Cell A* for Navigation of Unmanned Aerial Vehicles in Partially-known Environments

TL;DR

This paper introduces an enhanced A* algorithm for UAV navigation in partially known environments, enabling real-time, collision-free path planning with reduced computational load, validated on drones and remote control cars.

Contribution

It extends the A* algorithm to improve stability and efficiency for online navigation in complex, partially known environments, suitable for autonomous UAVs.

Findings

Algorithm achieves stable, collision-free paths in real-time

Reduced computational burden compared to standard A*

Validated on drone and remote control car platforms

Abstract

Proper path planning is the first step of robust and efficient autonomous navigation for mobile robots. Meanwhile, it is still challenging for robots to work in a complex environment without complete prior information. This paper presents an extension to the A* search algorithm and its variants to make the path planning stable with less computational burden while handling long-distance tasks. The implemented algorithm is capable of online searching for a collision-free and smooth path when heading to the defined goal position. This paper deploys the algorithm on the autonomous drone platform and implements it on a remote control car for algorithm efficiency validation.