• Journal of Institute of Control, Robotics and Systems
• /
• v.17 no.4
• /
• pp.362-367
• /
• 2011

The collision-free path of a manipulator should be regenerated in the real time to achieve collision safety when obstacles or humans come into the workspace of the manipulator. A probabilistic roadmap (PRM) method, one of the popular path planning schemes for a manipulator, can find a collision-free path by connecting the start and goal poses through the roadmap constructed by drawing random nodes in the free configuration space. The path planning method based on the configuration space shows robust performance for static environments which can be converted into the off-line processing. However, since this method spends considerable time on converting dynamic obstacles into the configuration space, it is not appropriate for real-time generation of a collision-free path. On the other hand, the method based on the workspace can provide fast response even for dynamic environments because it does not need the conversion into the configuration space. In this paper, we propose an efficient real-time path planning by combining the PRM and the potential field methods to cope with static and dynamic environments. The PRM can generate a collision-free path and the potential field method can determine the configuration of the manipulator. A series of experiments show that the proposed path planning method can provide robust performance for various obstacles.

• Journal of Institute of Control, Robotics and Systems
• /
• v.12 no.5
• /
• pp.444-449
• /
• 2006

Path planning is a key element in navigation of a mobile robot. Several algorithms such as a gradient method have been successfully implemented so for. Although the gradient method can provide the global optimal path, it computes the navigation function over the whole environment at all times, which result in high computational cost. This paper proposes a high-speed path planning scheme, called a gradient method with topological information, in which the search space for computation of a navigation function can be remarkably reduced by exploiting the characteristics of the topological information reflecting the topology of the navigation path. The computing time of the gradient method with topological information can therefore be significantly decreased without losing the global optimality. This reduced path update period allows the mobile robot to find a collision-free path even in the dynamic environment.

• Journal of Institute of Control, Robotics and Systems
• /
• v.20 no.11
• /
• pp.1175-1180
• /
• 2014

This paper proposes a new technique that produces improved local information using a low-cost GPS/INS system combined with Extended Kalman Filter and Path Planning when a Quad-rotor flies. In the research, a low-cost GPS is combined with INS by Extended Kalman Filter to improve local information. However, this system has disadvantages in that estimation accuracy is getting worsens when the Quad-rotor flies through the air in a curve and precision of location information is influenced by performance of the used GPS. An algorithm based on Path Planning is adopted to deal with these weaknesses. When the Quad-rotor flies outdoors, a short moving path can be predicted because all short moving paths of quad-rotor can be assumed to be straight. Path planning is used to make the short moving path and determine the closest local information of data of the GPS/INS system to location determined by path planning. Through the foregoing process, improved local data is obtained when the quad-rotor flies, and the performance of the proposed system is verified from various outdoor experiments.

• Journal of Institute of Control, Robotics and Systems
• /
• v.18 no.3
• /
• pp.267-274
• /
• 2012

We proposed the KPP (Korea university Path Planner) in our previous works. The KPP is the path planning scheme of a car-like mobile robot in parking environment. The objective of this paper is to investigate the advantage of the KPP through the quantitative and qualitative analysis compared with conventional RRT. For comparison, we proposed travel time for performance index. This paper shows that the KPP shows outstanding performances from the viewpoint of travel time and computational efficiency compared with RRT.

• International Journal of Control, Automation, and Systems
• /
• v.5 no.6
• /
• pp.674-680
• /
• 2007

The probabilistic roadmap (PRM) method, which is a popular path planning scheme, for a manipulator, can find a collision-free path by connecting the start and goal poses through a roadmap constructed by drawing random nodes in the free configuration space. PRM exhibits robust performance for static environments, but its performance is poor for dynamic environments. On the other hand, reinforcement learning, a behavior-based control technique, can deal with uncertainties in the environment. The reinforcement learning agent can establish a policy that maximizes the sum of rewards by selecting the optimal actions in any state through iterative interactions with the environment. In this paper, we propose efficient real-time path planning by combining PRM and reinforcement learning to deal with uncertain dynamic environments and similar environments. A series of experiments demonstrate that the proposed hybrid path planner can generate a collision-free path even for dynamic environments in which objects block the pre-planned global path. It is also shown that the hybrid path planner can adapt to the similar, previously learned environments without significant additional learning.

• Proceedings of the KIEE Conference
• /
• 1997.11a
• /
• pp.576-580
• /
• 1997

This paper presents a path planning algorithm for biped walking robot in 3-D workspace. Since the biped walking robot can generate path on some 3-D obstacles that cannot generate path in case of mobile robot, we have to make a new path planning algorithms. A 3-D-to-2-D mapping algorithm is proposed and two kinds of path planning algorithms are also proposed. They make it easier to generate an efficient path for biped walking robot under given environment. Some simulation results are shown to prove the effectiveness of proposed algorithms.

• 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.

• Proceedings of the Computational Structural Engineering Institute Conference
• /
• 2007.04a
• /
• pp.78-83
• /
• 2007

The paper focuses on the establishment of optimized bucket path planning and trajectory control designated for force-reflecting backhoe reacting to excavation environment, such as potential obstacles and ground characteristics. The developed path planning method can be used for precise bucket control, and more importantly for obstacle avoidance which is directly related to safety issues. The platform of this research was based on conventional papers regarding the kinematic model of excavator. Jacobian matrix was constructed to find optimal joint angles and rotation angles of bucket from position and orientation data of excavator. By applying Newton-Raphson method optimal joint angles and bucket orientation were derived simultaneously in the way of minimizing positional errors of excavator. The model presented in this paper was intended to function as a cornerstone to build complete and advanced path planning of excavator by implementing soil mechanics and further study of excavator dynamics together.

• Journal of Institute of Control, Robotics and Systems
• /
• v.16 no.1
• /
• pp.53-60
• /
• 2010

We propose a motion planning method for autonomous mobile robots. In order to minimize traveling time, a smooth path and a time optimal velocity profile should be generated under kinematic and dynamic constraints. In this paper, we develop an effective and practical method to generate a good solution with lower computation time. The initial path is obtained from voronoi diagram by Dijkstra's algorithm. Then the path is improved by changing the graph and path simultaneously. We apply the dynamic programming algorithm into the stage of improvement. Simulation results are presented to verify the performance of the proposed method.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2001.10a
• /
• pp.301-305
• /
• 2001

Conventionally, robot control algorithms are divided into two stages, namely, path or trajectory planning and path tracking(or path control). This division has been adopted mainly as a means of alleviating difficulties in dealing with complex, complex, coupled manipulator dynamics. The minimum-time manipulator control problem is solved for the case when the path is specified and the actuator torque limitations are known. In path planning, DP is applied to applied to find the shortest path form initial position to final position with the assumptions that there is no obstacle and that each path is straight line. In path control, the phase plane technique is applied to the minimum-time control with the assumptions that the bound on each actuator torque is a function of joint position and velocity or constant. This algorithm can be used for any manipulator that has rigid link, known dynamics equations of motion, and joint angles that can be determined at a given position on the path.