In order to speed up image processing in visual servoing, the distributed computational power across networks and appropriate data transmission mechanisms are of particular interest. In this paper, a high sampling rate of visual feedback is achieved by distributed computation on a cloud image processing platform. For target tracking with a networked visual servo control system, a switching control law considering the varying feedback delay caused by image processing and data transmission is applied to improve the control performance. A sending rate scheduling strategy aiming at saving the network load is proposed based on the tracking error. Experiments on a 7 degree-of-freedom (DoF) manipulator are carried out to validate the proposed approach. The proposed approach shows a similar control performance as a system without sending rate scheduling, however, beneficially with largely reduced network load.

In our previous work, we proposed a novel approach to partition sensors on wireless sensor network, called node coverage grouping (NCG), where sensors in the connectivity group are within sensing range of each other, and their collected data are assumed to be similar. Power saving was achieved by alternatively turning on a sensor in a connectivity group for working. We also proved that partitioning n nodes by node coverage grouping into connectivity groups is a NP-hard problem, and proposed a heuristic algorithm of node coverage grouping with the time complexity of O(n3). In this paper, we present two approaches, called periodic probing and next-turn probing, to address the avoidance of blind period for the node coverage grouping. The experimental results showed that the NCG applying the avoidance of blind period would have shorter blind period than two other power saving approaches on wireless sensor network, the Lightweight Deployment-Aware Scheduling (LDAS) and the Probing Environment and Adaptive Sleeping (PEAS). In conclusion, the NCG-next-turn probing outperformed the LDAS and the PEAS with more number of living nodes, more coverage, and shorter blind period with respect to the proceeding of the system lifetime.

In this paper, a networked visual servo control system with distributed computation is proposed to overcome the low sampling rate problem in vision-based control systems. A real-time image data transmission protocol based on Realtime Transport Protocol (RTP) is developed. The captured images are sent to different processing nodes connected over a communication network and processed in parallel. Thus, a high sampling rate of the visual feedback is achieved under a cloud image processing architecture. The varying image processing delay caused by the varying number of extracted features and the random transmission delay are modeled as a random process with Bernoulli distribution. By using the input-delay approach, the resulted networked visual servo control system is reformulated into a stochastic continuous-time system with time-varying delay. Experiments on two 1-DoF linear motor modules are carried out to validate the proposed approach. A visual servo control system without parallel distributed computation is implemented for comparison. The experimental results demonstrate significant performance improvement by the proposed approach.

In this paper, a novel switching controller is proposed for a networked visual servo control system with varying feedback delay due to image processing and data transmission. The varying image processing delay caused by the varying number of extracted features for pose estimation due to different view angles, illumination conditions and noise, is modeled by its occurrence probability. The time delay due to transmission over the communication network is also modeled as random process. By using a sampled-data system approach and an input-delay approach, the linearized visual servo control system is reformulated into a stochastic continuous-time system with time-varying delay. A novel stability condition and associated switching controller are derived based on the occurrence probabilities of delays. Experiments on a 1-DoF linear module equipped with a camera are conducted to validate the proposed approach. A non-switching controller approach is implemented for comparison. The experimental results demonstrate significant performance improvement of the proposed control approach.

The stability and performance of visual servo control systems strongly depend on the delays caused by image processing. In order to accelerate the visual feedback, the distributed computational power across networks and appropriate data transmission mechanism are of particular interest. In this paper, a novel distributed computation with data scheduling is proposed for networked visual servo control systems (NVSCSs) aiming at improving the control performance. A realtime transport protocol is developed for image data transmission. For a NVSCS which is modeled as a continuous-time system with computation, transmission and holding delays, a switching control law is applied. A probabilistic sampling scheduler is derived such that the control performance and the network load caused by image data transmission are balanced. Experiments on two 1-DoF linear modules equipped with a camera are conducted to validate the proposed approach. A visual servo system without data scheduling is implemented for comparison. The experimental results demonstrate a comparable control performance of the proposed approach with an advantage of reduced network load.

The study of wireless Sensor Networks (WSN)recently has drawn a lot of attention. Such networks are composed of a large number of tiny wireless sensors, which have limited calculating capability as well as limited power typically supplied by microelectronics hardware. How to prolong network lifetime while maintaining a sufficient sensing area has been one of the most important issues in WSN research. In a high-density environment of sensor networks, excessive working sensors could cause several problems, such as excessive sensing or channel congestion. In this paper, we present a novel approach to partition sensors in the WSN, called node coverage grouping (NCG). Sensors in the connectivity group are within sensing range of each other, and data collected by sensors in a connectivity group are assumed to be similar. In this paper, we also prove that partitioning n nodes by node coverage grouping into connectivity groups is a NP-hard problem. Then we propose a heuristic algorithm of node coverage grouping with the time complexity of O(n3). The experimental results show that the NCG outperforms the LDAS and the PEAS methods in terms of the number of living nodes, the number of working nodes, and the coverage with the proceeding of system lifetime.

In this paper, an implementation of a virtual chat room system for multimedia communications is presented. In addition to the conventional ability of chat room applications to transmit talk messages between users, the system provides a 3-D virtual environment and adopts 3-D facial models for character representation. Each participant can communicate with the others with keyboard, mouse, and his facial expressions. Essential implementation issues including the facial expression analysis process are detailed in this article.