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M-SET: Multi-Drone Swarm Intelligence Experimentation with Collision Avoidance Realism

Chuhao Qin, Alexander Robins, Callum Lillywhite-Roake, Adam Pearce, Hritik Mehta, Scott James, Tsz Ho Wong, Evangelos Pournaras

TL;DR

The Multi-drone Sensing Experimentation Testbed (M-SET) is introduced, a novel platform designed to prototype, develop, test, and evaluate distributed sensing with swarm intelligence, and ensures collision-free navigation and sensing, further optimized via a multi-agent collective learning algorithm.

Abstract

Distributed sensing by cooperative drone swarms is crucial for several Smart City applications, such as traffic monitoring and disaster response. Using an indoor lab with inexpensive drones, a testbed supports complex and ambitious studies on these systems while maintaining low cost, rigor, and external validity. This paper introduces the Multi-drone Sensing Experimentation Testbed (M-SET), a novel platform designed to prototype, develop, test, and evaluate distributed sensing with swarm intelligence. M-SET addresses the limitations of existing testbeds that fail to emulate collisions, thus lacking realism in outdoor environments. By integrating a collision avoidance method based on a potential field algorithm, M-SET ensures collision-free navigation and sensing, further optimized via a multi-agent collective learning algorithm. Extensive evaluation demonstrates accurate energy consumption estimation and a low risk of collisions, providing a robust proof-of-concept. New insights show that M-SET has significant potential to support ambitious research with minimal cost, simplicity, and high sensing quality.

M-SET: Multi-Drone Swarm Intelligence Experimentation with Collision Avoidance Realism

TL;DR

The Multi-drone Sensing Experimentation Testbed (M-SET) is introduced, a novel platform designed to prototype, develop, test, and evaluate distributed sensing with swarm intelligence, and ensures collision-free navigation and sensing, further optimized via a multi-agent collective learning algorithm.

Abstract

Distributed sensing by cooperative drone swarms is crucial for several Smart City applications, such as traffic monitoring and disaster response. Using an indoor lab with inexpensive drones, a testbed supports complex and ambitious studies on these systems while maintaining low cost, rigor, and external validity. This paper introduces the Multi-drone Sensing Experimentation Testbed (M-SET), a novel platform designed to prototype, develop, test, and evaluate distributed sensing with swarm intelligence. M-SET addresses the limitations of existing testbeds that fail to emulate collisions, thus lacking realism in outdoor environments. By integrating a collision avoidance method based on a potential field algorithm, M-SET ensures collision-free navigation and sensing, further optimized via a multi-agent collective learning algorithm. Extensive evaluation demonstrates accurate energy consumption estimation and a low risk of collisions, providing a robust proof-of-concept. New insights show that M-SET has significant potential to support ambitious research with minimal cost, simplicity, and high sensing quality.
Paper Structure (21 sections, 5 equations, 7 figures)

This paper contains 21 sections, 5 equations, 7 figures.

Table of Contents

  1. Introduction
  2. Related Work
  3. Testbed Design
  4. Testbed components
  5. UAVs
  6. Virtual sensing environment
  7. Swarm intelligence
  8. Collision detection and collision avoidance
  9. Architecture overview
  10. Testbed Prototyping
  11. Hardware UAVs for sensing
  12. Setting up an indoor sensing environment
  13. Swarm intelligence using collective learning
  14. Collision avoidance using artificial potential field
  15. Experimental Evaluation
  16. ...and 6 more sections

Figures (7)

  • Figure 1: Three types of collisions and corresponding avoidance methods. Cross collision denotes two drones fly across each other; Parallel collision indicates two drones fly towards each other; Destination-occupied collision means one drone performing sensing occupies another drone's destination.
  • Figure 2: An overview of the prototyped M-SET architecture.
  • Figure 3: Assembly of Crazyflies for two types of functions.
  • Figure 4: Indoor sensing lab using a large screen, Crazyflies, wireless chargers, and lighthouse base stations for positioning. Drones fly to grid cells and collect sensor data by recording the videos of traffic vehicles.
  • Figure 5: An example of collision avoidance using artificial potential field: (a) The blue drone is the target drone attracted by the destination, whereas the red drone is the obstacle drone repelling the target drone. The vectors influenced by both attractive and repulsive forces point towards the navigation of the target drone. (b) Since drone 2 has higher priority and stronger repulsive force than drone 1, drone 1 is pushed out of the target cell and "wait" until drone 2 passes by, and makes a "turn" when traveling to the next cell.
  • ...and 2 more figures