Abstract
Abstract. This paper addresses an innovative evolutionary computation approach to 3D path planning of autonomous UAVs in real environment. To solve this Np-hard problem, Newtonian imperialist competitive algorithm (NICA) was developed and extended for path planning problem. This paper is related to optimal trajectory-designing before UAV missions. NICA planner provides 3D optimal paths for UAV planning in real topography of north Tehran environment. To simulate UAV path planning, a real DTM is used to algorithm. For real-world applications, final generated paths should be smooth and also physical flyable that made the path planning problems complex and more constrained. The planner progressively presents a smooth 3D path from first position to mission target location. The objective function contains distinctive measures of the problem. Our main goal is minimization of the total mission time. For evaluating of NICA efficiency, it is compared with other three well-known methods, i.e. ICA, GA, and PSO. Then path planning of UAV will done. Finally simulations proved the high capabilities of proposed methodology.
Highlights
UAVs can be used in geomatics applications, such as photogrammetry, monitoring and search-and-rescue tasks (Schøler et al, 2012)
The assimilation operation moves each colony in a group toward the best solution in the same group (Gargari, 2007) This algorithm starts with random initial solutions
In the Newtonian imperialist competitive algorithm (NICA), all the countries attract to others based on their powers, by the gravitational force, and this force causes a global movement of all nations towards the countries with more power
Summary
UAVs can be used in geomatics applications, such as photogrammetry, monitoring and search-and-rescue tasks (Schøler et al, 2012). The use of UAVs, which can fly autonomously in 3D environments, is becoming a solution for kind of problems. Civilian applications of UAVs, such as search and rescue and aerial surveillance require precise maneuvers and optimal navigation and efficient path planning algorithms. Autonomous UAV would be conscious of other UAVs flying in environment surrounded by obstructions Researchers have resolved this problem by path planning approaches. The novel Imperialist Competitive Algorithm (ICA), which has been recently designed (Gargari, 2007), has shown improved performances in many optimization problems. The remarkable point is that assimilation operation of ICA in high-dimensional constrained problems often converges to a local optimum. This is the result of non-convexity of feasible solution space. The peer-review was conducted on the basis of the abstract
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
