In modern warehouse logistics, the integration of robot technology has revolutionized operational efficiency by automating tasks such as sorting, transportation, and management. However, the optimization of multi-scenario task scheduling for handling robots remains a significant challenge due to issues like unbalanced task allocation, traffic congestion, and task conflicts. Conventional methods often rely on cloud computing and path decoding but fail to account for operational angle errors, leading to reduced efficiency. In this paper, we propose a comprehensive approach to optimize the scheduling of handling robots in multi-scenario tasks, focusing on calculating angle errors, constructing an optimization model, and employing the A* algorithm for resolution. Our method aims to minimize handling time, maximize equipment load balance, and ensure high workpiece quality, thereby enhancing overall system performance. Through simulation experiments, we demonstrate the effectiveness of our approach in improving robot operational efficiency. The widespread adoption of robot technology in warehouses underscores the importance of advanced scheduling strategies to meet dynamic demands.
The foundation of our method lies in accurately calculating the operational angle of handling robots, as angle errors can impede bidirectional movement and compromise task completion in multi-scenario environments. We utilize a momentum scale-invariant matching technique to align real-time robot operation images with predefined cargo handling images. By sorting the stop positions based on angle magnitude, we derive the relative angle deviation. The robot’s kinematic model is described using nonlinear functions, enabling precise computation of the end-effector’s motion direction and subsequent angle errors. This process ensures that the robot can adjust its orientation dynamically during operations, reducing the risk of failures. The integration of robot technology in this phase highlights its role in enhancing positional accuracy and stability. Key formulas involved in this calculation are as follows:
First, the robot’s kinematic model is represented as: $$S_j = \sum_{A} \left\| T_1 \cdot T \cdot F(q) \cdot f_t \right\|^2$$ where \(T\) and \(T_1\) denote the projection times of the experimental camera and internal robot equipment, respectively, \(F(q)\) is the position vector of the end-effector, and \(f_t\) is a nonlinear function. This equation captures the dynamic behavior of the robot during task execution.
Next, the motion direction vector of the end-effector is given by: $$Q_s = \sum_{S_j} \frac{f_s()}{g_c / n_1}$$ where \(f_s()\) is the matching function, \(g_c\) is the momentum factor, and \(n_1\) is the number of assigned tasks. This step ensures that the robot’s movement aligns with task requirements.
The angle error, which reflects the horizontal deviation during operation, is computed as: $$e_w = \sum_{i=1}^{n_1} \frac{Q_s (L_1 – L_2)}{x_t}$$ where \(L_1\) and \(L_2\) are the angle deviations of the robot and cargo relative to the horizontal direction, and \(x_t\) is the operational path. This error metric is crucial for identifying misalignments.
Subsequently, the rotation angle during operation is derived as: $$w_s = e_w \times b_v \times \left[ \frac{H}{V} \right]$$ where \(b_v\) is the angle error offset between the robot and cargo, and \(H\) and \(V\) are coefficient and adjacency matrices, respectively. This formula accounts for rotational adjustments.
Finally, the operational angle is determined by: $$\theta_z = w_s \times \frac{r_t}{d_f} / h_0$$ where \(r_t\) is the Euler angle of the manipulator’s rotation, \(d_f\) is the rotation matrix, and \(h_0\) is the initial position of the robot. This comprehensive calculation enables precise control over the robot’s orientation, a key aspect of advanced robot technology.
To illustrate the parameters involved in these calculations, we present a table summarizing key variables used in the angle error computation:
| Parameter | Description | Typical Value |
|---|---|---|
| \(T\) | Camera projection time | 0.1 s |
| \(F(q)\) | End-effector position vector | [1.0, 2.0, 3.0] |
| \(L_1\) | Robot angle deviation | 5° |
| \(L_2\) | Cargo angle deviation | 3° |
| \(b_v\) | Angle error offset | 0.02 |
Building on the angle calculation, we construct an optimization scheduling model for multi-scenario tasks. The model aims to minimize handling time, maximize equipment load balance, and achieve the highest workpiece quality, which refers to the integrity and lack of damage during transportation. We assume fixed task execution times, simultaneous multi-task capability, equal task priorities, and constant velocity movement without collisions. The objective function is defined as: $$F = \min z_1 + \max z_2 + \max z_3$$ where \(z_1\), \(z_2\), and \(z_3\) represent handling time, load balance rate, and workpiece quality, respectively. These components are calculated as follows: $$z_1 = k_i \times \theta_z \times \frac{h_b}{v_m}$$ $$z_2 = \sum_{j=1}^{r} \frac{M_t}{S_d} / r_u$$ $$z_3 = R_s \times \sum \sum \frac{v_h}{X_c}$$ Here, \(k_i\) is the current task, \(\theta_z\) is the operational angle, \(h_b\) is the set of robots, \(v_m\) is the set of order tasks, \(r\) is the action set, \(M_t\) is the robot state, \(S_d\) is the state space, \(r_u\) is the transition probability matrix, \(R_s\) is the performance matrix, \(v_h\) is the Landé factor, and \(X_c\) is the gain matrix. This model leverages robot technology to balance multiple objectives effectively.
To ensure practical applicability, we impose several constraints on the model. The cargo handling constraint specifies that each task must be processed at a unique picking station: $$\sum_{u \in E} d_{hu} = 1$$ where \(E\) is the set of picking stations and \(d_{hu}\) indicates that task \(h\) is processed at station \(u\). The picking station service constraint prevents multiple shelves from being serviced simultaneously: $$y_{m’n’u} = 0, \quad n \in C, u \in E$$ where \(y_{m’n’u}\) denotes station \(u\) serving shelves \(m\) and \(n\) sequentially, and \(C\) is the set of shelves. The robot starting point constraint ensures that operations begin in the warehouse shipping area with a single path: $$Y_s = \tau_0 \times \frac{A_b}{N_s}$$ where \(\tau_0\) is the average picking time, \(A_b\) is the travel time to shelf \(b\), and \(N_s\) is the number of picking stations for order \(s\). The power constraint mandates sufficient battery life: $$P_t \leq \phi_t \sum \vartheta’ / s_l$$ where \(\phi_t\) is the service time at station \(t\), \(\vartheta’\) is the start time, and \(s_l\) is the movement start time. These constraints are integral to robust robot technology implementations.
The final optimized scheduling model incorporates weight factors for each objective: $$F = \psi (\beta_1 z_1 + \beta_2 z_2 + \beta_3 z_3)$$ where \(\beta_1\), \(\beta_2\), and \(\beta_3\) are weights assigned to handling time, load balance, and workpiece quality, respectively, and \(\psi\) represents the combined constraints. This formulation allows for flexible adjustment based on specific warehouse requirements, showcasing the adaptability of modern robot technology.
To solve the optimization model, we employ the A* algorithm, which efficiently navigates the search space by evaluating path costs. The algorithm involves initializing the robot’s pose and operational angle, publishing target points along the path, and computing path costs using Manhattan distance. The cost for each target point is given by: $$\varpi = \varepsilon_1 + \varepsilon_2$$ where \(\varepsilon_1\) is the shortest distance from the start to the current point, and \(\varepsilon_2\) is the shortest distance to the end point. The minimum handling time is then: $$Z_4 = \frac{\min \varpi l_z}{v}$$ where \(l_z\) is the shortest path length and \(v\) is the velocity. The algorithm iteratively selects the point with the lowest cost, updates the navigation list, and outputs the optimal path. The best scheduling strategy is derived as: $$F(z) = \min(z_1 + z_4) + \max z_2 + \max z_3$$ This approach highlights the synergy between algorithmic efficiency and robot technology in achieving optimal task scheduling.
In our simulation experiments, we evaluated the proposed method in a large warehouse logistics center covering 50,000 square meters. The center offers services like storage, sorting, and distribution, utilizing intelligent handling robots for multi-scenario tasks. The robots were configured with technical parameters such as a maximum load capacity of 40 kg, battery life of 12 hours, and a maximum speed of 2.5 m/s. We built a discrete-event simulation platform comprising agents (e.g., shelves, cargo), simulators for updating states, controllers for decision-making, and visualization components for interaction. This setup allowed us to model dynamic events and optimize robot paths effectively, demonstrating the practical application of robot technology in real-world environments.
The simulation results showed significant improvements after applying our optimization method. For instance, the path length for robots was reduced across various task points, as summarized in the following table:
| Number of Task Points | Path Length Before Optimization (m) | Path Length After Optimization (m) |
|---|---|---|
| 10 | 39.56 | 25.36 |
| 20 | 40.12 | 32.10 |
| 30 | 35.33 | 28.45 |
| 40 | 40.25 | 33.02 |
| 50 | 36.78 | 29.16 |
| 60 | 46.20 | 42.17 |
This reduction in path length directly translates to shorter handling times and higher efficiency, underscoring the benefits of our robot technology-driven approach. Additionally, we compared our method with alternative approaches, such as an improved gray wolf algorithm and a reinforcement learning algorithm, in terms of order completion time and rate. Our method consistently achieved lower completion times and higher completion rates, as illustrated in the following table:
| Number of Orders | Completion Time with Our Method (s) | Completion Time with Method 1 (s) | Completion Time with Method 2 (s) |
|---|---|---|---|
| 100 | 120 | 150 | 180 |
| 200 | 240 | 290 | 320 |
| 300 | 360 | 420 | 480 |
Furthermore, the order completion rates demonstrated the superiority of our method, with rates exceeding 95% across various order volumes, compared to 90-94% for Method 1 and 85-89% for Method 2. The convergence speed of our model was also faster, stabilizing after approximately 100 training steps, whereas the alternatives required 400-600 steps. These results validate the effectiveness of our optimization strategy in enhancing robot technology applications for warehouse logistics.
In conclusion, our research presents a robust framework for optimizing multi-scenario task scheduling in warehouse logistics handling robots. By integrating angle error calculations, multi-objective modeling, and the A* algorithm, we address key limitations of conventional methods and achieve significant efficiency gains. The experimental outcomes confirm that our approach reduces path lengths, shortens completion times, and improves task completion rates, all of which are critical for advancing robot technology in dynamic environments. Future work could extend this method to other domains, such as manufacturing or healthcare, to further leverage the potential of intelligent robot systems. The continuous evolution of robot technology will undoubtedly drive innovations in logistics and beyond, making our contributions a stepping stone for broader applications.