Multi-manipulator System Research Articles

Neural Network Learning Control for Position and Force Tracking of Multi-Manipulator Systems

ABSTRACTIn this article, an approach to improving the performance of multi-manipulator systems using neural networks is presented. This approach is formulated in the constrained motion framework, within which a nominal feedback control augmented by a neural network is derived. It is shown that the closed-loop system with the neural network learning on-line is stable in the sense that all signals in the systems are bounded. It is further proved that the performance of the multi-manipulator system is improved in the sense that the “size” of a certain error measure decreases as the learning process of the neural network is iterated. Results of computer simulations conducted to verify the analytical conclusions are presented. The results of this work suggest that neural networks could be used as “add-on” control modules to improve the performance of industrial robots in execution of tasks involving two or more cooperative manipulators.

Time-optimal trajectories for cooperative multi-manipulator systems

We present two schemes for planning the time-optimal trajectory for cooperative multi-manipulator system (CMMS) carrying a common object. We assume that the desired path is given and parameterizable by an arclength variable. Both approaches take into account the dynamics of the manipulators and object. The first approach employs linear programming techniques, and it allows us to obtain the time-optimal execution of the given task utilizing the maximum torque capacities of the joint motors. The second approach is a sub-time-optimal method that is computationally very efficient. In the second approach the given load is divided into a share for each robot in the CMMS in a manner in which the trajectory acceleration/deceleration is maximized, hence the trajectory execution time is minimized. This load distribution approach uses optimization schemes that degenerate to a linear search algorithm for the case of two robots manipulating a common load, and this results in significant reduction of computation time. The load distribution scheme not only enables us to reduce the computation time, but also gives us the possibility of applying this method in real-time planning and control of CMMS. Further, we show that for certain object trajectories the load distribution scheme yields truly time-optimal trajectories.