With technological advancement, drone has emerged as unmanned aerial vehicle that can be controlled by humans to fly or reach a destination. This may be autonomous as well, where the drone itself is intelligent enough to find a shortest obstacle-free path to reach the destination from a designated source. Be it a planned smart city or even a wreckage site affected by natural calamity, we may imagine the buildings, any surface-erected structure or other blockage as obstacles for the drone to fly in a straight line-of-sight path. To address such path-planning of drones, the bird's eye-view of the whole landscape is first transformed to a graph of grid-cells, where some are occupied to indicate the obstacles and some are free to indicate the free path. We propose a method to find out the shortest obstacle-free path in the coordinate system guided by GPS. The autonomous drone (AutoDrone) will thus be able to move from one place to another along the shortest path, without colliding into hindrances, while traveling in a two-dimensional space. Heuristics to extend this to long journeys and 3D space are also elaborated. Our approach can be especially beneficial in rescue operations and fast delivery or pick-up in an energy-efficient way, where our algorithm will help in finding out the shortest path and angle along which it should fly. Experiments are done on different scenarios of map layouts and obstacle positions to understand the shortest feasible route, computed by the autonomous drone.
翻译:随着技术进步,无人驾驶飞机已成为无人驾驶飞行器,可以由人类控制,飞或抵达目的地;这也可能是自主的,无人驾驶飞机本身足够智能,足以找到从指定来源到达目的地的最短无障碍通道;无论是计划好的智能城市,还是受自然灾害影响的残骸地点,我们可以想象这些建筑物、任何地表结构或其他阻力是无人驾驶飞机在直线视线道路上飞行的障碍;为了解决无人驾驶飞机的这种路径规划,鸟对整个地貌的眼界的视线首先变成一个网格细胞图,其中有人被用来指出障碍,有些人可以自由指出自由通道;我们建议一种方法,在全球定位系统指导下的协调系统中找到最短无障碍的道路;因此,自主无人驾驶飞机(AutoDrone)将能够沿着最短的路径向另一条路移动,而不会陷入障碍,同时在两维空间旅行。超时将这种视距延伸到长的航程和3D空间图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图图