Minimum set of feedback sensors for high performance decentralized cooperative force control of redundant manipulators

David Navarro Alarcon, V. Parra-Vega, E. Olguín-Díaz

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

4 Citations (Scopus)


Manipulation tasks by humans require a variety of biological sensors, from visual, kinesthetic, contact, pressure, temperature, etc., as well as lots of involved degrees of freedom, conforming a highly redundant biomechanical system. Besides such sensor's network and the unmatched brainpower of humans to carry out this task, biomechanical redundancy is a key issue to solve whether we want to transfer this ability on mechanical robots. A much simpler version of a human manipulation task is a cooperative robotic task, which has been mastered by experimental robots at some degree. In this case, the question is whether robots can achieve high performance (fast, accurate and robust tracking without complete knowledge of system dynamics) for this cooperative task using only sensors based on the Newtonian dynamics (encoders, tachometers and force sensors). In this paper, we find a positive answer to this question provided that precise redundancy resolution is introduced, otherwise an intelligent sensor networks is required. Simulation results of representative cooperative tasks employing 7 degrees of freedom manipulators illustrate this concept and farther discussions are presented.
Original languageEnglish
Title of host publicationROSE 2008 - IEEE International Workshop on Robotic and Sensors Environments Proceedings
Number of pages6
Publication statusPublished - 22 Dec 2008
Externally publishedYes
EventPhotonic Device Engineering for Dual-Use Applications - Orlando, FL, United States
Duration: 17 Apr 199517 Apr 1995


ConferencePhotonic Device Engineering for Dual-Use Applications
Country/TerritoryUnited States
CityOrlando, FL


  • Cooperative task
  • Force control
  • Redundant manipulators

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering


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