Sensor-based task-constrained motion planning using model predictive control

Massimo Cefalo; Emanuele Magrini; Giuseppe Oriolo

doi:10.1016/j.ifacol.2018.11.545

Sensor-based task-constrained motion planning using model predictive control

Massimo Cefalo
, Emanuele Magrini
, Giuseppe Oriolo

University of Rome La Sapienza

Research output: Contribution to journal › Conference article › peer-review

Abstract

A redundant robotic system must execute a task in a workspace populated by obstacles whose motion is unknown in advance. For this problem setting, we present a sensor-based planner that uses Model Predictive Control (MPC) to generate motion commands for the robot. We also propose a real-time implementation of the planner based on ACADO, an open source toolkit for solving general nonlinear MPC problems. The effectiveness of the proposed algorithm is shown through simulations and experiments carried out on a UR10 manipulator.

Original language	English
Pages (from-to)	220-225
Number of pages	6
Journal	IFAC-PapersOnLine
Volume	51
Issue number	22
Early online date	12 Dec 2018
DOIs	https://doi.org/10.1016/j.ifacol.2018.11.545
Publication status	Published - 12 Dec 2018
Externally published	Yes
Event	12th IFAC Symposium on Robot Control - Budapest Congress Centre, Budapest, Hungary Duration: 27 Aug 2018 → 30 Aug 2018 https://www.ifac-control.org/

Bibliographical note

Journal issue date: 2018

Keywords

Motion Planning
Obstacle Avoidance
Predictive Control

Access to Document

10.1016/j.ifacol.2018.11.545Licence: Other

Cite this

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title = "Sensor-based task-constrained motion planning using model predictive control",

abstract = "A redundant robotic system must execute a task in a workspace populated by obstacles whose motion is unknown in advance. For this problem setting, we present a sensor-based planner that uses Model Predictive Control (MPC) to generate motion commands for the robot. We also propose a real-time implementation of the planner based on ACADO, an open source toolkit for solving general nonlinear MPC problems. The effectiveness of the proposed algorithm is shown through simulations and experiments carried out on a UR10 manipulator.",

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author = "Massimo Cefalo and Emanuele Magrini and Giuseppe Oriolo",

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AU - Cefalo, Massimo

AU - Magrini, Emanuele

AU - Oriolo, Giuseppe

N1 - Journal issue date: 2018

PY - 2018/12/12

Y1 - 2018/12/12

N2 - A redundant robotic system must execute a task in a workspace populated by obstacles whose motion is unknown in advance. For this problem setting, we present a sensor-based planner that uses Model Predictive Control (MPC) to generate motion commands for the robot. We also propose a real-time implementation of the planner based on ACADO, an open source toolkit for solving general nonlinear MPC problems. The effectiveness of the proposed algorithm is shown through simulations and experiments carried out on a UR10 manipulator.

AB - A redundant robotic system must execute a task in a workspace populated by obstacles whose motion is unknown in advance. For this problem setting, we present a sensor-based planner that uses Model Predictive Control (MPC) to generate motion commands for the robot. We also propose a real-time implementation of the planner based on ACADO, an open source toolkit for solving general nonlinear MPC problems. The effectiveness of the proposed algorithm is shown through simulations and experiments carried out on a UR10 manipulator.

KW - Motion Planning

KW - Obstacle Avoidance

KW - Predictive Control

U2 - 10.1016/j.ifacol.2018.11.545

DO - 10.1016/j.ifacol.2018.11.545

M3 - Conference article

AN - SCOPUS:85058240882

SN - 2405-8971

VL - 51

SP - 220

EP - 225

JO - IFAC-PapersOnLine

JF - IFAC-PapersOnLine

IS - 22

T2 - 12th IFAC Symposium on Robot Control

Y2 - 27 August 2018 through 30 August 2018

ER -

Sensor-based task-constrained motion planning using model predictive control

Abstract

Bibliographical note

Keywords

Access to Document

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Cite this