AutoDrone: Shortest Optimized Obstacle-Free Path Planning for Autonomous Drones

TL;DR

AutoDrone introduces an efficient method for autonomous drones to find the shortest obstacle-free path in 2D space, with extensions to longer routes and 3D, aiding in rescue and delivery missions.

Contribution

The paper presents a novel grid-based graph approach and heuristics for shortest obstacle-free path planning for autonomous drones in 2D and extended to 3D.

Findings

Effective shortest path computation in various map scenarios

Energy-efficient routes for rescue and delivery tasks

Potential extension to 3D space for complex environments

Abstract

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.