In this paper, a dynamic task allocation and controller design methodology for cooperative robot teams is presented. Fuzzy-logic-based utility functions are derived to quantify each robot's ability to perform a task. These utility functions are used to allocate tasks in real time through a limited lookahead control methodology partially based on the basic principles of discrete event supervisory control theory. The proposed controller design methodology accommodates flexibility in task assignments, robot coordination, and tolerance to robot failures and repairs. Implementation details of the proposed methodology are demonstrated through a warehouse patrolling case study.

This work proposes a teleoperation architecture for mobile robots in partially unknown environments under the presence of variable time delay. The system is provided with artificial intelligence represented by a probabilistic path planner that, in combination with a prediction module, assists the operator while guaranteeing a collision-free motion. For this purpose, a certain level of autonomy is given to the system. The structure was tested in indoor environments for different kinds of operators. A maximum time delay of 2s was successfully coped with.

This paper deals with the motion prediction and control of the macro–micro parallel manipulator system for a 500-m-aperture spherical radio telescope (FAST). Firstly, based on principles of parallel mechanism, a decoupled tracking and prediction algorithm to predict the position and orientation of the movable macro parallel manipulator is presented in this paper. Then, taken as the upper layer supervisory controller in the joint space of the micro parallel manipulator, the adaptive interaction PID controller utilizing the adaptive interaction algorithm to adjust the parameters of a canonical PID controller is discussed. In addition, the digital servo filters with feedforward are employed in the linear actuators as the lower layer controllers. Experimental results of a one-tenth scale FAST field model validate the effectiveness of the supervisory controller and the motion prediction algorithm.

This article presents the results of research concerning possibilities of applying multilayer perceptron type of neural network for fault diagnosis, state estimation, and prediction in the gas pipeline transmission network. The influence of several factors on accuracy of the multilayer perceptron was considered. The emphasis was put on the multilayer perceptrons' function as a state estimator. The choice of the most informative features, the amount and sampling period of training data sets, as well as different configurations of multilayer perceptrons were analyzed.

The Multimodal User Supervised Interface and Intelligent Control (MUSIIC) project focuses on a multimodal human-machine interface which addresses user need to manipulate familiar objects in an unstructured environment. The control of a robot by individuals with significant physical limitations presents a challenging problem of telemanipulation. This is addressed by a unique user-interface integrating the user's command (speech) and gestures (pointing) with autonomous planning techniques (knowledge-bases and 3-D vision). The resultant test-bed offers the opportunity to study telemanipulation by individuals with physical disabilities, and can be generalized to an effective technique for other, including remote and time-delayed, telemanipulation. This paper focuses on the knowledge-driven planning mechanism that is central to the MUSIIC system.