Obstacle detection, tracking and avoidance for a teleoperated UAV

Marcin Odelga, Paolo Stegagno, Heinrich Bulthoff

Research output: Chapter in Book/Report/Conference proceedingConference contribution

21 Citations (Scopus)

Abstract

In this paper, we present a collision-free indoor navigation algorithm for teleoperated multirotor Unmanned Aerial Vehicles (UAVs). Assuming an obstacle rich environment, the algorithm keeps track of detected obstacles in the local surroundings of the robot. The detection part of the algorithm is based on measurements from an RGB-D camera and a Bin-Occupancy filter capable of tracking an unspecified number of targets. We use the estimate of the robot's velocity to update the obstacles state when they leave the direct field of view of the sensor. The avoidance part of the algorithm is based on the Model Predictive Control approach. By predicting the possible future obstacles states, it filters the operator commands to prevent collisions. The method is validated on a platform equipped with its own computational unit, which makes it self-sufficient in terms of external CPUs.

Original languageEnglish
Title of host publication2016 IEEE International Conference on Robotics and Automation, ICRA 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages2984-2990
Number of pages7
Volume2016-June
ISBN (Electronic)9781467380263
DOIs
Publication statusPublished - 2016 Jun 8
Externally publishedYes
Event2016 IEEE International Conference on Robotics and Automation, ICRA 2016 - Stockholm, Sweden
Duration: 2016 May 162016 May 21

Other

Other2016 IEEE International Conference on Robotics and Automation, ICRA 2016
CountrySweden
CityStockholm
Period16/5/1616/5/21

Fingerprint

Unmanned aerial vehicles (UAV)
Robots
Model predictive control
Bins
Program processors
Navigation
Cameras
Sensors

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Cite this

Odelga, M., Stegagno, P., & Bulthoff, H. (2016). Obstacle detection, tracking and avoidance for a teleoperated UAV. In 2016 IEEE International Conference on Robotics and Automation, ICRA 2016 (Vol. 2016-June, pp. 2984-2990). [7487464] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICRA.2016.7487464

Obstacle detection, tracking and avoidance for a teleoperated UAV. / Odelga, Marcin; Stegagno, Paolo; Bulthoff, Heinrich.

2016 IEEE International Conference on Robotics and Automation, ICRA 2016. Vol. 2016-June Institute of Electrical and Electronics Engineers Inc., 2016. p. 2984-2990 7487464.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Odelga, M, Stegagno, P & Bulthoff, H 2016, Obstacle detection, tracking and avoidance for a teleoperated UAV. in 2016 IEEE International Conference on Robotics and Automation, ICRA 2016. vol. 2016-June, 7487464, Institute of Electrical and Electronics Engineers Inc., pp. 2984-2990, 2016 IEEE International Conference on Robotics and Automation, ICRA 2016, Stockholm, Sweden, 16/5/16. https://doi.org/10.1109/ICRA.2016.7487464
Odelga M, Stegagno P, Bulthoff H. Obstacle detection, tracking and avoidance for a teleoperated UAV. In 2016 IEEE International Conference on Robotics and Automation, ICRA 2016. Vol. 2016-June. Institute of Electrical and Electronics Engineers Inc. 2016. p. 2984-2990. 7487464 https://doi.org/10.1109/ICRA.2016.7487464
Odelga, Marcin ; Stegagno, Paolo ; Bulthoff, Heinrich. / Obstacle detection, tracking and avoidance for a teleoperated UAV. 2016 IEEE International Conference on Robotics and Automation, ICRA 2016. Vol. 2016-June Institute of Electrical and Electronics Engineers Inc., 2016. pp. 2984-2990
@inproceedings{79ffdef1bda34a6bb9c7178b961192e7,
title = "Obstacle detection, tracking and avoidance for a teleoperated UAV",
abstract = "In this paper, we present a collision-free indoor navigation algorithm for teleoperated multirotor Unmanned Aerial Vehicles (UAVs). Assuming an obstacle rich environment, the algorithm keeps track of detected obstacles in the local surroundings of the robot. The detection part of the algorithm is based on measurements from an RGB-D camera and a Bin-Occupancy filter capable of tracking an unspecified number of targets. We use the estimate of the robot's velocity to update the obstacles state when they leave the direct field of view of the sensor. The avoidance part of the algorithm is based on the Model Predictive Control approach. By predicting the possible future obstacles states, it filters the operator commands to prevent collisions. The method is validated on a platform equipped with its own computational unit, which makes it self-sufficient in terms of external CPUs.",
author = "Marcin Odelga and Paolo Stegagno and Heinrich Bulthoff",
year = "2016",
month = "6",
day = "8",
doi = "10.1109/ICRA.2016.7487464",
language = "English",
volume = "2016-June",
pages = "2984--2990",
booktitle = "2016 IEEE International Conference on Robotics and Automation, ICRA 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",

}

TY - GEN

T1 - Obstacle detection, tracking and avoidance for a teleoperated UAV

AU - Odelga, Marcin

AU - Stegagno, Paolo

AU - Bulthoff, Heinrich

PY - 2016/6/8

Y1 - 2016/6/8

N2 - In this paper, we present a collision-free indoor navigation algorithm for teleoperated multirotor Unmanned Aerial Vehicles (UAVs). Assuming an obstacle rich environment, the algorithm keeps track of detected obstacles in the local surroundings of the robot. The detection part of the algorithm is based on measurements from an RGB-D camera and a Bin-Occupancy filter capable of tracking an unspecified number of targets. We use the estimate of the robot's velocity to update the obstacles state when they leave the direct field of view of the sensor. The avoidance part of the algorithm is based on the Model Predictive Control approach. By predicting the possible future obstacles states, it filters the operator commands to prevent collisions. The method is validated on a platform equipped with its own computational unit, which makes it self-sufficient in terms of external CPUs.

AB - In this paper, we present a collision-free indoor navigation algorithm for teleoperated multirotor Unmanned Aerial Vehicles (UAVs). Assuming an obstacle rich environment, the algorithm keeps track of detected obstacles in the local surroundings of the robot. The detection part of the algorithm is based on measurements from an RGB-D camera and a Bin-Occupancy filter capable of tracking an unspecified number of targets. We use the estimate of the robot's velocity to update the obstacles state when they leave the direct field of view of the sensor. The avoidance part of the algorithm is based on the Model Predictive Control approach. By predicting the possible future obstacles states, it filters the operator commands to prevent collisions. The method is validated on a platform equipped with its own computational unit, which makes it self-sufficient in terms of external CPUs.

UR - http://www.scopus.com/inward/record.url?scp=84977594490&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84977594490&partnerID=8YFLogxK

U2 - 10.1109/ICRA.2016.7487464

DO - 10.1109/ICRA.2016.7487464

M3 - Conference contribution

VL - 2016-June

SP - 2984

EP - 2990

BT - 2016 IEEE International Conference on Robotics and Automation, ICRA 2016

PB - Institute of Electrical and Electronics Engineers Inc.

ER -