Human-intuitable collision avoidance for autonomous and semi-autonomous aerial vehicles

Research output: Contribution to journalConference article

Abstract

This paper addresses the problem of path planning and collision avoidance for multiple aerial vehicles. We develop an algorithm that is scalable, operates in real-time, and is implementable on a UAV's onboard computers. Our method makes use of velocity-based potential field methods for collision avoidance. Potential field methods utilize attractive and repulsive potential to guide the robot towards the goal. We demonstrate the system in a simulation involving 50 vehicles. Next, on physical platforms, we conduct experiments with two and three UAVs capturing the system's ability to avoid other moving vehicles and measuring closest approach. We find that the UAVs not only avoid the collisions but also maintain a minimum distance specified. This method can be used in the future for trajectory planning of multiple aerial vehicles in dense airspaces.

Original languageEnglish (US)
JournalAnnual Forum Proceedings - AHS International
Volume2018-May
StatePublished - Jan 1 2018
Event74th American Helicopter Society International Annual Forum and Technology Display 2018: The Future of Vertical Flight - Phoenix, United States
Duration: May 14 2018May 17 2018

Fingerprint

Collision avoidance
Unmanned aerial vehicles (UAV)
Antennas
Motion planning
Trajectories
Robots
Planning
Drones
Experiments

All Science Journal Classification (ASJC) codes

  • Engineering(all)

Cite this

@article{d38f98b6134a49049f765aa66316d39c,
title = "Human-intuitable collision avoidance for autonomous and semi-autonomous aerial vehicles",
abstract = "This paper addresses the problem of path planning and collision avoidance for multiple aerial vehicles. We develop an algorithm that is scalable, operates in real-time, and is implementable on a UAV's onboard computers. Our method makes use of velocity-based potential field methods for collision avoidance. Potential field methods utilize attractive and repulsive potential to guide the robot towards the goal. We demonstrate the system in a simulation involving 50 vehicles. Next, on physical platforms, we conduct experiments with two and three UAVs capturing the system's ability to avoid other moving vehicles and measuring closest approach. We find that the UAVs not only avoid the collisions but also maintain a minimum distance specified. This method can be used in the future for trajectory planning of multiple aerial vehicles in dense airspaces.",
author = "Sagar Lakhmani and Langelaan, {Jacob Willem} and Wagner, {Alan Richard}",
year = "2018",
month = "1",
day = "1",
language = "English (US)",
volume = "2018-May",
journal = "Annual Forum Proceedings - AHS International",
issn = "1552-2938",
publisher = "American Helicopter Society",

}

TY - JOUR

T1 - Human-intuitable collision avoidance for autonomous and semi-autonomous aerial vehicles

AU - Lakhmani, Sagar

AU - Langelaan, Jacob Willem

AU - Wagner, Alan Richard

PY - 2018/1/1

Y1 - 2018/1/1

N2 - This paper addresses the problem of path planning and collision avoidance for multiple aerial vehicles. We develop an algorithm that is scalable, operates in real-time, and is implementable on a UAV's onboard computers. Our method makes use of velocity-based potential field methods for collision avoidance. Potential field methods utilize attractive and repulsive potential to guide the robot towards the goal. We demonstrate the system in a simulation involving 50 vehicles. Next, on physical platforms, we conduct experiments with two and three UAVs capturing the system's ability to avoid other moving vehicles and measuring closest approach. We find that the UAVs not only avoid the collisions but also maintain a minimum distance specified. This method can be used in the future for trajectory planning of multiple aerial vehicles in dense airspaces.

AB - This paper addresses the problem of path planning and collision avoidance for multiple aerial vehicles. We develop an algorithm that is scalable, operates in real-time, and is implementable on a UAV's onboard computers. Our method makes use of velocity-based potential field methods for collision avoidance. Potential field methods utilize attractive and repulsive potential to guide the robot towards the goal. We demonstrate the system in a simulation involving 50 vehicles. Next, on physical platforms, we conduct experiments with two and three UAVs capturing the system's ability to avoid other moving vehicles and measuring closest approach. We find that the UAVs not only avoid the collisions but also maintain a minimum distance specified. This method can be used in the future for trajectory planning of multiple aerial vehicles in dense airspaces.

UR - http://www.scopus.com/inward/record.url?scp=85054520074&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85054520074&partnerID=8YFLogxK

M3 - Conference article

VL - 2018-May

JO - Annual Forum Proceedings - AHS International

JF - Annual Forum Proceedings - AHS International

SN - 1552-2938

ER -