• KSII Transactions on Internet and Information Systems (TIIS)
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• v.15 no.1
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• pp.216-239
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• 2021

The present work addresses the challenging problem of coordinating power allocation with interference management in multi-robot networks by applying the promising expansion capabilities of multiple-input multiple-output (MIMO) and full duplex systems, which achieves it for maximizing the throughput of networks under the impacts of Doppler frequency shifts and external jamming. The proposed power allocation with interference coordination formulation accounts for three types of the interference, including cross-tier, co-tier, and mixed-tier interference signals with cluster head nodes operating in different full-duplex modes, and their signal-to-noise-ratios are respectively derived under the impacts of Doppler frequency shifts and external jamming. In addition, various optimization algorithms, including two centralized iterative optimization algorithms and three decentralized optimization algorithms, are applied for solving the complex and non-convex combinatorial optimization problem associated with the power allocation and interference coordination. Simulation results demonstrate that the overall network throughput increases gradually to some degree with increasing numbers of MIMO antennas. In addition, increasing the number of clusters to a certain extent increases the overall network throughput, although internal interference becomes a severe problem for further increases in the number of clusters. Accordingly, applications of multi-robot networks require that a balance should be preserved between robot deployment density and communication capacity.

• Journal of Communications and Networks
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• v.9 no.3
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• pp.296-311
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• 2007

Mobile multi-robot teams are useful in many critical applications such as search and rescue. Explicit communication among robots in such mobile multi-robot teams is useful for the coordination of such teams as well as exchanging data. Since many applications for mobile robots involve scenarios in which communication infrastructure may be damaged or unavailable, mobile robot teams frequently need to communicate with each other via ad hoc networking. In such scenarios, low-overhead and energy-efficient routing protocols for delivering messages among robots are a key requirement. Two important primitives for communication are essential for enabling a wide variety of mobile robot applications. First, unicast communication (between two robots) needs to be provided to enable coordination and data exchange. Second, in many applications, group communication is required for flexible control, organization, and management of the mobile robots. Multicast provides a bandwidth-efficient communication method between a source and a group of robots. In this paper, we first propose and evaluate two unicast routing protocols tailored for use in ad hoc networks formed by mobile multi-robot teams: Mobile robot distance vector (MRDV) and mobile robot source routing (MRSR). Both protocols exploit the unique mobility characteristics of mobile robot networks to perform efficient routing. Our simulation study show that both MRDV and MRSR incur lower overhead while operating in mobile robot networks when compared to traditional mobile ad hoc network routing protocols such as DSR and AODV. We then propose and evaluate an efficient multicast protocol mobile robot mesh multicast (MRMM) for deployment in mobile robot networks. MRMM exploits the fact that mobile robots know what velocity they are instructed to move at and for what distance in building a long lifetime sparse mesh for group communication that is more efficient. Our results show that MRMM provides an efficient group communication mechanism that can potentially be used in many mobile robot application scenarios.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.25 no.9
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• pp.50-55
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• 2011

This research is to apply the adaptive control of neuron networks for the real-time attitude control of Multi-articulated robot. Multi-articulated robot is expressed with a complicated mathematical model on account of the mechanic, electric non-linearity which each articulation of mechanism has, and includes an unstable factor in time of attitude control. If such a complex expression is included in control operation, it leads to the disadvantage that operation time is lengthened. Thus, if the rapid change of the load or the disturbance is given, it is difficult to fulfill the control of desired performance. In this research we used the response property curve of the robot instead of the activation function of neural network algorithms, so the adaptive control system of neural networks constructed without the information of modeling can perform a real-time control. The proposed adaptive control algorithm generated control signs corresponding to the non-linearity of Multi-articulated robot, which could generate desired motion in real time.

• Proceedings of the KIPE Conference
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• 2007.07a
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• pp.491-493
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• 2007

This paper is suggesting the method to embody Multi-Axes robot controller by using CAN which has been the most popular industrial networks. The robot controller guarantees the efficiency and reliability by using CAN as a communication tool between upper robot control parts and lower control parts.

• Proceedings of the KIEE Conference
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• 2003.11c
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• pp.657-660
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• 2003

Control of multi-link robot arms is a very difficult problem because of the highly nonlinear dynamics. Decentralized control scheme is developed for control of robot manipulators based on RBF(Radial Basis Function) Neural Networks. RBF Neural Networks is used to approximate the coupling forces among the joints, coriolis force, centrifugal force, gravitational force, and frictional force. The compensation controller is also proposed to estimate the bound of approximation error so that the chattering effect of the control effort can be reduced. The proposed scheme does not require an accurate manipulator dynamic, and it is proved that closed-loop system is asymptotic stable despite the gross robot parameter variations. Numerical simulations for two-link robot manipulator are included to show the effectiveness of controller.

• Journal of Computing Science and Engineering
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• v.2 no.4
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• pp.321-338
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• 2008

This paper presents a Modified Multiple Depth First Search algorithm for the exploration of the indoor environments occupied with obstacles in random distribution. The proposed algorithm was designed and implemented to employ one or a team of Khepera II mini robots for the exploration process. In case of multi-robots, the BlueCore2 External Bluetooth module was used to establish wireless networks with one master robot and one up to three slaves. Messages are sent and received via the module's Universal Asynchronous Receiver/Transmitter (UART) interface. Real exploration experiments were performed using locally developed teleworkbench with various autonomy features. In addition, computer simulation tool was also developed to simulate the exploration experiments with one master robot and one up to ten slaves. Computer simulations were in good agreement with the real experiments for the considered cases of one to one up to three networks. Results of the MMDFS for single robot exhibited 46% reduction in the needed number of steps for exploring environments with obstacles in comparison with other algorithms, namely the Ants algorithm and the original MDFS algorithm. This reduction reaches 71% whenever exploring open areas. Finally, results performed using multi-robots exhibited more reduction in the needed number of exploration steps.

• Journal of Institute of Control, Robotics and Systems
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• v.5 no.1
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• pp.95-104
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• 1999

In this paper we propose a fast and precise control algorithm for a mobile robot, which aims at the self-tuning control applying two multi-layered neural networks to the structure of computed torque method. Through this algorithm, the nonlinear terms of external disturbance caused by variable task environments and dynamic model errors are estimated and compensated in real time by a long term neural network which has long learning period to extract the non-linearity globally. A short term neural network which has short teaming period is also used for determining optimal gains of PID compensator in order to come over the high frequency disturbance which is not known a priori, as well as to maintain the stability. To justify the global effectiveness of this algorithm where each of the long term and short term neural networks has its own functions, simulations are peformed. This algorithm can also be utilized to come over the serious shortcoming of neural networks, i.e., inefficiency in real time.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.26 no.10
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• pp.89-94
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• 2012

This research is to apply the control of neuron networks for the real-time walking control of Multi-articulated robot. Multi-articulated robot is expressed with a complicated mathematical model on account of the mechanic, electric non-linearity which each articulation of mechanism has, and includes an unstable factor in time of walking control. If such a complex expression is included in control operation, it leads to the disadvantage that operation time is lengthened. Thus, if the rapid change of the load or the disturbance is given, it is difficult to fulfill the control of desired performance. This paper proposes a new mode to implement a neural network controller by installing a real object for controlling and an algorithm for this, which can replace the existing method of implementing a neural network controller by utilizing activation function at the output node. The proposed control algorithm generated control signs corresponding to the non-linearity of Multi-articulated robot, which could generate desired motion in real time.

• ETRI Journal
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• v.43 no.2
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• pp.221-231
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• 2021

To resolve energy depletion issues in massive Internet of Things sensor networks, we developed a set of distributed energy beamforming methods with one-bit feedback and clustering for multi-node wireless energy transfer, where multiple singleantenna distributed energy transmitters (Txs) transfer their energy to multiple nodes wirelessly. Unlike previous works focusing on distributed information beamforming using a single energy receiver (Rx) node, we developed a distributed energy beamforming method for multiple Rx nodes. Additionally, we propose two clustering methods in which each Tx node chooses a suitable Rx node. Furthermore, we propose a fast distributed beamforming method based on Tx sub-clustering. Through computer simulations, we demonstrate that the proposed distributed beamforming method makes it possible to transfer wireless energy to massive numbers of sensors effectively and rapidly with small implementation complexity. We also analyze the energy harvesting outage probability of the proposed beamforming method, which provides insights into the design of wireless energy transfer networks with distributed beamforming.

• International Journal of Precision Engineering and Manufacturing
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• v.9 no.2
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• pp.69-74
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• 2008

Robotics and artificial intelligence are components of IT that involve networks, electrical and electronic engineering, and wireless communication. We consider an algorithm for efficient navigation by building a precise map in a multi-robot system under conditions of limited and unlimited communications. The basis of the navigation algorithm described in this paper is a wave algorithm, which is effective in obtaining an accurate map. Each robot in a multi-robot system has its own task such as building a map for its local position. By combining their data into a shared map, the robots can actively seek to verify their relative locations. Using shared maps, they coordinate their exploration strategies to maximize exploration efficiency. To prove the efficiency of the proposed technique, we compared the final results with the results in $Burgard^{8}$ and $Stachniss.^{9-10}$ All of the simulation comparisons, which are shown as graphs, were made in four different environments.