Planning the Optimal 3D Quadcopter Trajectory Using a Delivery System-Based Hybrid Algorithm

Abstract

Path planning is one of the most crucial aspects of implementing unmanned aerial vehicle (UAV) missions. Therefore, it is essential to figure out the optimal path from the starting point to the target point in different scenarios. In this paper, we propose employing a hybrid algorithm, named FPA-GA, generated by combining the flower pollination algorithm (FPA) and genetic algorithm (GA), to find an optimum path in a real modern building's environment. In addition, a cubic polynomial algorithm via two points was proposed to make the route adequate and smooth. Because the GA has a good capability for exploring the search space, it is employed for exploration while the FPA and GA are employed to increase the exploitation capability in the proposed algorithm. Five different scenarios are utilized to evaluate FPA-GA's ability to find the optimal path in a variety of situations, and then the proposed algorithm's performance is compared with that of seven other algorithms: GA, FPA, bat algorithm (BA), particle swarm optimization (PSO), whale optimization algorithm (WOA), improved whale optimization algorithm (IWOA), and IWOA-PSO. The best path, the mean path length, the standard derivation, and the worst path length are the four parameters used to compress data. In all scenarios, the results demonstrate that FPA-GA is capable of locating the shortest and most collision-free paths, and the hybrid algorithm FPA-GA is always superior to other algorithms. The proposed algorithm provided the mean path length enhancement in all scenarios, where the maximum enhancement equals (33.6%), (23.1%), (55.5%), (29.8%), (50.5%), (53.9%), and (26.4%) compared with GA, FPA, BA, PSO, WOA, IWOA, and IWOA-PSO, respectively. On the other hand, the proposed algorithm is guaranteed to find the best path in all scenarios, where the standard deviation of FPA-GA is always less than that of other algorithms.

Optimal Trajectory Tracking: A Comparative Study of Autonomous Quadcopter Controllers

“…Natural pollination processes are divided into biotic and abiotic mechanisms, whereas artificial pollination is the intentional use of suitable pollen with the help of human intervention. The computational framework in this research models a process similar to mechanical artificial pollination, resembling palm pollination techniques [4], [34].…”

“…This combination of the FPA and GA tries to balance exploration and exploitation to reduce the possibility of local optima [4], as shown in Figure 3.…”

“…Quadcopters are among the most commonly used UAVs due to their flexibility in performing various duties, including search and rescue operations, military missions, and agricultural procedures. Furthermore, the quadcopters' ability to take off and land vertically, hover in place, exhibit exceptional mobility, and demonstrate agility renders them adaptable for various duties [1], [2], [3], [4]. The quadcopter has gained significant popularity as a subject of study in control systems.…”

Unmanned aerial vehicle path planning in a 3D environment using a hybrid algorithm

“…The environment is downloaded as a DXF file, which autoCAD can access. It is then modified and transformed into an STL file for MATLAB use [6], [21].…”

“…The fifth category is multi-fusion-based algorithms, which combine two or more algorithms; these algorithms seek to solve and enhance route planning problems by increasing their quality, stability, and convergence performance [19]. Examples include a probabilistic roadmap based on ant colony optimization [20], improved particle swarm optimization (IPSO), grey wolf optimizer (GWO) [19], a hybrid flower pollination and genetic algorithm (FPA-GA) [21], (BAS-GA) is a combination of the genetic algorithm and the beetle antennae search algorithm [22], and others. Depending on the situation, each of these algorithms has advantages and disadvantages.…”