ABSTRACT
Autonomous mission capabilities with optimal path are stringent requirements for Unmanned Aerial Vehicle (UAV) navigation in diverse applications. The proposed research framework is to identify an energy-efficient optimal path to achieve the designated missions for the navigation of UAVs in various constrained and denser obstacle prone regions. Hence, the present work is aimed to develop an optimal energy-efficient path planning algorithm through combining well known modified ant colony optimization algorithm (MACO) and a variant of A*, namely the memory-efficient A* algorithm (MEA*) for avoiding the obstacles in three dimensional (3D) environment and arrive at an optimal path with minimal energy consumption. The novelty of the proposed method relies on integrating the above two efficient algorithms to optimize the UAV path planning task. The basic design of this study is, that by utilizing an improved version of the pheromone strategy in MACO, the local trap and premature convergence are minimized, and also an optimal path is found by means of reward and penalty mechanism. The sole notion of integrating the MEA* algorithm arises from the fact that it is essential to overcome the stringent memory requirement of conventional A* algorithm and to resolve the issue of tracking only the edges of the grids. Combining the competencies of MACO and MEA*, a hybrid algorithm is proposed to avoid obstacles and find an efficient path. Simulation studies are performed by varying the number of obstacles in a 3D domain. The real-time flight trials are conducted experimentally using a UAV by implementing the attained optimal path. A comparison of the total energy consumption of UAV with theoretical analysis is accomplished. The significant finding of this study is that, the MACO-MEA* algorithm achieved 21% less energy consumption and 55% shorter execution time than the MACO-A*. moreover, the path traversed in both simulation and experimental methods is 99% coherent with each other. it confirms that the developed hybrid MACO-MEA* energy-efficient algorithm is a viable solution for UAV navigation in 3D obstacles prone regions.
ABSTRACT
Graphene is a promising electrode material for supercapacitor applications due to its unique properties. Interaction of electrolyte ions with graphene lattice sites is a crucial factor in ionic liquid electrolyte based supercapacitors. In an effort to increase the interaction of high viscous electrolyte with electrode material, here, we here report the results of a systematic study carried out on a supercapacitor with nitrogen doped graphene as electrode material and [BMIM][TFSI] as electrolyte. In this study, nitrogen doped hydrogen exfoliated graphene (N-HEG) is prepared by radio frequency (R.F) magnetron sputtering and employed as electrode material for [BMIM][TFSI] electrolyte based high performance supercapacitor. N-HEG shows a high specific capacitance of 170.1 F/g compared to that of electrolyte modified graphene (124.5 F/g), at a specific current of 2 A/g. The improved performance of N-HEG based supercapacitor is attributed to the presence of nitrogen atoms in the graphene lattice which in turn increases the lattice-ion interaction and the electrical conductivity. In addition, the presence of wrinkles on the graphene surface provides a shortest directional path to access pores and surface. The device shows high charge storage capacity (72.37 Wh/kg) along with wide operating voltage (3.5 V) and high cyclic stability.
