1. Introduction
In recent years, UAV technology has garnered significant attention from scholars and researchers. Its applications span across various domains, including the military, civilian, and commercial sectors, demonstrating increasing versatility [
1]. UAVs are not only cost-effective, agile, and flexible in deployment, but they also offer the advantage of conducting missions without risking human lives and are reusable. Equipped with cameras and sensors mounted atop, they can effortlessly capture, record, and measure images and data in remote or complex terrains, hence finding extensive applications in aerial photography such as sports events, commercial activities, and inspections [
2,
3]. Inspection tasks enable the timely detection of hazards and defects in various areas and equipment, playing a crucial role in ensuring human safety. For instance, many aging rural buildings exhibit cracks, tilts, tile detachment, or excessive deformation, posing threats to the residents. Therefore, utilizing UAVs for inspection tasks emerges as a promising and vital technology.
Most inspection tasks for UAVs are focused on transmission towers [
4] and ports [
5], and there is little research on the application of UAVs in building inspection. The building inspection mission of a UAV primarily involves two key aspects: building image acquisition and mission planning. Zhang Z et al. have proposed a co-optimal coverage path planning (CCPP) method that jointly optimizes the image quality and the path of the UAV to realize the image detection of complex structures [
6]. Zhang et al. proposed a method for automatically detecting structural damage and exterior wall cracks in building structures using UAV remote sensing information extraction, successfully extracting post-earthquake damage information [
7]. Ruiz et al. employed a field research experimental approach, emphasizing the feasibility and effectiveness of utilizing UAV technology for inspections [
8]. Additionally, mission planning comprises task allocation and path planning. Typically, inspection mission planning employs a collaborative approach involving UGV and multiple UAVs. While UGV boasts high payload capacity and endurance, it faces challenges in rough terrain. Similarly, UAVs are constrained by factors such as limited payload capacity and flight duration, and a single UAV may even have defects such as a high risk of failure and long task time. In the face of a wide range of inspections and complex tasks, reliability is reduced, and it is often unable to ensure the smooth completion of the task. Hence, the collaborative technique of UGVs and UAVs offers distinct advantages. Radmanesh et al. successfully planned rational paths for medical or emergency supply delivery using UAV-UGV cooperation in disaster scenarios [
9], while Wu et al. effectively employed UAV-UGV synergy for continuous urban surveillance [
10].
The MPP of UGV-UAVs is a challenging and pivotal problem. MPP is a general term for task assignment issues and path planning problems in UAVs [
11]. By assigning one or more ordered tasks to a group of UAVs, redundancy and conflicts between UAVs are avoided, optimizing overall task efficiency. Typically, the optimization objective aims to minimize the flight cost of multiple UAVs, such as by minimizing flight path length or overall cost. To obtain rational task allocation solutions, it is imperative to establish mathematical models for collaborative UAV planning and employ algorithms for solving. Common algorithms of MPP include market-based algorithms [
12], clustering algorithms [
13], and metaheuristic algorithms [
14]. Currently, research on the algorithms of MPP primarily focuses on metaheuristic algorithms due to their ability to strike a balance between solution quality and computational time, producing satisfactory solutions within acceptable time frames. These algorithms have low computational complexity, fast execution, and robustness, making them more adaptable to complex task and resource variations compared to market-based and clustering algorithms.
Metaheuristic algorithms draw inspiration from natural phenomena or biological behaviors [
15]. Typical algorithms include Genetic Algorithm (GA) [
16], Ant Colony Optimization (ACO) [
17], Simulated Annealing (SA) [
18], Particle Swarm Optimization (PSO) [
19], and Tabu Search (TS) [
20]. Local search algorithms such as TS and SA search for a single solution and prevent local optima using strategies like maintaining a Tabu list or probabilistic acceptance of inferior solutions. These algorithms are simple and easy to operate, but they are often inefficient for solving complex problems. GA, inspired by biological evolution processes of chromosome selection, crossover, and mutation, exhibits good optimization capabilities and population diversity. Zhang et al. improved GA convergence by incorporating a gravity search mechanism into the update process, solving collaborative reconnaissance task planning problems for multiple UAVs [
21]. Zhu et al. addressed the task pre-allocation problem for multiple UAVs with different types of targets using a genetic algorithm with dual-chromosome encoding, achieving satisfactory results [
22]. PSO, originating from the study of bird flock foraging, demonstrates good global search capabilities, fast convergence speed, and robustness [
23]. Gou et al. proposed an inertia weight adaptive strategy to overcome PSO’s susceptibility to local optima [
24], though efficiency concerns remained unaddressed. ACO simulates the optimization of foraging routes by ants, exhibiting good convergence speed and optimal pathfinding. Ebadinezhad introduced a dynamic evaporation strategy to adapt ACO, improving its convergence speed and alleviating the tendency to fall into local optima [
25]. While much research has focused on improving traditional algorithms and enhancing optimization outcomes, inherent flaws in singular algorithms persist. Consequently, scholars have attempted to integrate two algorithms to complement each other’s strengths and weaknesses. Shang et al. proposed a genetic-ant colony algorithm for reconnaissance task planning [
26]; meanwhile, Jia et al. combined genetic concepts to propose an improved particle swarm optimization algorithm for solving multi-UAV task planning problems [
27]. And Jiang et al. proposed an ant colony-single parent genetic algorithm to solve large-scale multiple traveling salesman problems, improving algorithm convergence speed, yet optimization outcomes warrant further improvement [
28].
From the aforementioned studies, several issues in current research emerge: firstly, most studies of MPP focus solely on multiple UAVs without considering UGV and neglecting the issues caused by the low endurance of the UAVs. Secondly, in optimization processes, most research prioritizes the shortest paths or flight times, overlooking UAV operation processes or treating them homogeneously. However, the complexity of tasks at various points in real environments varies, leading to differences in operation times. Lastly, many studies reveal flaws in algorithms during the solving process, indicating room for improvement in overall optimization. Thus, addressing these issues, this paper calculates parking point positions before task planning, fully considering the positions of various task points and their impact on operation times. Subsequently, an ACO-GA is proposed, integrating the search process of ACO into GA’s selection operation to enhance the initial solution quality. Furthermore, a series of improvements are made to the crossover and mutation operations to increase search capabilities and prevent falling into local optima. During optimization, considerations are given to the impact of UAV battery levels and operation time, aiming to solve the multi-UAV task problem effectively.
2. Establishment of Mathematical Model
During the inspection task in a specific area, UGV will park at a designated parking point, and the locations of task points are determined. Each task point is assigned to only one UAV, as illustrated in
Figure 1 for the collaborative inspection of UAVs. Each rotary-wing UAV only needs to fly to a few task points for operation, with UAVs jointly completing all tasks in the area. Due to the scattered distribution of task points and the limitations of UAV positioning accuracy and safe return requirements, UGV needs to remain stationary and wait for UAVs to complete inspection tasks before departure. UAVs are influenced by their own battery levels; when the battery is low, they need to return to parking points to change batteries before continuing tasks. Simultaneously, considering the synchronicity of multiple UAVs completing tasks, the inspection task at the parking point is only considered to have been completed when the last UAV completes its task and lands on the UGV.
To facilitate the quantitative study of this MPP, the following assumptions are made for the MPP model of UAVs from a single parking point:
Each UAV on the UGV possesses uniform characteristics, with identical cruising speeds during flight and equal battery capacities.
UAVs take off with full battery capacity, and when battery levels are low, they return to the UGV for battery replacement, disregarding the time for takeoff, landing, ascent, descent, and battery replacement.
Constrained by the distribution of task points and the precision requirements of UAV positioning, the UGV needs to remain stationary at the parking point until all UAVs complete their inspection tasks.
During the flight between the start and the destination, the time variations caused by altitude changes have been incorporated into the operational time of each task.
These assumptions and settings allow for the establishment of a mathematical model for task planning. For a region with n task points and m UAVs taking off from parking point Q0 to execute inspection tasks at task points in accordance with their respective planned sequences, where the inspection time at task point Qi is denoted as ti, from the above parameters, the optimization model for this task planning can be formulated. The objective is to minimize the total time spent by all UAVs to complete their respective inspection tasks. Constraints are on UAV battery capacity and the requirement that each task point be inspected by only one UAV, and all task points must be inspected. The decision variables represent the sequence of UAV flights to each task point. They can be expressed using the following formulas.
Objective Function:
where
xk,i,j represents the decision variable. When
xk,i,j = 1, it means that the
kth UAV flies from the task point
i to the task point
j; otherwise, it does not fly from the task point
i to the task point
j;
tk, i represents the time spent by the
kth UAV inspecting the task point
i;
tmax is the maximum flight time that the UAV can operate under full charge.
tuse is the time that has been flown,
nk is the number of task points assigned to the
kth UAV,
mk is the total number of flight sections required by the
kth UAV during the execution of the task,
F is the value of the objective function, indicating the maximum time consumed by each UAV to complete the task,
Fk is the time consumed by the
kth UAV to complete the task.
Si,j represents the distance from mission point
i to mission point
j, and
v represents the flying speed of the drone. Equation (2) indicates the constraint that the UAV needs to return to the parking point after completing the task or at low power. The reference battery level is set at 15% based on the fact that the power consumption for a single task point will not exceed 10%. This threshold ensures both the maximization of task efficiency and the safe return of the UAV. Equations (3) and (4) ensure that only one UAV is required to perform the inspection task for each task point
i or
j. Equation (5) ensures that all task points have been inspected. Equation (6) shows how the value of the objective function is calculated.
5. Conclusions
This paper addresses MPP for building inspection by establishing a task planning model for coordinated inspection using UGV and UAVs. Firstly, the MPP problem is described, which involves dispatching UGV to designated location and utilizing multiple UAVs to inspect all buildings within a certain area. Secondly, a mathematical optimization model for the MPP is established, aiming to minimize the mission time. The constraints include ensuring coverage of all buildings and maintaining sufficient battery power for the UAVs. Additionally, the model takes into full account the impact of varying operational time requirements for different mission points. Subsequently, the location of parking spots is determined based on the positions of the mission points and the operational time required. Finally, to address the issues of weak search capabilities in ACO and the tendency of GA to get trapped in local optima, the paper proposes an ACO-GA hybrid approach.
The simulation results and comparisons with other algorithms demonstrate the superiority and effectiveness of the proposed ACO-GA, effectively addressing the task planning problem for building inspections. While this study focuses only on the task planning problem with a single parking point, the process of using UAVs for building inspections at individual task points has not been thoroughly studied, and real-world applications often involve inspection over large areas, requiring task planning with multiple parking points. In addition, the research findings of this paper primarily remain in the stages of algorithm design and simulation verification. Its applicability in the real world remains to be validated. Therefore, future research will focus on how UAVs can conduct inspections within individual buildings. And it is necessary to continue to explore solutions to task planning issues in larger areas and utilize actual data for verification and experimental validation of the effectiveness of the methods presented in this paper.