Supervisory control for co-ordination of multiple robots†

Garg, Devendra P.

doi:10.1080/00207729208949415

Cited by 1 publication

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“…All of these considerations inspire research in real time control of multiple cooperating robotic systems. A lot of research has been carried out in the robotics and manufacturing automation (RAMA) Laboratory at Duke University for coordinating position, motion, and grasping force for multi-manipulator systems acting in synchronization (Garg, 2000;Garg, 1992;Nagchaudhuri and Garg, 1992). A number of control strategies, such as fuzzy logic (Prabhu and Garg, 1998;Woodard and Garg, 1999;Prabhu and Garg, 1995) neural networks Prabhu and Garg, 1996;Ananthraman and Garg, 1993a, b), general redundancy optimization method (Zhang and Ferch, 1998), and two level hierarchical fuzzy logic for hyperredundant cooperating robots (Ivanescu and Bizdoaca, 2000) have been published in technical literature.…”

Section: Introductionmentioning

confidence: 99%

Optimization techniques applied to multiple manipulators for path planning and torque minimization

Garg

Kumar

2002

Engineering Applications of Artificial Intelligence

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114

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This paper presents the formulation and application of a strategy for the determination of an optimal trajectory for a multiple robotic configuration. Genetic Algorithm (GA) and Simulated Annealing (SA) have been used as the optimization techniques and results obtained from them compared. First, the motivation for multiple robot control and the current state-of-art in the field of cooperating robots are briefly given. This is followed by a discussion of energy minimization techniques in the context of robotics, and finally, the principles of using genetic algorithms and simulated annealing as an optimization tool are included. The initial and final positions of the end effector are specified. Two cases, one of a single manipulator, and the other of two cooperating manipulators carrying a common payload illustrate the proposed approach. The GA and SA techniques identify the optimal trajectory based on minimum joint torque requirements. The simulations performed for both the cases show that although both the methods converge to the global minimum, the SA converges to solution faster than the GA. r

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