Path Planning for Unmanned Aerial Vehicle Using a Mix-Strategy-Based Gravitational Search Algorithm

Abstract

Path planning is a global optimization problem aims to program the optimal flight path for Unmanned Aerial Vehicle (UAV) that has short length and suffers from low threat. In this paper, we present a Mixed-Strategy based Gravitational Search Algorithm (MSGSA) for the path planning. In MSGSA, an adaptive adjustment strategy for the gravitational constant attenuation factor alpha (α) is presented firstly, in which the value of α is adjusted based on the evolutionary state of the particles. This helps to adaptively balance the exploration and exploitation of the algorithm. In addition, to further alleviate the premature convergence problem, a Cauchy mutation strategy is developed for MSGSA. In this strategy, only when the global best particle cannot be further improved for several times the mutation is executed. In the MSGSA based path planning procedure, we construct an objective function using the flight length cost, threat area cost, and turning angle constraint to decrease the flight risk and obtain the smoother path. For performance evaluation, the MSGSA is applied to two typical simulated flight missions with complex flight environments, including user-defined forbidden flying areas, Radar, missile, artillery and anti-aircraft gun. The obtained flight paths are compared with that of the standard Gravitational Search Algorithm (GSA) and two improved variants of GSA, i.e. gbest-guided GSA (GGSA), and hybrid Particle Swarm Optimization and GSA (PSOGSA). The experimental results demonstrate the superiority of the MSGSA based method in terms of the solution quality, robustness, as well as the constraint-handling ability.

Design and Analysis of a Novel Concept-Based Unmanned Aerial Vehicle with Ground Traversing Capability

“…Route planning with optimum path selection is also an important problem in UAV motion. Many researchers have also addressed this problem, and some related works [12,13,14,15,16] are cited here. Route planning for a quadcopter or UAV is to search the optimal flight path from the source point to a destination to reduce the time and energy [12].…”

“…Many researchers have also addressed this problem, and some related works [12,13,14,15,16] are cited here. Route planning for a quadcopter or UAV is to search the optimal flight path from the source point to a destination to reduce the time and energy [12]. Therefore, Xu et al [12] implemented a gravitational search algorithm to address this issue.…”

“…Route planning for a quadcopter or UAV is to search the optimal flight path from the source point to a destination to reduce the time and energy [12]. Therefore, Xu et al [12] implemented a gravitational search algorithm to address this issue. The MATLAB graphical user interface module was used to show the different 2D and 3D flight motion results.…”

“…8, the UGV's frame (6) of the ground traversing device is attached to the lower portion of a UAV's frame (11). This UGV's frame ( 6) is attached to UAV's frame (11) by bolt (12) and nut (13). The UGV's frame (6) of the ground traversing device is attached to the aerial driving assembly, at its one side that is opposite to another side of the UGV's frame (6), at which the wheel and DC motors (8) are attached for the ground traversing mechanism.…”

IRS-aided network scheme supports unmanned aerial vehicle communication

“…[1], [2]. Therefore, the requirement of achieving highspeed and stable air-to-ground wireless links is anticipated to play an important role in future UAV communication systems [3] and the UAV control and path planning also become the crucial problems [4], [5]. Moreover, there are so many challenges which the UAV communication have to be encountered, especially in urban areas where the blockages caused by common objects such as the buildings and trees are the prominent challenges [6] which can lead to the issues of poor coverage and connectivity.…”