SwarmPath: Drone Swarm Navigation through Cluttered Environments Leveraging Artificial Potential Field and Impedance Control

TL;DR

SwarmPath combines artificial potential fields and impedance control to enable drone swarms to navigate cluttered environments efficiently and safely, reducing travel time and maintaining connectivity.

Contribution

This paper introduces SwarmPath, a novel integrated approach for drone swarm navigation that enhances collision avoidance and path efficiency in complex environments.

Findings

Reduces travel time by 30% compared to traditional methods.

Maintains drone connectivity and safety during navigation.

Achieves an average absolute percentage error of 6% between simulation and real-world trajectories.

Abstract

In the area of multi-drone systems, navigating through dynamic environments from start to goal while providing collision-free trajectory and efficient path planning is a significant challenge. To solve this problem, we propose a novel SwarmPath technology that involves the integration of Artificial Potential Field (APF) with Impedance Controller. The proposed approach provides a solution based on collision free leader-follower behaviour where drones are able to adapt themselves to the environment. Moreover, the leader is virtual while drones are physical followers leveraging APF path planning approach to find the smallest possible path to the target. Simultaneously, the drones dynamically adjust impedance links, allowing themselves to create virtual links with obstacles to avoid them. As compared to conventional APF, the proposed SwarmPath system not only provides smooth collision-avoidance but also enable agents to efficiently pass through narrow passages by reducing the total travel time by 30% while ensuring safety in terms of drones connectivity. Lastly, the results also illustrate that the discrepancies between simulated and real environment, exhibit an average absolute percentage error (APE) of 6% of drone trajectories. This underscores the reliability of our solution in real-world scenarios.