This paper presents a modeling and analysis method for the motion planning and control of mobile robot systems in a hybrid fashion. Robotic systems obtain environmental information from perceptive sensors and respond to the perceptions to execute the task through decision and control process. The hybrid perceptive model of the robotic system has one continuous and two discrete layers, and it also has continuous and discrete perceptive references. The discrete layers enable the robot system to plan and modify original path through switching. Stability can be guaranteed in switching. The hybrid perceptive references can not be stopped by blocking the continuous reference, including obstacles. The model is verified by experiments.

In real-time Internet based teleoperation, different robotic tasks have different dexterity requirements during task progress and thus different network resources are required for safe and reliable task accomplishment. In order to control remote manipulators efficiently and smoothly via the Internet, dynamic bandwidth allocation is crucial to successful accomplishment of robotic tasks controlled by remote operator. In this paper, a novel bandwidth allocation mechanism is developed based on the online measured task dexterity index of current dexterous tasks so that operators can control remote manipulators efficiently and smoothly even under poor network quality. Experiments have been conducted to demonstrate the effectiveness of the presented resource (bandwidth) allocation algorithm in Internet based teleoperation system.

A model identification method for unknown environments has been developed. By the interactions between a mobile manipulator and the unknown object, a nonholonomic cart, sensory information has been collected to estimate the model parameters of the cart, which are used to control the cart. Since the raw data are contaminated by noise they can not be modeled statistically, a wavelet based least square method is proposed to estimate these parameters for the cart. The raw signal has been decomposed into a certain bandwidth to generate a series of new signals, which are used to estimate the parameters. The new signal, which has the minimal estimation residual in least square sense is adopted as the best estimation. The error convergence of the estimation approach is given. The experimental results indicate that the estimation accuracy can be significantly improved by the use of the proposed method.

This paper presents theoretical and experimental results on supermedia enhanced human/machine cooperative control of robot formations. Supermedia is the collection of all feedback streams rendered for the operator; such as video, haptic, temperature and others. The core idea is to utilize machine intelligence for the control of a robot formation. However, once this intelligence is insufficient to cope with unexpected events, human intervention is an option. To accomplish this, without the need for replanning, perceptive planning and control theory is utilized. This would allow the cooperation of human and machine for the control of the robot formation. To increase the flexibility and efficiency of such systems, commands of different levels of complexity can be issued. This gives rise to a hierarchical command structure, which can be described by a hierarchical perceptive frame that is modeled using automata and languages.