Dissertation - Using Swarm AI to map a cave network

Abstract

This project aims to find an efficient solution to cave exploration and mapping by simulating multiple autonomous flying drones, all initially unaware of their environment in a visualised and custom generated cave environment. The project tackles various problems in the field of robotics such as: sensing, frontier searching, teamwork, route planning and target selection. Drones will build an occupancy grid of its environment then proceed to map the cave by repeatedly identifying and searching an optimal frontier cell which are boundaries between unoccupied areas and unknown areas. Incorporating multiple drones into the simulation allows for communication of information to propel search efficiency and thus reduce search duplication achieved by mimicking swarm behaviour. Such behaviour includes exploring frontiers away from nearby drones, moving away from nearby drones, and branching out at junctions. Realistic caves for the simulation are generated using a random but reproducible process combining Simplex Noise, Cellular Automata and Flood fill. The project aims to identify how various factors of the drone’s behaviour such as how many drones exist or their search radius may affect the performance of the search.

Running

Build from source

./build

Run the program

./main