Sensor-Based Task-Constrained Motion Planning using Model Predictive Control
| Autor: | Massimo Cefalo, Emanuele Magrini, Giuseppe Oriolo |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
0209 industrial biotechnology
Computer science Control engineering 02 engineering and technology Workspace Motion (physics) Task (project management) Computer Science::Robotics Nonlinear system Model predictive control Motion Planning Obstacle Avoidance Predictive Control Control and Systems Engineering 020901 industrial engineering & automation 0202 electrical engineering electronic engineering information engineering Robot 020201 artificial intelligence & image processing Motion planning Manipulator |
| Zdroj: | SyRoCo |
| ISSN: | 2405-8963 |
| DOI: | 10.1016/j.ifacol.2018.11.545 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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