Motion planning for robotic manipulators using robust constrained control. (February 2017)

• Record Type: Journal Article
• Authors: Zanchettin, Andrea Maria; Rocco, Paolo
• Is Part Of: Control engineering practice. Volume 59(2017)
• Page Start: 127
• Page End: 136
• Publication Date: 2017-02
• Subjects: Robotic manipulators -- Trajectory planning -- Constraints -- Robustness -- Uncertainty -- Industrial robots; Automatic control -- Periodicals; 629.89
• DOI: 10.1016/j.conengprac.2016.11.010
• Languages: English
• ISSNs: 0967-0661

Abstract

Abstract: Since their first appearance in the 1970's, industrial robotic manipulators have considerably extended their application fields, allowing end-users to adopt this technology in previously unexplored scenarios. Correspondingly, the way robot motion can be specified has become more and more complex, requiring new capabilities to the robot, such as reactivity and adaptability. For an even enhanced and widespread use of industrial manipulators, including the newly introduced collaborative robots, it is necessary to simplify robot programming, thus allowing this activity to be handled by non-expert users. Next generation robot controllers should intelligently and autonomously interpret production constraints, specified by an application expert, and transform them into motion commands only at a lower and real-time level, where updated sensor information or other kind of events can be handled consistently with the higher level specifications. The availability of several execution strategies could be then effectively exploited in order to further enhance the flexibility of the resulting robot motion, especially during collaboration with humans. This paper presents a novel methodology for motion specification and robust reactive execution. Traditional trajectory generation techniques and optimisation-based control strategies are merged into a unified framework for simultaneous motion planning and control. An experimental case study demonstrates the effectiveness and the robustness of this approach, as applied to an image-guided grasping task.