• Journal of Information Processing Systems
• /
• 제16권6호
• /
• pp.1324-1342
• /
• 2020

Various modified algorithms of rapidly-exploring random tree (RRT) have been previously proposed. However, compared to the RRT algorithm for collision avoidance with global and static obstacles, it is not easy to find a collision avoidance and local path re-planning algorithm for dynamic obstacles based on the RRT algorithm. In this study, we propose boundary-RRT*, a novel-algorithm that can be applied to aerial vehicles for collision avoidance and path re-planning in a three-dimensional environment. The algorithm not only bounds the configuration space, but it also includes an implicit bias for the bounded configuration space. Therefore, it can create a path with a natural curvature without defining a bias function. Furthermore, the exploring space is reduced to a half-torus by combining it with simple right-of-way rules. When defining the distance as a cost, the proposed algorithm through numerical analysis shows that the standard deviation (σ) approaches 0 as the number of samples per unit time increases and the length of epsilon ε (maximum length of an edge in the tree) decreases. This means that a stable waypoint list can be generated using the proposed algorithm. Therefore, by increasing real-time performance through simple calculation and the boundary of the configuration space, the algorithm proved to be suitable for collision avoidance of aerial vehicles and replanning of local paths.

• 로봇학회논문지
• /
• 제16권3호
• /
• pp.250-259
• /
• 2021

Path planning is one of the important technologies for automated parking. It requires to plan a collision-free path considering the vehicle's kinematic constraints such as minimum turning radius or steering velocity. In a complex parking lot, Rapidly-exploring Random Tree^* (RRT^*) can be used for planning a parking path, and Reeds-Shepp or Hybrid Curvature can be applied as a tree-extension method to consider the vehicle's constraints. In this case, each of these methods may affect the computation time of planning the parking path, path-tracking error, and parking success rate. Therefore, in this study, we conduct comparative analysis of two tree-extension functions: Reeds-Shepp (RS) and Hybrid Curvature (HC), and show that HC is a more appropriate tree-extension function for parking path planning. The differences between the two functions are introduced, and their performances are compared by applying them with RRT^*. They are tested at various parking scenarios in simulation, and their advantages and disadvantages are discussed by computation time, cross-track error while tracking the path, parking success rate, and alignment error at the target parking spot. These results show that HC generates the parking path that an autonomous vehicle can track without collisions and HC allows the vehicle to park with lower alignment error than those of RS.

• 산업공학
• /
• 제8권3호
• /
• pp.221-230
• /
• 1995

Computer Aided Process Planning(CAPP) has been emerged as playing a key role in Computer Integrated Manufacturing(CIM) as the most critical link to integrate CAD and CAM, and therefore much effort has been dedicated to the structure and creation of CAPP system. In this research, a modified variant CAPP system based on process planning rule base is developed, which generates process plans for parts automatically where GT code data are provided as input. In order to execute process planning, rules are constructed in the form of Decision Tree and this system has the inference engine that extracts the results of process planning on the basis of tree-structured rules which are concerned with manufacturing processes.

• 한국환경복원기술학회지
• /
• 제23권6호
• /
• pp.125-135
• /
• 2020

The uncontrolled urban expansion causes various social, economic problems and natural/environmental problems. Therefore, it is necessary to forecast urban expansion by identifying various factors related to urban expansion. This study aims to forecast it using a decision tree that is widely used in various areas. The study used geographic data such as the area of use, geographical data like elevation and slope, the environmental conservation value assessment map, and population density data for 2006 and 2018. It extracted the new urban expansion areas by comparing the residential, industrial, and commercial zones of the zoning in 2006 and 2018 and derived a decision tree using the 2006 data as independent variables. It is intended to forecast urban expansion in 2030 by applying the data for 2018 to the derived decision tree. The analysis result confirmed that the distance from the green area, the elevation, the grade of the environmental conservation value assessment map, and the distance from the industrial area were important factors in forecasting the urban area expansion. The AUC of 0.95051 showed excellent explanatory power in the ROC analysis performed to verify the accuracy. However, the forecast of the urban area expansion for 2018 using the decision tree was 15,459.98㎢, which was significantly different from the actual urban area of 4,144.93㎢ for 2018. Since many regions use decision tree to forecast urban expansion, they can be useful for identifying which factors affect urban expansion, although they are not suitable for forecasting the expansion of urban region in detail. Identifying such important factors for urban expansion is expected to provide information that can be used in future land, urban, and environmental planning.

• 유통과학연구
• /
• 제13권6호
• /
• pp.11-15
• /
• 2015

Purpose - Robot path planning, a constrained optimization problem, has been an active research area with many methods developed to tackle it. This study proposes the use of a Rapidly-exploring Random Tree and Particle Swarm Optimizer algorithm for path planning. Research design, data, and methodology - The grid method is built to describe the working space of the mobile robot, then the Rapidly-exploring Random Tree algorithm is applied to obtain the global navigation path and the Particle Swarm Optimizer algorithm is adopted to obtain the best path. Results - Computer experiment results demonstrate that this novel algorithm can rapidly plan an optimal path in a cluttered environment. Successful obstacle avoidance is achieved, the model is robust, and performs reliably. The effectiveness and efficiency of the proposed algorithm is demonstrated through simulation studies. Conclusions - The findings could provide insights to the validity and practicability of the method. This method makes it is easy to build a model and meet real-time demand for mobile robot navigation with a simple algorithm, which results in a certain practical value for distribution environments.

• 제어로봇시스템학회논문지
• /
• 제21권3호
• /
• pp.257-264
• /
• 2015

This paper proposes an adaptive RRT (Rapidly-exploring Random Tree) for path planning of high DOF multibody robotic system. For an efficient path planning in high-dimensional configuration space, the proposed algorithm adaptively selects the robot bodies depending on the complexity of path planning. Then, the RRT grows only using the DOFs corresponding with the selected bodies. Since the RRT is extended in the configuration space with adaptive dimensionality, the RRT can grow in the lower dimensional configuration space. Thus the adaptive RRT method executes a faster path planning and smaller DOF for a robot. We implement our algorithm for path planning of 19 DOF robot, AMIRO. The results from our simulations show that the adaptive RRT-based path planner is more efficient than the basic RRT-based path planner.

• 한국환경과학회지
• /
• 제20권7호
• /
• pp.881-890
• /
• 2011

In this study, thermal environment changes for a marathon course of IAAF World Championship, Daegu 2011 were modeled to provide improvements of thermal environment, so that runners could have the maximum condition and citizens pleasant streets. The three biggest size of intersections were selected for the study. Envi-met, 3G microclimate model, were used for a thermal environment analysis and three different cases - present status, planting roadside tree scenario, and roof-garden scenario - were compared. The followings are the results of the study. 1. The highest thermal distribution were shown at 1 p.m., but there was no significant difference between a thermal distribution at 1 p.m. and that at 5 p.m. since a heat flux from buildings affects thermal distributions rather than insolation does. 2. Tree planting or adding environmental friendly factors might lead a temperature drop effect, but the effect was not significant for areas covered with impermeability packing materials such as concrete or asphalt (especally, for Site case 2) 3. The combination of tree planting and adding environmental friendly factors also brought a temperature drop effect (Site 1 and 2) and this case showed even better result if green spaces (especially, parks) were closed.

• 농촌계획
• /
• 제17권3호
• /
• pp.67-80
• /
• 2011

This study was conducted to understand the distribution characteristics of old tree (protected tree) and propose the effective management plan for old tree to make hold a sustained function as natural and cultural resources in rural area. We surveyed 96 old trees at 69 farm villages in Yaesan-gun, South Chungcheong Province. The species of tree was investigated with Zelkova serrata, Ginkgo biloba, Quercus acutissima, Pinus densiflora, Celtis sinensis, and Juniperus cbinensis order. Most of them located at the inside (43.6%) and the entrance (35.1%) of a village, and at mountain slope (31.9%) and alluvial plain (25.3%) in terms of distribution topography. The existing place of pollution source was investigated with 61%, and the pollution sources were blocks, construction materials, cement packings, farm machines and living garbage. The place where the rates of bare ground were more than 50% for the root region of a protection tree was 63%. The tree surgical operation was investigated in 37.5% of protection trees, and 12.5% of protection trees were investigated with a tree surgical operation being immediately. The average score for health condition of old tree at study sites was 18.6 points. A monitoring class was divided by public monitor 59.3%, main monitor 38.5%, and dead tree 2.2% on the basis of that information. Hereafter, we will conduct to promote the management guideline and develope culture contents through additional investigation.

• 로봇학회논문지
• /
• 제1권2호
• /
• pp.107-116
• /
• 2006

We propose a planning algorithm to automatically generate a robust behavior plan (RBP) with which mobile robots can achieve their task goal from any initial states under dynamically changing environments. For this, task description space (TDS) is formulated, where a redundant task configuration space and simulation model of physical space are employed. Successful task episodes are collected, where $A^*$ algorithm is employed. Interesting TDS state vectors are extracted, where occurrence frequency is used. Clusters of TDS state vectors are found by using state transition tuples and features of state transition tuples. From these operations, characteristics of successfully performed tasks by a simulator are abstracted and generalized. Then, a robust behavior plan is constructed as an ordered tree structure, where nodes of the tree are represented by attentive TDS state vector of each cluster. The validity of our method is tested by real robot's experimentation for a box-pushing-into-a-goal task.

• ETRI Journal
• /
• 제40권4호
• /
• pp.471-482
• /
• 2018

This paper proposes the necessity of a walking period in footstep planning and details situations in which it should be considered. An optimization-based fast footstep planner that takes the walking period into consideration is also presented. This footstep planner comprises three stages. A binary search is first used to determine the walking period. The front stride, side stride, and walking direction are then determined using the modified rapidly-exploring random tree algorithm. Finally, particle swarm optimization (PSO) is performed to ensure feasibility without departing significantly from the results determined in the two stages. The parameters determined in the previous two stages are optimized together through the PSO. Fast footstep planning is essential for coping with dynamic obstacle environments; however, optimization techniques may require a large computation time. The two stages play an important role in limiting the search space in the PSO. This framework enables fast footstep planning without compromising on the benefits of a continuous optimization approach.