Safe multi-agent drone control using control barrier functions and acceleration fields

Vinicius Mariano Gonçalves; Dimitris Chaikalis; Anthony Tzes; Farshad Khorrami

doi:10.1016/j.robot.2023.104601

Safe multi-agent drone control using control barrier functions and acceleration fields

Vinicius Mariano Gonçalves, Dimitris Chaikalis, Anthony Tzes, Farshad Khorrami

Research output: Contribution to journal › Article › peer-review

Abstract

The objective of this article is to develop real-time controllers for positioning multi-agent autonomous platforms (specifically drones) without any collisions. The framework is divided in two controllers. The first, based on control barrier functions and quadratic programming, is an acceleration field, that computes an acceleration for each position and velocity of the system that guides the agents towards the goal while avoiding obstacles and inter-collisions. The second controls the agents’ total thrusts and angular velocities to track the previously-defined acceleration profiles. We show formal guarantees for both controllers, and simulation and experimental studies are used to show the efficacy of the proposed approach.

Original language	English (US)
Article number	104601
Journal	Robotics and Autonomous Systems
Volume	172
DOIs	https://doi.org/10.1016/j.robot.2023.104601
State	Published - Feb 2024

Keywords

Control barrier functions
Multi-agent systems
Obstacle avoidance
Optimization

ASJC Scopus subject areas

Control and Systems Engineering
Software
General Mathematics
Computer Science Applications

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10.1016/j.robot.2023.104601

Cite this

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title = "Safe multi-agent drone control using control barrier functions and acceleration fields",

abstract = "The objective of this article is to develop real-time controllers for positioning multi-agent autonomous platforms (specifically drones) without any collisions. The framework is divided in two controllers. The first, based on control barrier functions and quadratic programming, is an acceleration field, that computes an acceleration for each position and velocity of the system that guides the agents towards the goal while avoiding obstacles and inter-collisions. The second controls the agents{\textquoteright} total thrusts and angular velocities to track the previously-defined acceleration profiles. We show formal guarantees for both controllers, and simulation and experimental studies are used to show the efficacy of the proposed approach.",

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AU - Gonçalves, Vinicius Mariano

AU - Chaikalis, Dimitris

AU - Tzes, Anthony

AU - Khorrami, Farshad

PY - 2024/2

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N2 - The objective of this article is to develop real-time controllers for positioning multi-agent autonomous platforms (specifically drones) without any collisions. The framework is divided in two controllers. The first, based on control barrier functions and quadratic programming, is an acceleration field, that computes an acceleration for each position and velocity of the system that guides the agents towards the goal while avoiding obstacles and inter-collisions. The second controls the agents’ total thrusts and angular velocities to track the previously-defined acceleration profiles. We show formal guarantees for both controllers, and simulation and experimental studies are used to show the efficacy of the proposed approach.

AB - The objective of this article is to develop real-time controllers for positioning multi-agent autonomous platforms (specifically drones) without any collisions. The framework is divided in two controllers. The first, based on control barrier functions and quadratic programming, is an acceleration field, that computes an acceleration for each position and velocity of the system that guides the agents towards the goal while avoiding obstacles and inter-collisions. The second controls the agents’ total thrusts and angular velocities to track the previously-defined acceleration profiles. We show formal guarantees for both controllers, and simulation and experimental studies are used to show the efficacy of the proposed approach.

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KW - Multi-agent systems

KW - Obstacle avoidance

KW - Optimization

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Safe multi-agent drone control using control barrier functions and acceleration fields

Abstract

Keywords

ASJC Scopus subject areas

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