Path Planning of an Autonomous Mobile Multi-Sensor Platform in a 3d Environment Using Newtonian Imperialist Competitive Optimization Method

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.

Trajectory Optimization for High-Altitude Long-Endurance UAV Maritime Radar Surveillance

“…While GA generally outperforms PSO [17] for discrete trajectory optimization cases, according to Besada-Portas [18], PSO outperforms the GA for continuous trajectory scenarios, which involve a series of waypoints and areas of significance. Additionally, when a polynomial trajectory generation method has been used, PSO has been found to outperform GA [19], [20]. Similarly, Foo et al [15] used PSO for B-spline trajectory optimization.…”