We consider the throughput-maximization problem for both up- and downlink in the wireless network. For the purpose, we design an iterative and distributive uplink algorithm based on Lagrangian relaxation. Using the Lagrange multipliers and the network duality, we make throughput-maximization in the downlink. Our analysis and computational tests assume that channels are symmetric between up- and downlink. The network duality we proved here is a generalized version of previous researches by Jindal et al. and Catrein et al. Computational test shows that the performance of up- and downlink throughput for our algorithms is close to the optimal value for the channel orthogonality factor between 0.5 and 1. In particular, our duality-based approach gives 97-98% throughput of the optimal uplink algorithm proposed by Kumaran and Quian, and a downlink heuristic algorithm (MPA-1) proposed by Mo and Kim, when the channel orthogonality factor is a value between 0.5 and 1. On the other hand, when channels are rather orthogonal (between 0 and 0.5), we have observed some throughput degradation in the downlink, which is about 86% of MPA-1. Considering the complexity of the existing algorithms, we conclude that these results are quite encouraging in terms of both performance and practical applicability of the generalized duality theorem.

Proposes a hybrid control architecture which combines the key features of the two well-known robot control architectures; hierarchical and behavioral-based. The overall control architecture consists of three layers, i.e. the high-level planner, the middle plan executor, and the lower level monitor and behavior-based controller. In the planned task, only one behavior module is chosen by the logical coordinator in the plan executor according to the way point bin. In the exceptional situation, the central controller in the plan executor issues an additional control command to reach the planned way point. Several simulations and experiments with an autonomous mobile robot show that the proposed architecture enables the robot controller to achieve the multiple sequential goals even in a dynamic and uncertain environment.

Focusing on the manipulation tasks to be executed by humanoid robots, principal requirements which are to be satisfied by hardware/software of the control system are considered. In order to meet the requirements, a novel type of hardware structure and software architecture is proposed. Since the target humanoid robot consists of multiple subsystems such as a central controller for brain, a vision controller for eye, and five motion sub-controllers for two arms, two hands, one spine, the on-board hardware control system is designed to have a distributed control structure connected by pseudo real-time Ethernet interfaces. A goal-achieving software architecture is also proposed which meets the requirements of semi-autonomy, reactivity, expandability, and object-orientedness. Specifically, in order to achieve reactivity, a coordination method is proposed to configure three kinds of executive modules, primitive module, flow-control module, and goal module, which have multiple exit states. The control architecture proposed has been implemented for performing toy-block assembly tasks on a humanoid robot as well as on the graphic simulator.

An object-oriented approach to behavior-based control architecture for autonomous mobile robots as presented. Instead of traditional implementations of the architecture with proprietary programming languages which resulted in slow execution speed and poor hardware compatibility, the C++ language is used and an optimal scheduling algorithm is proposed for fast and guaranteed response time. Each behavior module is compiled separately and loaded at run-time using dynamic binding to improve modularity and expandability. A vector valued representation of output signals is also proposed to arbitrate conflicting signals properly without losing any information. The proposed method is implemented and applied to control a laboratory developed autonomous mobile robot and tested in real-time.