• Journal of the Korea Institute of Military Science and Technology
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• v.13 no.6
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• pp.990-997
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• 2010

The Mission environment of UGV(Unmanned Ground Vehicle) has a complexity and variety, and the status of system and sensor is dependent on the environment factors such as operation time, the weather and road type. It is necessary for UGV to cope adaptively with the various mission types, operation modes and operation environment as human operators do. To satisfy this necessity, we present an autonomy manager based on the autonomous architecture. In this paper, we design a path planning software architecture and LPP manager by using open autonomous architecture which is previously designed by ADD. Field test is conducted with UGV in order to verify the performance of LPP Manager based on the Autonomous Architecture with scenarios.

• International Journal of Control, Automation, and Systems
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• v.4 no.3
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• pp.318-324
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• 2006

The mobile manipulator is expected to work in partially defined or unstructured environments. In our global/local approach to path planning, joint trajectories are generated for a desired Cartesian space path, designed by the global path planner. For a local path planner, inverse kinematics for a redundant system is used. Joint displacement limit for the manipulator links is considered in the motion planner. In an event of failure to obtain feasible trajectories, the task cannot be accomplished. At the point of failure, a deviation in the Cartesian space path is obtained and a replanner gives a new path that would achieve the goal position. To calculate the deviation, a nonlinear optimization problem is formulated and solved by standard Sequential Quadratic Programming (SQP) method.

• Journal of the Korean Society for Precision Engineering
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• v.15 no.6
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• pp.107-115
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• 1998

A path planning using potential field method is very useful for the real-time navigation of mobile robots. However, the method needs high modeling cost to calculate the potential field because of complex preprocessing, and mobile robots may get stuck into local minima. In this paper, An efficient path planning algorithm for multiple mobile robots, based on the potential field method, was proposed. In the algorithm. the concepts of subgoals and obstacle priority were introduced. The subgoals can be used to escape local minima, or to design and change the paths of mobile robots in the work space. In obstacle priority, all the objects (obstacles and mobile robots) in the work space have their own priorities, and the object having lower priority should avoid the objects having higher priority than it has. In this paper, first, potential based path planning method was introduced, next an efficient collision-avoidance algorithm for multiple mobile robots, moving in the obstacle environment, was proposed by using subgoals and obstacle priority. Finally, the developed algorithm was demonstrated graphically to show the usefulness of the algorithm.

• Journal of Institute of Control, Robotics and Systems
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• v.11 no.2
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• pp.137-143
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• 2005

Autonomous mobile robot has an ability to navigate using both map in known environment and sensors for detecting obstacles in unknown environment. In general, autonomous mobile robot navigates by global path planning on the basis of already made map and local path planning on the basis of various kinds of sensors to avoid abrupt obstacles. This paper provides a global path planning method using self-organizing feature map which is a method among a number of neural network. The self-organizing feature map uses a randomized small valued initial weight vectors, selects the neuron whose weight vector best matches input as the winning neuron, and trains the weight vectors such that neurons within the activity bubble are move toward the input vector. On the other hand, the modified method in this research uses a predetermined initial weight vectors, gives the systematic input vector whose position best matches obstacles, and trains the weight vectors such that neurons within the activity bubble are move toward the input vector. According to simulation results one can conclude that the modified neural network is useful tool for the global path planning problem of a mobile robot.

• 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.

• The Journal of Korea Robotics Society
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• v.2 no.3
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• pp.275-281
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• 2007

This paper presents a motion planning strategy for legged robots using locomotion primitives in the complex 3D environments. First, we define configuration, motion primitives and locomotion primitives for legged robots. A hierarchical motion planning method based on a combination of 2.5 dimensional maps of the 3D workspace is proposed. A global navigation map is obtained using 2.5 dimensional maps such as an obstacle height map, a passage map, and a gradient map of obstacles to distinguish obstacles. A high-level path planner finds a global path from a 2D navigation map. A mid-level planner creates sub-goals that help the legged robot efficiently cope with various obstacles using only a small set of locomotion primitives that are useful for stable navigation of the robot. A local obstacle map that describes the edge or border of the obstacles is used to find the sub-goals along the global path. A low-level planner searches for a feasible sequence of locomotion primitives between sub-goals. We use heuristic algorithm in local motion planner. The proposed planning method is verified by both locomotion and soccer experiments on a small biped robot in a cluttered environment. Experiment results show an improvement in motion stability.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2007.04a
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• pp.7-10
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• 2007

This paper presents a fuzzy footstep planner for humanoid robots in complex environments. First, we define locomotion primitives for humanoid robots. A global planner finds a global path from a navigation map that is generated based on a combination of 2.5 dimensional maps of the 3D workspace. A local planner searches for an optimal sequence of locomotion primitives along the global path by using fuzzy footstep planning. We verify our approach on a virtual humanoid robot in a simulated environment. Simulation results show a reduction in planning time and the feasibility of the proposed method.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1996.11a
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• pp.743-747
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• 1996

A Potential Field Method is applied to the proposed algorithm for the planning of collision-free paths of redundant manipulators. The planning is carried out on the base of kinematic configuration. To make repulsive potentials, sources are distributed on the boundaries of obstacles. To escape from local minimum of the main potential and to attack other difficulties of the planning, various potentials are defined simultaneously, Inverse Kinematics Problems of the redundant manipulators are solved by unconstrained optimization method. Computer simulation result of the path planning is presented.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.10 no.3
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• pp.203-209
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• 2010

This paper presents a hierarchical fuzzy motion planner for humanoid robots in 3D uneven environments. First, we define both motion primitives and locomotion primitives of humanoid robots. A high-level planner finds a global path from a global navigation map that is generated based on a combination of 2.5 dimensional maps of the workspace. We use a passage map, an obstacle map and a gradient map of obstacles to distinguish obstacles. A mid-level planner creates subgoals that help the robot efficiently cope with various obstacles using only a small set of locomotion primitives that are useful for stable navigation of the robot. We use a local obstacle map to find the subgoals along the global path. A low-level planner searches for an optimal sequence of locomotion primitives between subgoals by using fuzzy motion planning. We verify our approach on a virtual humanoid robot in a simulated environment. Simulation results show a reduction in planning time and the feasibility of the proposed method.

• Journal of the Korea Institute of Military Science and Technology
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• v.20 no.5
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• pp.647-653
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• 2017

In this paper, we deal with the experimental verification of 1D virtual force field algorithm based reflexive local path planning on uneven and dusty environment. The existing obstacle detection method on uneven and dusty environment and 1D virtual force field based reflexive local path planning algorithm simply are introduced. Although the 1D virtual force field algorithm is verified by various simulations, additional efforts are needed to verify this algorithm in the real-world. The introduced methods are combined with each other, installed to real mobile platforms and verified by various real experiments.