**3. Proposed Control Approach for Path Planning and Tracking**

The control strategy of this study consists of path planning and tracking. The hybrid HHO–GWO algorithm, which has high convergence speed and swarm intelligence that can avoid local minimum points, is proposed in this study in determining the optimum path. The path planning performances of the proposed optimization algorithm are compared with metaheuristic optimization algorithms such as PSO and GWO. The payload hold– release path determined by these optimization algorithms is generated with the shortest distance and avoiding the areas where there are obstacles. By analyzing the multi-objective function with metaheuristic optimization algorithms, waypoints to be followed by the UAV are generated. As seen in Figure 2, after the waypoints that the quadcopter are to follow are generated, the following of these waypoints, namely, the path tracking, is carried out with controller in a nested structure. The main idea of the study is that a new control strategy is proposed to carry out path planning and tracking together for the quadcopter's payload

hold–release mission. The section includes not only controller design of quadcopter but also metaheuristic algorithms such as PSO, GWO, and HHO.

**Figure 2.** The proposed control strategy of the quadcopter.
