• Corpus ID: 240354735

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

  title={AutoDrone: Shortest Optimized Obstacle-Free Path Planning for Autonomous Drones},
  author={Prithwish Jana and Debasish Jana},
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… 

Figures from this paper


Automatic and Semantically-Aware 3D UAV Flight Planning for Image-Based 3D Reconstruction
This work proposes a 3D UAV path planning framework designed for detailed and complete small-scaled 3D reconstructions considering the semantic properties of the environment allowing for user-specified restrictions on the airspace.
A literature review of UAV 3D path planning
This paper analyses the most successful UAV 3D path planning algorithms that developed in recent years and classifies them into five categories, sampling-based algorithms, node-based algorithm, mathematical model based algorithms, Bio-inspired algorithms, and multi-fusion based algorithms.
Real-Time Path Planning for Multi-copters flying in UTM -TCL4
An on-line path planning scheme is proposed which can effectively plan for feasible paths in real time with TCL-4 constraints and avoids other obstacles and other UAVs flying in the shared airspace.
Path Planning and Moving Obstacle Avoidance with Neuromorphic Computing
This paper proposes an SNN algorithm for path planning with moving obstacles and considers two agents with SNN which tries to achieve two goals: “reaching its destination promptly” and “avoiding moving obstacles properly”.
Dijkstra's Shortest Path Algorithm
There is no rigorous justification with respect to the correctness of Dijkstra’s algorithm, so functions in the Mizar library, which seem to be pseudo-codes, and are similar to those in the functional programming language, e.g. Lisp are adopted.
DeepOSM-3D: recognition in aerial LiDAR RGBD imagery
This paper addresses the full semantic segmentation of aerial LiDAR and RGBD imagery by exploiting crowd-sourced labels that densely canvas each image in the 2015 Dublin dataset, indicating important improvements to detection and segmentation accuracy.
Drone & me: an exploration into natural human-drone interaction
It is discovered that people interact with drones as with a person or a pet, using interpersonal gestures, such as beckoning the drone closer, when gesturing for the drone to stop.
Fusion of aerial lidar and images for road segmentation with deep CNN
A cost-effective, modular, deep convolution network design, TriSeg is described which gives better IoU metric for the aerial road segmentation problem than the state of the art RGB only architectures.
Bellman Ford algorithm - in Routing Information Protocol (RIP)
Routing Information Protocol (RIP) is one of dynamic routing that uses the bellman-ford algorithm where this algorithm will search for the best path that traversed the network by leveraging the value of each link, so with the bellmansford algorithm owned by RIP can optimize existing networks.
Aerial image semantic segmentation using DCNN predicted distance maps