• 디지털산업정보학회논문지
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• 제17권3호
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• pp.27-37
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• 2021

Highly informative heuristics in AI planning can help to a more efficient search a solutions. However, in general, to obtain informative heuristics from planning problem specifications requires a lot of computational effort. To address this problem, we propose a Partial Planning Graph(PPG) and Mixed Heuristics for solving planning problems more efficiently. The PPG is an improved graph to be applied to can find a partial heuristic value for each goal condition from the relaxed planning graph which is a means to get heuristics to solve planning problems. Mixed Heuristics using PPG requires size of each graph is relatively small and less computational effort as a partial plan generated for each goal condition compared to the existing planning graph. Mixed Heuristics using PPG can find partial interactions for each goal conditions in an effective way, then consider them in order to estimate the goal state heuristics. Therefore Mixed Heuristics can not only find interactions for each goal conditions more less computational effort, but also have high accuracy of heuristics than the existing max and additive heuristics. In this paper, we present the PPG and the algorithm for computing Mixed Heuristics, and then explain analysis to accuracy and the efficiency of the Mixed Heuristics.

• Industrial Engineering and Management Systems
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• 제9권3호
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• pp.227-241
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• 2010

A memory module industry's supply chain usually consists of multiple manufacturing sites and multiple distribution centers. In order to fulfill the variety of demands from downstream customers, production planners need not only to decide the order allocation among multiple manufacturing sites but also to consider memory module industrial characteristics and supply chain constraints, such as multiple material substitution relationships, capacity, and transportation lead time, fluctuation of component purchasing prices and available supply quantities of critical materials (e.g., DRAM, chip), based on human experience. In this research, a directed graph-based supply network planning (DGSNP) model is developed for memory module industry. In addition to multi-site order allocation, the DGSNP model explicitly considers production planning for each manufacturing site, and purchasing planning from each supplier. First, the research formulates the supply network's structure and constraints in a directed-graph form. Then, a proposed genetic algorithm (GA) solves the matrix form which is transformed from the directed-graph model. Finally, the final matrix, with a calculated maximum profit, can be transformed back to a directed-graph based supply network plan as a reference for planners. The results of the illustrative experiments show that the DGSNP model, compared to current memory module industry practices, determines a convincing supply network planning solution, as measured by total profit.

• 한국전자통신학회논문지
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• 제18권4호
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• pp.709-714
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• 2023

유연한 planning system은 로봇이 다양한 임무를 수행하기 위해서 중요한 요소이다. 본 논문에서는 변화하는 환경에 대응할 수 있는 automated planning system architecture를 소개한다. 심볼릭 기반의 task planning을 위해 PDDL을 활용하였으며 실시간 환경 정보 업데이트를 위해 그래프 데이터베이스를 이용한다. 제안한 구조는 시나리오 기반 실험을 통해 검증하였다.

• 로봇학회논문지
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• 제19권2호
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• pp.196-202
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• 2024

The ability to understand given environments and plan a sequence of actions leading to goal state is crucial for personal service robots. With recent advancements in deep learning, numerous studies have proposed methods for state representation in planning. However, previous works lack explicit information about relationships between objects when the state observation is converted to a single visual embedding containing all state information. In this paper, we introduce graph-based state representation that incorporates both object and relationship features. To leverage these advantages in addressing the task planning problem, we propose a Graph Neural Network (GNN)-based subgoal prediction model. This model can extract rich information about object and their interconnected relationships from given state graph. Moreover, a search-based algorithm is integrated with pre-trained subgoal prediction model and state transition module to explore diverse states and find proper sequence of subgoals. The proposed method is trained with synthetic task dataset collected in simulation environment, demonstrating a higher success rate with fewer additional searches compared to baseline methods.

• 정보처리학회논문지B
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• 제18B권1호
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• pp.29-38
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• 2011

계획 문제 명세로부터 영역-독립적인 휴리스틱을 유도해내기 위해서는 주어진 계획문제에 대한 간략화와 간략화된 계획문제에 대한 해 도출 과정이 요구된다. 본 논문에서는 초기 상태의 불확실성과 비결정적 동작 효과를 모두 포함한 조건부 계획문제를 풀기 위한 새로운 융합 계획그래프와 이것을 이용한 GD 휴리스틱 계산법을 소개한다. 융합 계획그래프는 고전적 계획 문제 풀이를 위한 휴리스틱 계산에 이용되는 간략화된 계획그래프를 조건부 계획문제에 적용할 수 있도록 확장한 자료구조이다. 융합 계획그래프에서는 감지 동작과 비결정적 동작들을 포함한 조건부 계획 문제에 대한 휴리스틱을 얻기 위해, 전통적인 삭제 간략화외에도 감지 동작과 비결정적 동작들에 대한 효과-융합 간략화를 추가로 이용한다. 융합 계획 그래프의 전향 확장과 병행적으로 진행되는 GD 휴리스틱 계산에서는 목표조건들 간의 상호 의존성을 분석하여 전체 목표 집합에 대한 최소 도달비용을 추정할 때 불필요한 중복성을 배제한다. 따라서 GD 휴리스틱은 기존의 겹침 휴리스틱보다 더 적은 계산시간 을 요구하면서도, 최대 휴리스틱이나 합산 휴리스틱보다 더 높은 정보력을 가진다는 장점이 있다. 본 논문에서는 GD 휴리스틱의 정확성과 탐색 효율성을 확인하기 위한 실험적 분석에 대해 설명한다.

• 디지털산업정보학회논문지
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• 제13권2호
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• pp.27-34
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• 2017

Highly informative admissible heuristics can help to conduct more efficient search for optimal solutions. However, in general, more informative ones of heuristics from planning problems requires lots of computational effort. To address this problem, we propose an Delete Relaxation based Action Costs-based Planning Graph(ACPG) and Action Costs-based Heuristics for solving optimal planning problems more efficiently. The ACPG is an extended one to be applied to can find action costs between subgoal & goal conditions from the Relaxed Planning Graph(RPG) which is a common means to get heuristics for solving the planning problems, Action Costs-based Heuristics utilizing ACPG can find action costs difference between subgoal & goal conditions in an effective way, and then consider them to estimate the goal distance. In this paper, we present the heuristics algorithm to compute Action Costs-based Heuristics, and then explain experimental analysis to investigate the efficiency and the accuracy of the Action Costs-based Heuristics.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.120.3-120
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• 2001

We propose a new method of path planning for cleaning robots. Path planning problem for cleaning robots is different from conventional path planning problems in which finding a collision-free trajectory from a start point to a goal point is focused. In the case of cleaning robots, however, a planned path should cover all area to be cleaned. To resolve this problem in a systematic way, we propose a method based on a graph model as follows: at first, partition a given map into proper regions, then transform a divided region to a vertex and a connectivity between regions to an edge of a graph. Finally, a region is divided into sub-regions so that the graph has a unary tree which is the simplest Hamilton path. The effectiveness of the proposed method is shown by computer simulation results.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.418-418
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• 2000

This paper proposes a path planning algorithm for mobile robot. To generate an optimal path and minimum time path for a mobile robot, we use the Genetic Algorithm(GA) and Visibility Graph. After finding a minimum-distance between start and goal point, the path is revised to find the minimum time path by path-smoothing algorithm. Simulation results show that the proposed algorithms are more effective.

• 대한전기학회논문지:시스템및제어부문D
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• 제51권12호
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• pp.577-582
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• 2002

The objective of this paper is to produce a weighted graph map for path-planning of an autonomous mobile robot(AMR) based on the measurements from a single ultrasonic sensor, which are acquired when the autonomous mobile robot explores unknown indoor circumstance. The AMR navigates in th unknown space by following the wall and gathers the range data using the ultrasonic sensor, from which the occupancy grid map is constructed by associating the range data with occupancy certainties. Then, the occupancy grid map is converted to a weighted graph map suing morphological image processing and thinning algorithms. the path- planning for autonomous navigation of a mobile robot can be carried out based on the occupancy grid map. These procedures are implemented and tested using an AMR, and primary results are presented in this paper.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년 학술대회 논문집 정보 및 제어부문
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• pp.184-186
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• 2006

We propose a new method for shortest path planning of mobile robots. The topological information of the graph is obtained by thinning method to generate the collision-free path of robot. And the travelling path is determined through hierarchical planning stages. The first stage generates an initial path by use of Dijkstra's algorithm. The second stage then generates the final path by use of dynamic programming (DP). The DP adjusts the intial path to reduce the total travelling distance of robot. Simulation results are presented to verify the performance of the proposed method.