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A Novel Technique for Drone Path Planning Based on a Neighborhood Dragonfly Algorithm.

Sameer Agrawal¹, Bhumeshwar K Patle¹, Sudarshan Sanap¹

¹Department of Mechanical Engineering, MIT Art, Design & Technology University, Pune 412201, India.

Sensors (Basel, Switzerland)

|February 13, 2025

Summary

Neighborhood dragonfly algorithms (NDAs) enhance autonomous aerial drone navigation for indoor applications. This research validates NDAs for efficient path planning, showing over 5% path length savings compared to other methods.

Keywords:

AI technique aerial navigation dragonfly algorithm drone optimization path planning

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Area of Science:

Robotics and Control Systems
Artificial Intelligence
Computer Vision

Background:

Autonomous aerial drone navigation is crucial for indoor applications like surveillance and search and rescue.
Existing path planning methods face challenges with dynamic environments and optimal path discovery.

Purpose of the Study:

To implement and validate the Neighborhood Dragonfly Algorithm (NDA) for autonomous drone path planning in complex indoor environments.
To assess the collaborative behavior of dragonflies for effective solution space exploration and faster convergence in drone navigation.

Main Methods:

Development and real-time simulation of indoor environments with stationary and moving obstacles.
Implementation of the Neighborhood Dragonfly Algorithm (NDA) for single and multi-drone path planning.
Comparative analysis of NDA against existing algorithms such as IACO and PRM.

Main Results:

The NDA approach demonstrated robust performance, achieving path length and navigational time differences of less than 5.7% between simulation and real-time experiments.
NDA outperformed IACO and PRM, showing significant improvements in smooth path planning and path length optimization, with savings exceeding 5%.

Conclusions:

The Neighborhood Dragonfly Algorithm (NDA) offers a consistent, feasible, and efficient solution for autonomous drone navigation in indoor environments.
NDA provides a strong foundation for developing advanced and reliable drone navigation systems capable of handling complex scenarios.