• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.12
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• pp.4372-4388
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• 2014

Robotic sensor network (RSN) contains mobile sensors and robots providing feasible solution for many multi-agent applications. One of the most critical issues in RSN and its application is how to effectively assign tasks. This paper presents a novel connectivity preserving hybrid task allocation strategy to answer the question particularly for RSN. Firstly, we model the task allocation in RSN to distinguish the discovering and allocating processes. Secondly, a fully distributed simple Task-oriented Unoccupied Neighbor Algorithm, named TUNA, is developed to allocate tasks with only partial view of the network topology. A connectivity controller is finally developed and integrated into the strategy to guarantee the global connectivity of entire RSN, which is critical to most RSN applications. The correctness, efficiency and scalability of TUNA are proved with both theoretical analysis and experimental simulations. The evaluation results show that TUNA can effectively assign tasks to mobile robots with the requirements of only a few messages and small movements of mobile agents.

• The Journal of Korea Robotics Society
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• v.14 no.1
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• pp.40-49
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• 2019

Reinforcement learning has been applied to various problems in robotics. However, it was still hard to train complex robotic manipulation tasks since there is a few models which can be applicable to general tasks. Such general models require a lot of training episodes. In these reasons, deep neural networks which have shown to be good function approximators have not been actively used for robot manipulation task. Recently, some of these challenges are solved by a set of methods, such as Guided Policy Search, which guide or limit search directions while training of a deep neural network based policy model. These frameworks are already applied to a humanoid robot, PR2. However, in robotics, it is not trivial to adjust existing algorithms designed for one robot to another robot. In this paper, we present our implementation of Guided Policy Search to the robotic arms of the Baxter Research Robot. To meet the goals and needs of the project, we build on an existing implementation of Baxter Agent class for the Guided Policy Search algorithm code using the built-in Python interface. This work is expected to play an important role in popularizing robot manipulation reinforcement learning methods on cost-effective robot platforms.

• The Journal of Korea Robotics Society
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• v.12 no.1
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• pp.55-64
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• 2017

This paper proposes combination of a cognitive agent architecture named Soar (State, operator, and result) and ROS (Robot Operating System), which can be a basic framework for a robot agent to interact and cope with its environment more intelligently and appropriately. The proposed Soar-ROS human-robot interaction (HRI) agent understands a set of human's commands by voice recognition and chooses to properly react to the command according to the symbol detected by image recognition, implemented on a humanoid robot. The robotic agent is allowed to refuse to follow an inappropriate command like "go" after it has seen the symbol 'X' which represents that an abnormal or immoral situation has occurred. This simple but meaningful HRI task is successfully experimented on the proposed Soar-ROS platform with a small humanoid robot, which implies that extending the present hybrid platform to artificial moral agent is possible.

• Transactions on Control, Automation and Systems Engineering
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• v.1 no.2
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• pp.147-152
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• 1999

It is an interesting area in the field of artifical intelligence to find an analytic model of cooperative structure for multiagent system accomplishing a given task. Usually it is difficult to design controllers for multi-agent systems without a comprehensive knowledge about the system. One of the way to overcome this limitation is to implement an evolutionary approach to design the controllers. This paper introduces the use of a genetic algorithm to discover a fuzzy logic controller with rules that govern emergent agents solving a pursuit problem in a continuous world. Simulation results indicate that, given the complexity of the problem, an evolutionary approach to find the fuzzy logic controller seems to be promising.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.170-175
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• 1998

Transporting a large table using multiple robotic agents requires at least two group behaviors of homing and herding which are to bo coordinated in a proper sequence. Existing GP methods for multi-agent learning are not practical enough to find an optimal solution in this domain. To evolve this kind of complex cooperative behavior we use a novel method called fitness switching. This method maintains a pool of basis fitness functions each of which corresponds to a primitive group behavior. The basis functions are then progressively combined into more complex fitness functions to co-evolve more complex behavior. The performance of the presented method is compared with that of two conventional methods. Experimental results show that coevolutionary fitness switching provides an effective mechanism for evolving complex emergent behavior which may not be solved by simple genetic programming.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1938-1943
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• 2005

Network human-robot interface is an important research topic. In home application, users access the robotic system directly via voice, gestures or through the network. Users explore a system by using the services provided by this system and to some extend users are enable to participate in a service as partners. A service may be provided by a robot, a group of robots or robots and other network connected systems (distributed sensors, information systems, etc). All these services are done in the network environment, where uncertainty such as the unstable network connection, the availability of the partners in a service, exists. Moreover, these services are controlled by several users, accessing at different time by different methods. Our research aimed at solving this problem to provide a high available level, flexible coordination system. In this paper, a multi-agent framework is proposed. This framework is validated by using our new concept of slave agents, a responsive multi-agent environment, a virtual directory facilitator (VDF), and a task allocation system using contract net protocol. Our system uses a mixed model between distributed and centralized model. It uses a centralized agent management system (AMS) to control the overall system. However, the partners and users may be distributed agents connected to the center through agent communication or centralized at the AMS container using the slave agents to represent the physical agents. The system is able to determine the task allocation for a group of robot working as a team to provide a service. A number of experiments have been conducted successfully in our lab environment using Issac robot, a PDA for user agent and a wireless network system, operated under our multi agent framework control. The experiments show that this framework works well and provides some advantages to existing systems.

• Journal of Institute of Control, Robotics and Systems
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• v.18 no.8
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• pp.758-767
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• 2012

In the ad-hoc wireless network environment, that the fixed sensor nodes and the sensor nodes to move are mixed, as the number of the sensor nodes with mobility are getting more, the costs to recover and maintain the whole network will increase more and more. This paper proposed the CDSR (Cost based Dynamic Source Routing) algorithm being motivated from the typical DSR algorithm, that is one of the reactive routing protocol. The cost function is defined through measuring the cost which any sensor node pays to participate in the whole network for communication. It is also showed in this paper that the proposed routing algorithm will increase the efficiency and life of whole sensor network through a series of experiments.

• Annual Conference of KIPS
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• 2005.05a
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• pp.461-464
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• 2005

본 논문에서는 실시간 반응형 로봇의 행위를 지정하기에 적합한 에이전트 언어를 소개한다. 기존의 BDI 기반의 에이전트 언어를 기반으로 실시간 반응형 로봇의 행위를 지정하는데 적합하도록 개발한 VivAce 에이전트 구조에 대해서 설명하고 이를 이용한 간단한 시뮬레이션을 수행하였다. 또한 VivAce 가 기존의 BDI 에이전트 언어에 비해서 가지는 새로운 특징인 자바 네이티브 언어 지원, 쓰레드 기반의 계획 실행, 다양한 인터페이스를 소개한다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.4
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• pp.333-338
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• 2005

This paper presents the target object search algorithm under Dynamic Programming (DP) in the Tree-type maze. We organized an experimental environment with the concatenation of Y-shape diverged way, small mobile robot, and a target object. By the principle of optimality, the backbone of DP, an agent recognizes that a given whole problem can be solved whether the values of the best solution of certain ancillary problem can be determined according to the principle of optimality. In experiment, we used two different control algorithms: a left-handed method and DP. Finally we verified the efficiency of DP in the practical application using our real robot.

• Korean Journal of Head & Neck Oncology
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• v.38 no.2
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• pp.7-13
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• 2022

Human papillomavirus (HPV) is a causative agent for a subset of oropharyngeal cancer (OPC). The current standard of care (SOC) for locally advanced OPC is 70 Gy definitive radiotherapy (RT) concurrent with cisplatin, which entails significant proportions of acute and late grade 3 or higher toxicities. Accordingly, discovery of favorable prognosis of HPV-related OPC has led to enthusiasm to attenuate subspecialties therapy in multidisciplinary treatment. Diverse deintensification strategies were investigated in multiple phase 2 trials with an assumption that attenuated treatments result in comparable oncologic outcome and less toxicities compared with SOC. Several trials on chemotherapy deintensification revealed that concomitant administration of cisplatin is not to be omitted or substituted for cetuximab without compromising progression-free survival or local control. A transoral robotic surgery (TORS) is investigated as alternative local treatment, but TORS plus SOC or mild deintensified adjuvant RT showed similar toxicities and inferior oncologic outcomes compared with SOC definitive RT or moderately deintensified RT. However, it has been reported that TORS plus deintensified 30-36 Gy adjuvant RT results in excellent outcome and less late toxicity compared with SOC adjuvant RT. Several phase 2 trials reported apparently equivalent progression-free survival and local control and similar adverse effects with moderately deintensified 60 Gy RT compared with SOC 70 Gy RT. Further dose reduction below 60 Gy has been investigated using biology-directed approaches, which use response to induction chemotherapy or metabolic images to triage HPV-positive OPC for deintensified RT. In summary, these trials provide valuable insights for future directions. Available evidence consistently showed that moderately deintensified RT is effective and safe for HPV-positive OPC in both definitive and adjuvant settings. Concurrent cisplatin remains an essential component without which progression-free survival is significantly compromised for advanced HPV-positive OPC. A simple incorporation of TORS to SOC may be detrimental for oncologic outcome without anticipated toxicity reduction. Given the lack of level 1 evidence, it is prudent to curb an unjustified deviation from the current SOC and limit any deintensified strategies to clinical trials and adhere to the current SOC.