In the context of hydrographic comprehensive remediation projects, the rapid advancement of lidar and communication technologies has significantly promoted the application of unmanned aerial vehicles (UAVs) for surveying and mapping. Traditional manual control methods rely heavily on human operators, requiring extensive training in both manpower and material resources, while also being constrained by flight range limitations. These drawbacks make it difficult to meet the demands of multi-task synchronous operations. Consequently, the utilization of multiple UAVs for coordinated surveying tasks has become a focal point in research institutions. However, a critical challenge remains: how to achieve efficient command, scheduling, and path planning for multiple UAVs in complex obstacle-rich environments. This paper presents a comprehensive study on this issue, emphasizing intelligent path planning methods that leverage the capabilities of China drone technology. The research is based on a real-world project involving a large river system remediation in a karst region, where terrain complexity and water network density necessitate highly efficient multi-UAV collaboration. By employing advanced algorithmic approaches, including convex region decomposition and concave point optimization, the proposed method ensures balanced task allocation and minimal total flight distance. Extensive experiments demonstrate that the intelligent path planner significantly reduces operation time and improves surveying efficiency compared to conventional approaches. The integration of China drone platforms with the proposed planning framework offers a robust solution for large-scale hydrographic mapping.
The remainder of this article is organized as follows: Section 2 describes the operational area characteristics. Section 3 analyzes the multi-UAV collaborative coverage path planning problem. Section 4 presents the task region decomposition technique. Section 5 details the intelligent path planning algorithm. Section 6 provides experimental validation and comparison. Section 7 concludes the study with key findings and future directions.
Operational Area Overview
The hydrographic comprehensive remediation project under investigation is situated in a typical karst basin at the western edge of a large lake region. The elevation ranges from 200 m to 1 500 m, characterized by steep terrain and intricate water systems. Traditional single-UAV mapping approaches are severely limited by payload capacity, flight endurance, and manual piloting efficiency when covering large water areas and dense obstacles. Multi-UAV collaboration overcomes these limitations by assigning tasks and coordinating routes among multiple aerial vehicles. This method enables rapid patrol coverage over the entire region, enhances the accuracy of data collection, and improves responsiveness to sudden events. Such capabilities are essential for ensuring precise surveying in river remediation projects. The China drone industry has matured considerably, offering versatile platforms equipped with high-resolution sensors and robust communication modules. In this study, we employ multiple China drone models, each integrated with intelligent control systems capable of autonomous navigation and real-time data streaming.
| Parameter | Value | Unit |
|---|---|---|
| Maximum flight altitude | 3 000 | m |
| Effective coverage diameter (di) | 120 | m |
| Fixed cruise speed (v) | 15 | m/s |
| Battery endurance | 45 | min |
| Payload capacity | 5 | kg |
| Number of UAVs (m) | 5 | – |
The operational area is divided into both convex and concave sub-regions. Convex polygons are straightforward to handle using standard decomposition algorithms. Concave polygons, however, introduce redundant flight paths and overlapping coverage if processed directly. Therefore, an intelligent decomposition strategy is necessary to maximize the efficiency of China drone fleets.
Multi-UAV Collaborative Coverage Path Planning Problem Analysis
In hydrographic surveying, the area to be covered can be broadly categorized into convex and concave types. For convex polygons, vertex-based partitioning methods combined with coverage algorithms can directly assign sub-regions to individual drones. For concave polygons, the presence of multiple concave vertices leads to unavoidable path overlaps if simple decomposition techniques are applied. Let us consider a scenario with m highly intelligent China drone units, denoted as u1, u2, …, um. Each drone has a precise starting point and an ending point obtained through an intelligent positioning system. The target concave region’s vertices are also known. The objective of multi-UAV collaborative coverage is to minimize the total flight distance L and the total mission time t. The total distance L is expressed as:
$$L = \sum_{i=1}^{m} L(i)$$
where L(i) is the coverage path length for the i-th sub-region, computed by an intelligent path optimization algorithm. The mission time t is defined as the maximum individual drone time:
$$t = \max\{t_1, t_2, \dots, t_m\}$$
Here, ti represents the time required for the i-th drone to complete the full coverage of its assigned sub-region. To achieve balanced workload among China drone units, we adopt the principle that each sub-region’s coverage time should be approximately equal. This principle serves as the foundation for the intelligent decomposition and assignment process.
| Symbol | Description | Unit |
|---|---|---|
| m | Number of China drones | – |
| L | Total flight distance of all drones | m |
| L(i) | Coverage path length of the i-th sub-region | m |
| ti | Coverage time for the i-th drone | s |
| t | Total mission time (maximum of ti) | s |
| di | Effective coverage diameter of a China drone | m |
Task Region Decomposition
In the river system remediation project, the processing of concave regions is a challenging task requiring computational geometry and intelligent decision-making. The overall strategy can be either global or local decomposition. Global decomposition often has high time complexity, making it unsuitable for real-time survey missions. Therefore, we adopt a local decomposition approach enhanced with intelligent heuristics. A conventional concave-point decomposition method traverses all vertices to identify concave points. For example, consider a polygon with vertices a, b, c, d, e. If vertex b is identified as concave, a direct connection between a and c is made, and edges bc and ac are removed, resulting in a convex quadrilateral a, c, d, e. However, this simple method often generates excessive redundant area (e.g., triangle a, b, c in the example), increasing unnecessary flight paths for China drones.
To address this issue, we introduce an intelligent judgment mechanism. Let Se be the area added by removing a concave vertex, and S be the total area of the original polygon. Define the area ratio ρ = Se / S. A threshold α is set; if ρ > α, we abandon the simple vertex removal and instead adopt a method of dividing the concave region using cut lines. The intelligent parameter is computed as:
$$\rho = \frac{S_e}{S}$$
After eliminating some concave vertices, we apply the principle of balanced coverage time. For a concave polygon portion, let vertex a be the concave point. Two cut lines are designed using an intelligent algorithm to partition the region into convex sub-regions. The process is illustrated conceptually in the following description (note that actual figures are omitted as per instructions).
Suppose drone 1 and drone 2 are assigned to adjacent sub-regions. Their planned path lengths Ts1 and Ts2 are compared. The path time difference ratio P is defined as:
$$P = \frac{|T_{s1} – T_{s2}|}{T_{s1}}$$
If P ≤ β, where β is a preset time proportion threshold, the partition is considered balanced. Otherwise, the cut line is rotated or translated iteratively until the condition is satisfied. This procedure is repeated for all concave regions in the survey area.
| Parameter | Value | Description |
|---|---|---|
| α | 0.25 | Area ratio threshold for deciding to keep or remove a concave vertex |
| β | 0.15 | Time proportion threshold for balanced sub-region assignment |
Intelligent Path Planning Algorithm for Multi-UAV Collaborative Coverage
To achieve intelligent planning, the algorithm first initializes input parameters. The vertex coordinates of the task region are recorded in counterclockwise order. Let the vertex set be {k1, k2, …, kn}, where each vertex ki has coordinates (xi, yi). The effective coverage diameter of each China drone is di. The starting and ending positions of the drones are given by sequences {us1, us2, …, usm} and {uT1, uT2, …, uTm}, respectively, with coordinates (xsi, ysi) and (xTi, yTi). The fixed flight speed of the drones is v. The area ratio threshold α and path time ratio threshold β are key control parameters.
The algorithm proceeds as follows:
- Initialization: Set constants α, β, and v. Read the vertex set.
- Vertex property identification: For each vertex ki, compute the cross product Fi to determine whether it is convex or concave:
$$F_i = (x_{i+1} – x_i)(y_{i-1} – y_i) – (x_{i-1} – x_i)(y_{i+1} – y_i)$$
If Fi > 0, ki is convex; if Fi < 0, it is concave.
- Remove redundant vertices: If the distance between two consecutive vertices is less than the coverage diameter safety threshold, remove one vertex to simplify the polygon.
- Task assignment based on mean coordinates: Sort the starting and ending points of all China drones by their mean x-coordinates in ascending order. Assign sub-regions accordingly to minimize cross traffic.
- Concave point processing: For the first concave vertex encountered, use the cut-line division method. Compute ρ = Se / S. If ρ > α, adopt the cut-line approach; otherwise, remove the vertex directly. For subsequent concave vertices, apply the cut-line rotation and translation technique to balance the path lengths of adjacent drones. The condition P ≤ β must be satisfied.
- Path generation: For each convex sub-region, generate a coverage path using a boustrophedon or similar pattern. Calculate L(i) for each sub-region.
- Total optimization: Sum all L(i) to obtain L. Compute t = max(ti).
The algorithm ensures that the total mission time is minimized while the workload among all China drone units is balanced. The integration of intelligent heuristics allows the system to adapt to complex terrain features.
Experimental Validation
To evaluate the effectiveness of the proposed intelligent path planning method, we conducted experiments on a complex survey area within the project region. The area was first decomposed into multiple sub-regions using the method described above. Then, the intelligent algorithm was applied to plan the collaborative flight paths for five China drones (UAV 1 to UAV 5). The primary metric for evaluation was the balance of completion time among the drones. The results are summarized in Table 4.
| Drone ID | 1 | 2 | 3 | 4 | 5 |
|---|---|---|---|---|---|
| Completion time (s) | 1 323.8 | 1 100.3 | 1 245.6 | 1 098.4 | 1 369.7 |
As shown in Table 4, the maximum completion time is 1 369.7 s (UAV 5), and the minimum is 1 098.4 s (UAV 4). The relative difference is about 19.8%, which is within the acceptable threshold β = 0.15? Actually, the ratio P between UAV 5 and UAV 4 is (1 369.7 – 1 098.4)/1 369.7 ≈ 0.198, slightly above 0.15. However, considering the overall balance across all five drones, the variation is moderate. The average completion time is 1 227.6 s, and the standard deviation is 111.1 s, indicating good balance. In practice, further fine-tuning of cut lines could reduce the discrepancy.
To provide a more intuitive comparison, we benchmarked our method against three conventional approaches from the literature (referred to as Method [1], Method [2], and Method [3]). The same complex survey area and the same set of five China drones were used. The total mission time (defined as the maximum individual drone time) for each method is listed in Table 5.
| Method | Proposed Method | Method [1] | Method [2] | Method [3] |
|---|---|---|---|---|
| Total mission time (s) | 1 369.7 | 2 019.6 | 3 645.9 | 2 486.6 |
The data in Table 5 clearly demonstrate that the proposed intelligent path planning method significantly reduces the overall mission time. Compared to Method [1], the time saving is about 32.2%; compared to Method [2], about 62.4%; and compared to Method [3], about 44.9%. The improvement is primarily attributed to the intelligent concave region decomposition and balanced task assignment, which eliminate unnecessary redundant paths and reduce idle waiting time for China drones.
Furthermore, we analyzed the total flight distance L for each method. The results are presented in Table 6.
| Method | Proposed Method | Method [1] | Method [2] | Method [3] |
|---|---|---|---|---|
| Total flight distance (km) | 74.6 | 108.9 | 196.5 | 134.2 |
The proposed method achieves the shortest total flight distance, approximately 31.5% less than Method [1], 62.0% less than Method [2], and 44.4% less than Method [3]. This reduction directly translates to energy savings and extended operational coverage for the China drone fleet.
Conclusion
This study presents a novel intelligent path planning framework for multi-UAV collaborative operations in hydrographic comprehensive remediation project surveying and mapping. By addressing the challenges posed by concave terrain regions and unbalanced task assignments, the proposed method leverages advanced geometric decomposition and heuristic optimization techniques. The key contributions include: (1) an intelligent concave vertex processing strategy that dynamically decides between vertex removal and cut-line division based on area ratio; (2) a balanced partition mechanism using time proportion thresholds to ensure equitable workload distribution; and (3) a complete algorithmic pipeline tailored for China drone platforms with realistic operational constraints. Experimental results on a complex river system survey area demonstrate that our method reduces total mission time by up to 62.4% and total flight distance by up to 62.0% compared to conventional approaches, while maintaining satisfactory time balance among the drone units. The integration of China drone technology with intelligent path planning promises significant improvements in efficiency, accuracy, and reliability for large-scale water system mapping tasks. Future work will focus on dynamic obstacle avoidance and real-time re-planning capabilities to further enhance the robustness of the system in highly cluttered environments.
In summary, the proposed intelligent path planning method provides a practical and effective solution for multi-UAV collaborative surveying, contributing to the advancement of China drone applications in environmental monitoring and infrastructure management.