An Efficient Bio-inspired Technique for Secure Path Planning of Unmanned Aerial Vehicles in a 3D Environment

Unmanned aerial vehicles (UAVs) exemplify automation in avionics offering a versatile platform for carrying out a diverse range of applications such as security surveillance, logistics, and agriculture.Optimizing generation path planning is a crucial challenge in UAV operations as it requires rapidly identifying a feasible route within the complicated environment.Bio-inspired algorithms have advantages in generating secure path planning.However, it has convergence challenges when dealing with narrow passageways and complex environments.To improve drone flying safety, this paper proposed an enhanced chaos chicken swarm optimization algorithm (CCSO).The proposed algorithm improved the standard chicken swarm optimization algorithm to address the convergence and local optima problems during secure path planning in a complex 3D environment for UAVs.Firstly, enhance the initialization process using a three-dimensional hyperchaotic system to provide dynamic uniform distribution to the chicken positions.Secondly, provide a new strategy to determine the hierarchal order of population.Furthermore, all steps of CCSO are executed in parallel computation to speed up the algorithm performance.Lastly, compare the proposed algorithm with standard chicken swarm optimization (CSO), particle swarm optimization (PSO), hybrid artificial bee colony (HABC), improved grey wolf optimization (IGWO), chaotic PSO (CPSO) and an improved chaos sparrow search algorithm (ICSSA) in simple and complex environments.The experimental results demonstrated that the CCSO has a minimum length route, reduces the initialization time, accelerates convergence, speed up runtime and the path is smoother than the other algorithms.The proposed algorithm outperforms PSO, CSO, HABC, IGWO, CPSO and ICSSA algorithms in term of fitness values and times since it has ability to improve the fitness values in complex environment by about 24%, 9%, 22%, 9%, 3% and 5% and speed up the converges toward optimal routes 4.5x, 3.34x, 8.48x, 2.87x, 1.23x and 2.27x when compared to the PSO, CSO, HABC, IGWO, CPSO and ICSSA techniques respectively which proved the superiority of CCSO in generating optimal path planning for UAVs in complex 3D environment.