• Journal of the Korea Institute of Military Science and Technology
• /
• v.20 no.6
• /
• pp.803-812
• /
• 2017

This paper proposes a local path planning method for stable autonomous driving in rough terrain. There are various path planning techniques such as candidate paths, star algorithm, and Rapidly-exploring Random Tree algorithms. However, such existing path planning has limitations to reflecting the stability of unmanned ground vehicles. This paper suggest a path planning algorithm that considering the stability of unmanned ground vehicles. The algorithm is based on the genetic algorithm and assumes to have probability based obstacle map and elevation map. The simulation result show that the proposed algorithm can be used for real-time local path planning in rough terrain.

• The Transactions of The Korean Institute of Electrical Engineers
• /
• v.63 no.1
• /
• pp.115-122
• /
• 2014

Mobile robot has been studied for long time due to its simple structure and easy modeling. Regarding path planning of the mobile robot, we suggest real-time hybrid path planning algorithm which is the combination of optimal path planning and real-time path planning in this paper. Real-time hybrid path planning algorithm modifies, finds best route, and saves calculating time. It firstly plan the route with real-time path planning then robot starts to move according to the planned route. While robot is moving, update the route as the best outcome which found by optimal path planning algorithm. Verifying the performance of the proposed method through the comparing real-time hybrid path planning with optimal path planning will be done.

• 제어로봇시스템학회:학술대회논문집
• /
• 2000.10a
• /
• pp.255-255
• /
• 2000

Technical advance of electrical chart and cruising equipment make it possible to sail without a man. It is important to decide the cruising route in view of effectiveness and stability of a ship. So we need to study on the optimal trajectory planning. Genetic algorithm is a strong optimization algorithm with adaptational random search. It is a good choice to apply genetic algorithm to the trajectory planning of a ship. We modify a genetic algorithm to solve this problem. The effectiveness of the revised genetic algorithm is assured through computer simulations.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.36 no.3
• /
• pp.17-24
• /
• 2013

Release planning in a software product line (SPL) is to select and assign the features of the multiple software products in the SPL in sequence of releases along a specified planning horizon satisfying the numerous constraints regarding technical precedence, conflicting priorities for features, and available resources. A greedy genetic algorithm is designed to solve the problems of release planning in SPL which is formulated as a precedence-constrained multiple 0-1 knapsack problem. To be guaranteed to obtain feasible solutions after the crossover and mutation operation, a greedy-like heuristic is developed as a repair operator and reflected into the genetic algorithm. The performance of the proposed solution methodology in this research is tested using a fractional factorial experimental design as well as compared with the performance of a genetic algorithm developed for the software release planning. The comparison shows that the solution approach proposed in this research yields better result than the genetic algorithm.

• The Journal of Korea Robotics Society
• /
• v.16 no.3
• /
• pp.283-290
• /
• 2021

We propose a task and motion planning algorithm for clearing obstacles and wiping surfaces, which is essential for surface disinfection during the pathogen disinfection process. The proposed task and motion planning algorithm determines task parameters such as grasping pose and placement location during the planning process without using pre-specified or discretized values. Furthermore, to quickly inspect many unit motions, we propose a motion feasibility prediction algorithm consisting of collision checking and an SVM model for inverse mechanics and self-collision prediction. Planning time analysis shows that the feasibility prediction algorithm can significantly increase the planning speed and success rates in situations with multiple obstacles. Finally, we implemented a hierarchical control scheme to enable wiping motion while following a planner-generated joint trajectory. We verified our planning and control framework by conducted an obstacle-clearing and surface wiping experiment in a simulated disinfection environment.

• 제어로봇시스템학회:학술대회논문집
• /
• 1996.10b
• /
• pp.1189-1192
• /
• 1996

Navigation is a science of directing a mobile robot as traversing the environment. The purpose of navigation is to reach a destination without getting lost or crashing into any obstacles. In this paper, we use a genetic algorithm for navigation. Genetic algorithm searches for path in the entire, continuous free space and unifies global path planning and local path planning. It is the efficient and effective method when compared with navigators using traditional approaches.

• The Journal of Korea Robotics Society
• /
• v.6 no.3
• /
• pp.255-262
• /
• 2011

Many intelligent robots have to be given environmental information to perform tasks. In this paper an intelligent robot, that is, a cleaning robot used a sensor fusing method of two sensors: LRF and StarGazer, and then was able to obtain the information. Throughout wall following using laser displacement sensor, LRF, the working area is built during the robot turn one cycle around the area. After the process of wall following, a path planning which is able to execute the work effectively is established using flow network algorithm. This paper describes an algorithm for minimal turning complete coverage path planning for intelligent robots. This algorithm divides the whole working area by cellular decomposition, and then provides the path planning among the cells employing flow networks. It also provides specific path planning inside each cell guaranteeing the minimal turning of the robots. The proposed algorithm is applied to two different working areas, and verified that it is an optimal path planning method.

• International Journal of Naval Architecture and Ocean Engineering
• /
• v.7 no.1
• /
• pp.87-99
• /
• 2015

In recent years, the development of autonomous surface vehicles has been a field of increasing research interest. There are two major areas in this field: control theory and path planning. This study focuses on path planning, and two objectives are discussed: path planning for Unmanned Surface Vehicles (USVs) and implementation of path planning in a real map. In this paper, satellite thermal images are converted into binary images which are used as the maps for the Finite Angle $A^*$ algorithm ($FAA^*$), an advanced $A^*$ algorithm that is used to determine safer and suboptimal paths for USVs. To plan a collision-free path, the algorithm proposed in this article considers the dimensions of surface vehicles. Furthermore, the turning ability of a surface vehicle is also considered, and a constraint condition is introduced to improve the quality of the path planning algorithm, which makes the traveled path smoother. This study also shows a path planning experiment performed on a real satellite thermal image, and the path planning results can be used by an USV.

• International Journal of Aeronautical and Space Sciences
• /
• v.11 no.2
• /
• pp.136-144
• /
• 2010

This paper presents an algorithm for planning waypoints for the operation of a surveillance mission using cooperative unmanned aerial vehicles (UAVs) in a given map. This algorithm is rather simple and intuitive; therefore, this algorithm is easily applied to actual scenarios as well as easily handled by operators. It is assumed that UAVs do not possess complete information about targets; therefore, kinematics, intelligence, and so forth of the targets are not considered when the algorithm is in operation. This assumption is reasonable since the algorithm is solely focused on a surveillance mission. Various parameters are introduced to make the algorithm flexible and adjustable. They are related to various cost functions, which is the main idea of this algorithm. These cost functions consist of certainty of map, waypoints of co-worker UAVs, their own current positions, and a level of interest. Each cost function is formed by simple and intuitive equations, and features are handled using the aforementioned parameters.

• Journal of Information Processing Systems
• /
• v.16 no.6
• /
• 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.