• Proceedings of the KIEE Conference
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• 1999.07g
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• pp.3036-3038
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• 1999

The conventional structure for an action selector of an Autonomous Mobile Robot (AMR) has been criticized for a repeated action. To overcome this problem recently many researches have been focused on the reactive planning systems such as the biological immune system. In this paper, we propose a learning aritificial immune network, the learning method is to use Genetic Algorithm (GA). The computer simulation show that the usefulness of the learning immune network.

• Proceedings of the Korea Information Processing Society Conference
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• 2021.11a
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• pp.1372-1375
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• 2021

최근 산업 현장에서 많은 안전사고가 일어나고 있고, 현장 노동력의 부족으로 무인 로봇 시스템들을 도입하는 등 다양한 변화를 맞이하고 있다. 이에 차세대 자동화 시스템은 보다 유연하고 지능적이어야 한다. AGV(Automatic Guided Vehicle)의 경우 실시간으로 변하는 현장에 대응하기 어렵고, 새로운 어플리케이션에 대한 제품개발의 어려움이 따른다. 이에 대한 대안으로 AGV 인식 스택을 재구축하여 인간과 동일한 공간인식 능력을 갖춘 AMR(Autonomous Mobile Robot)이 대두되고 있다. 본 연구에서는 SLAM과 ROS를 이용하여 AMR의 기능을 구축하였다. YD Lidar 센서와 SLAM을 이용하여 주변 환경을 지도화 하여 로봇의 현재 위치를 파악할 수 있도록 제작하였고, 직접 지도상의 최적 경로를 파악하여 주변 장애물을 회피하며 주행할 수 있음을 확인하였다. DC 모터의 응답 특성에 따라 주행 속도, 조향각 등을 제어할 수 있도록 구현하였다.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.11
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• pp.2083-2089
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• 2007

In this paper addresses the low-level behavior of fuzzy control and the high-level behavior selector for Autonomous Mobile Robots(AMRs) based on a Fuzzy Artificial Immune Network. The sensing information that comes from ultrasonic sensors is the antigen it, and stimulates antibodies. There are many possible combinations of actions between action-patterns and external situations. The question is how to handle the situations to decide the proper action. We propose a fuzzy artificial immune network to solve the above problem. and the computer simulation for an AMR action selector shows the usefulness of the proposed action selector.

• Journal of Drive and Control
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• v.21 no.1
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• pp.46-52
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• 2024

The advancement of autonomous driving technology has heightened the importance of Autonomous Mobile Robotics (AMR) within smart factories. Notably, in tasks involving the transportation of heavy objects, the consideration of weight in route optimization and path planning has become crucial. There is ongoing research on local path planning, such as Dijkstra, A^*, and RRT^*, focusing on minimizing travel time and distance within smart factory warehouses. Additionally, there are ongoing simultaneous studies on route optimization, including TSP algorithms for various path explorations and on minimizing energy consumption in mobile robotics operations. However, previous studies have often overlooked the weight of the objects being transported, emphasizing only minimal travel time or distance. Therefore, this research proposes route planning that accounts for the maximum payload capacity of mobile robotics and offers load-optimized path planning for multi-destination transportation. Considering the load, a genetic algorithm with the objectives of minimizing both travel time and distance, as well as energy consumption is employed. This approach is expected to enhance the efficiency of mobility within smart factories.

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.1216-1218
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• 1996

Genetic algorithm(GA) is useful to find optimal solution without any special mathematical modeling. This study presents to search optimal path of Autonomous Mobile Robot(AMR) by using GA without encoding and decoding procedure. Therefore, this paper shows that the proposed algorithm using GA can reduce the computation time to search the optimal path.

• The Transactions of the Korea Information Processing Society
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• v.2 no.3
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• pp.343-353
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• 1995

The path navigation of autonomous guided vehicle(AGV) or autonomous mobile robot(AMR) assumed that the environment was completely known and the obstacles were fixed. So that, in an environment only partly known or not known at all, the previous works were not successful since the path exploration techniques involved in the work were neither directly applicable nor extensible. In order to improve such problems, this paper was adopted the quadtree technique and proposed the algorithm for an optimal path planning autonomously in an environment and proved a validity through a simulation.

• The Journal of Korea Robotics Society
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• v.17 no.4
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• pp.407-416
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• 2022

Multiple AMRs have been proved to be effective in improving warehouse productivity by eliminating workers' wasteful walking time. Although Multi-agent Path Finding (MAPF)-based solution is an optimal approach for this task, its deployment in practice is challenging mainly due to its imperfect plan-execution capabilities and insufficient computing resources for high-density environments. In this paper, we present a MAPF-based fleet management system architecture that robustly manages multiple robots by re-computing their paths whenever it is necessary. To achieve this, we defined four events that trigger our MAPF solver framework to generate new paths. These paths are then delivered to each AMR through ROS2 message topic. We also optimized a graph structure that effectively captures spatial information of the warehouse. By using this graph structure we can reduce computational burden while keeping its rescheduling functionality. With proposed MAPF-based fleet management system, we can control AMRs without collision or deadlock. We applied our fleet management system to the real logistics warehouse with 10 AMRs and observed that it works without a problem. We also present the usage statistic of adopting AMRs with proposed fleet management system to the warehouse. We show that it is useful over 25% of daily working time.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.5
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• pp.506-512
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• 2000

In this paper, we propose a Fuzzy Classifier System(FCS) makes the classifier system be able to carry out the mapping from continuous inputs to outputs. The FCS is based on the fuzzy controller system combined with machine learning. Therefore the antecedent and consequent of a classifier in FCS are the same as those of a fuzzy rule. In this paper, the FCS modifies input message to fuzzified message and stores those in the message list. The FCS constructs rule-base through matching between messages of message list and classifiers of fuzzy classifier list. The FCS verifies the effectiveness of classifiers using Bucket Brigade algorithm. Also the FCS employs the Genetic Algorithms to generate new rules and modifY rules when performance of the system needs to be improved. Then the FCS finds the set of the effective rules. We will verifY the effectiveness of the poposed FCS by applying it to Autonomous Mobile Robot avoiding the obstacle and reaching the goal.