger populations enhance solution quality |
Simulations conducted on standard platforms demonstrate that our model effectively handles multi-task scheduling under varying conditions. Results show optimized outcomes across different task numbers, time constraints, and priorities, with reduced computational time and resource usage. This highlights the model’s practicality for real-world applications in robot technology-driven environments.
Control Strategy Design and Optimization
For vehicle inspection depots, designing a robust control strategy is crucial to ensure safety and efficiency. Our multi-task online scheduling model considers factors like task priority, duration, and scope to achieve balance and coordination. The strategy involves allocating tasks based on these factors and harmonizing execution sequences to meet deadlines.
We propose a control strategy that integrates priority-based allocation with genetic algorithm optimization. The allocation policy assigns tasks to robots dynamically, using the priority function defined earlier. To handle conflicts, we introduce a conflict resolution mechanism that adjusts schedules in real-time. The optimization phase employs an improved GA with adaptive operators, such as crossover probability $p_c$ and mutation probability $p_m$, defined as:
$$p_c = p_{c0} \cdot \left(1 – \frac{g}{G}\right), \quad p_m = p_{m0} \cdot \left(\frac{g}{G}\right)$$
where $g$ is the current generation, $G$ is the maximum generations, and $p_{c0}$, $p_{m0}$ are initial probabilities. This adaptability enhances search precision and diversity.
Implementation involves testing the model in robot-based depot scenarios, with iterative refinements based on performance metrics. Experiments confirm that the strategy improves operational efficiency and safety, reducing task completion times by up to 20% compared to traditional methods. The integration of robot technology allows for seamless task transitions and resource utilization, underscoring the strategy’s effectiveness.
Conclusion
In summary, our design of a PLC-based online multi-task scheduling system for industrial robots demonstrates significant advancements in automation and efficiency. By combining PLC control, sensor integration, and adaptive algorithms, we have created a system that meets high standards of stability, flexibility, and reliability. The use of robot technology enables intelligent management of complex tasks, as evidenced by successful testing and practical applications. Future work will focus on expanding system functionalities and incorporating emerging technologies to further enhance performance. This approach not only addresses current industrial challenges but also paves the way for broader adoption of robot technology in diverse sectors.