Task execution combined with in-contact obstacle navigation by exploiting torque feedback of sensitive robots
Autor: | Mohammad Safeea, Pedro Neto, Richard Bearee |
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Přispěvatelé: | Laboratoire d’Ingénierie des Systèmes Physiques et Numériques (LISPEN), Arts et Métiers Sciences et Technologies, HESAM Université (HESAM)-HESAM Université (HESAM) |
Jazyk: | angličtina |
Rok vydání: | 2020 |
Předmět: |
Structure (mathematical logic)
0209 industrial biotechnology Collaborative robots null space Computer science redundancy Control (management) Control engineering 02 engineering and technology intuitive interfaces Industrial and Manufacturing Engineering Task (project management) [SPI]Engineering Sciences [physics] 020303 mechanical engineering & transports 020901 industrial engineering & automation 0203 mechanical engineering Artificial Intelligence Obstacle Robot Torque Collision detection Sensitivity (control systems) torque feedback |
Zdroj: | Procedia Manufacturing Procedia Manufacturing, Elsevier, 2020, 51, pp.187-192. ⟨10.1016/j.promfg.2020.10.027⟩ |
ISSN: | 2351-9789 |
Popis: | Collaborative redundant manipulators are becoming more popular in industry. Lately, sensitive variants of those robots are introduced to the market. Their sensitivity is owed to the unique technology of integrating torque sensors into their joints. This technology has been used extensively for collision detection. Nevertheless, it can be used in other collaborative applications. In this study, we present a novel control method that uses the torque feedback at the joints to perform automatic adjustment of the self-motion manifold during a contact with surrounding obstacles, while allowing the user to control the robot at the end-effector (EEF) level. This makes the interaction with sensitive redundant manipulators more intuitive to users. Experimental tests on KUKA iiwa robot proved the effectiveness of the proposed method for navigating obstacles during a contact with robot’s structure while keeping the precision in the task under execution. |
Databáze: | OpenAIRE |
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