• Journal of Institute of Control, Robotics and Systems
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• v.22 no.9
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• pp.693-698
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• 2016

A decentralized control method is proposed to enable a group of robots to achieve maximum performance in multisensory target tracking while avoiding collision with the target. The decentralized control was designed based on navigation function formalism. The study showed that the multiple agent system converged to the positions providing the maximum performance by the decentralized controller, based on Lyapunov and Hessian theory. An exemplary simulation was given for a multiple agent system tracking a stationary target.

• Proceedings of the KIEE Conference
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• 2005.07d
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• pp.2780-2782
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• 2005

In this paper, we present a mobile robot localization solution by using a Ubiquitous Vision System (UVS). The collective information gathered by multiple cameras that are strategically placed has many advantages. For example, aggregation of information from multiple viewpoints reduces the uncertainty about the robots' positions. We construct UVS as a multi-agent system by regarding each vision sensor as one vision agent (VA). Each VA performs target segmentation by color and motion information as well as visual tracking for multiple objects. Our modified identified contractnet (ICN) protocol is used for communication between VAs to coordinate multitask. This protocol raises scalability and modularity of thesystem because of independent number of VAs and needless calibration. Furthermore, the handover between VAs by using ICN is seamless. Experimental results show the robustness of the solution with respect to a widespread area. The performance in indoor environments shows the feasibility of the proposed solution in real-time.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.10
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• pp.930-939
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• 2004

In this paper, both dynamic constraints and kinematic constraints are considered for the analysis of manipulability of robotic systems comprised of multiple cooperating arms. Given bounds on the torques of each Joint actuator for every robot, the purpose of this study is to drive the bounds of task-space acceleration of object carried by the system. Bounds on each joint torque, described as a polytope, is transformed to the task-space acceleration through matrices related with robot dynamics, robot kinematics, object dynamics, grasp conditions, and contact conditions. A series of mathematical manipulations including the procedure calculating minimum infinite-norm solution of linear equation is applied to get the reachable acceleration bounds from given actuator dynamic constrains. Several examples including two robot systems as well as three robot system are shown with the assumptions of complete-constraint contact model(or' very soft contact') and insufficient or proper degree of freedom robot.

• Journal of Mechanical Science and Technology
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• v.19 no.8
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• pp.1554-1567
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• 2005

When multiple robotics systems with several sub-chains grasp a common object, the inherent force redundancy provides a chance of utilizing internal loading. Analysis of grasping space based internal loading is proposed in this work since this method facilitates understanding the physical meaning of internal loadings in some applications, as compared to usual operational space based approach. Investigation of the internal loading for a triple manipulator has been few as ,compared to a dual manipulator. In this paper, types of the internal loading for dual and triple manipulator systems are investigated by using the reduced row echelon method to analyze the null space of those systems. No internal loading condition is derived and several load distribution schemes are compared through simulation. Furthermore, it is shown that the proposed scheme based on grasping space is applicable to analysis of special cases such as three-fingered and three-legged robots having a point contact with the grasped object or ground.

• Transactions on Control, Automation and Systems Engineering
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• v.4 no.1
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• pp.49-55
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• 2002

A decrease in the birthrate and aging are progressing in Japan and several countries. In that society, it is important that physically weak persons such as elderly persons are able to take care of themselves. We have been developing exoskeletal robots for human (especially for physically weak persons) motion support. In this study, the controller controls the angular position and impedance of the exoskeltal robot system using multiple fuzzy-neuro controllers based on biological signals that reflect the human subject's intention. Skin surface electromyogram (EMG) signals and the generated wrist force by the human subject during the elbow motion have been used as input information of the controller. Since the activation level of working muscles tends to vary in accordance with the flexion angle of elbow, multiple fuzzy-neuro controllers are applied in the proposed method. The multiple fuzzy-neuro controllers are moderately switched in accordance with the elbow flexion angle. Because of the adaptation ability of the fuzzy-neuro controllers, the exoskeletal robot is flexible enough to deal with biological signal such as EMG. The experimental results show the effectiveness of the proposed controller.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.176-180
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• 1998

We may gaze at some peculiar scenes of flocking of birds and fishes. This paper demonstrates that multiple agent mobile robots show complex behaviors from efficient and strategic rules. The simulated flock are realized by a distributed behavioral model and each mobile robot decides its own motion as an individual which moves constantly by sensing the dynamic environment.

• Proceedings of the KIEE Conference
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• 2004.07d
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• pp.2411-2413
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• 2004

In this paer, we propose a method to apply a decentralized control algorithm for passive velocity field control using virtual flywheel system to cooperative mobile robots. The considered system convey a common rigid object in a horizontal plain. The effectiveness of proposed control algorithm is examined by numerical simulation for cooperative tasks.

• Proceedings of the Safety Management and Science Conference
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• 1999.11a
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• pp.537-540
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• 1999

We propose a decision model to incorporates the values assigned by a group of experts on different factors in selecting robots. Using this model, SN ratio of taguchi method for each of subjective factors as well as values of weights are used in this comprehensive method for robot selection. A numerical example is presented to illustrate the model and to show a rank reversal when compared to a model that does not eliminate extreme values and eliminates the highest and lowest experts' values allocating the weights and the subjective factors.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.16 no.4
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• pp.308-317
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• 2016

Multiple evidences based decision making is an important functionality for computers and robots. To combine multiple evidences, mathematical theory of evidence has been developed, and it involves the most vital part called Dempster's rule of combination. The rule is used for combining multiple evidences. However, the combined result gives a counterintuitive conclusion when highly conflicting evidences exist. In particular, when we obtain two different sources of evidence for a single hypothesis, only one of the sources may contain evidence. In this paper, we introduce a modified combination rule based on the partial conflict measurement by using an absolute difference between two evidences' basic probability numbers. The basic probability number is described in details in Section 2 "Mathematical Theory of Evidence". As a result, the proposed combination rule outperforms Dempster's rule of combination. More precisely, the modified combination rule provides a reasonable conclusion when combining highly conflicting evidences and shows similar results with Dempster's rule of combination in the case of the both sources of evidence are not conflicting. In addition, when obtained evidences contain multiple hypotheses, our proposed combination rule shows more logically acceptable results in compared with the results of Dempster's rule.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.11
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• pp.5496-5521
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• 2018

Object detection and tracking is the basic capability of mobile robots to achieve natural human-robot interaction. In this paper, an object tracking system of mobile robot is designed and validated using improved multiple instance learning algorithm. The improved multiple instance learning algorithm which prevents model drift significantly. Secondly, in order to improve the capability of classifiers, an active sample selection strategy is proposed by optimizing a bag Fisher information function instead of the bag likelihood function, which dynamically chooses most discriminative samples for classifier training. Furthermore, we integrate the co-training criterion into algorithm to update the appearance model accurately and avoid error accumulation. Finally, we evaluate our system on challenging sequences and an indoor environment in a laboratory. And the experiment results demonstrate that the proposed methods can stably and robustly track moving object.