• Journal of the Korea Institute of Military Science and Technology
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• v.16 no.4
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• pp.473-480
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• 2013

The purpose of this paper is to demonstrate that the genetic algorithm can be useful for the global path planning when the obstacles and current field data are given. In particular, the possibilities for a novel type small AUV mission deployment in tidal regions, which experience vortical currents, were examined. Experimental simulations show feasibility and effective in generate the global path regardless of current and obstacles. By choosing an appropriate path in space, an AUV may both bypass adverse currents which are too fast to be overcome by the vehicle's motor and also exploit favorable currents to achieve far greater speeds than motors could otherwise provide, while substantially saving energy.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.4
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• pp.392-398
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• 2008

We propose a new path planning method for autonomous mobile robots. To maximize the utility of mobile robots, the collision-free shortest path should be generated by on-line computation. In this paper, we develop an effective and practical method to generate a good solution by lower computation time. The initial path is obtained from skeleton graph by Dijkstra's algorithm. Then the path is improved by changing the graph and path dynamically. We apply the dynamic programming algorithm into the stage of improvement. Simulation results are presented to verify the performance of the proposed method.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.3
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• pp.157-164
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• 2022

In this paper, we describe how to implement the mountainous terrain, radar, and air defense network for UAV path planning in a 3-D environment, and perform path planning and re-planning using the PRM algorithm, a sampling-based path planning algorithm. In the case of the original PRM algorithm, the calculation to check whether there is an obstacle between the nodes is performed 1:1 between nodes and is performed continuously, so the amount of calculation is greatly affected by the number of nodes or the linked distance between nodes. To improve this part, the proposed LineGridMask method simplifies the method of checking whether obstacles exist, and reduces the calculation time of the path planning through parallelization. Finally, comparing performance with existing PRM algorithms confirmed that computational time was reduced by up to 88% in path planning and up to 94% in re-planning.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.27 no.2
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• pp.1-8
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• 2019

Optimal path planning refers to find the safe route to the destination at a low cost, is a major problem with regard to autonomous navigation. Sampling Based Planning(SBP) approaches, such as Rapidly-exploring Random Tree Star($RRT^*$), are the most influential algorithm in path planning due to their relatively small calculations and scalability to high-dimensional problems. $RRT^*$-Smart introduced path optimization and biased sampling techniques into $RRT^*$ to increase convergent rate. This paper presents an improvement plan that has changed the biased sampling method to increase the initial convergent rate of the $RRT^*$-Smart, which is specified as m$RRT^*$-Smart. With comparison among $RRT^*$, $RRT^*$-Smart and m$RRT^*$-Smart in 2 & 3-D environments, m$RRT^*$-Smart showed similar or increased initial convergent rate than $RRT^*$ and $RRT^*$-Smart.

• Journal of Ocean Engineering and Technology
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• v.20 no.1 s.68
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• pp.69-76
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• 2006

The design of efficient search path to maximize the Cumulative Detection Probability(CDP) is mainly dependent on experience and intuition when searcher detect the target using SONAR in the ocean. Recently with the advance of modeling and simulation method, it has been possible to access the optimization problems more systematically. In this paper, a method for the optimal search path calculation is developed based on the combination of the genetic algorithm and the calculation algorithm for detection range. We consider the discrete system for search path, space, and time, and use the movement direction of the SONAR for the gene of the genetic algorithm. The developed algorithm, OASPP(Optimal Acoustic Search Path Planning), is shown to be effective, via a simulation, finding the optimal search path for the case when the intuitive solution exists. Also, OASPP is compared with other algorithms for the measure of efficiency to maximize CDP.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.34S no.12
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• pp.19-29
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• 1997

This paper presents stable kinematic modeling and path planning and path tracking algorithms for the poisition control of 4-wheeled 2-d.o.f(degree of freedom) mobile robot. We drived the actuated inverse and sensed forward solution for the calculation of actuator velocity and robot velocities. the deal-reckoning algorithm is introduced to calculate the position of WMR in real time. The gaussian functions are applied to control and to design the smooth orientation angle of WMR and the path planning algorithm for obstacle avoidance is prosed. We composed feedback control system to compensate for error because of uncertainty kinematic modeling and measurement noise. The simulation resutls show that the proposed kinematkc modeling and path planning and feedback control algorithms are useful.

• Korean Journal of Computational Design and Engineering
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• v.2 no.1
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• pp.19-27
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• 1997

This paper presents rough cut tool path planning for the fewer-axis machine consisting of a three-axis CNC machine and a rotary indexing table. In the problem dealt with in this paper, the tool orientation is "intermediately" changed, distinguished from the conventional problem where the tool orientation is assumed to be fixed. The developed rough cut path planning algorithm tries to minimize the number of tool orientation (setup) changes together with tool changes and the machining time for the rough cut by the four procedures: a) decomposition of the machining area based on the possibility of tool interference (via convex hull operation), b) determination of the optimal tool size and orientation (via network graph theory and branch-and bound algorithm), c) generation of tool path for the tool and orientation (based on zig-zag pattern), and d) feedrate adjustment to maintain the cutting force at an operation level (based on average cutting force). The developed algorithms are validated via computer simulations, and can be also used in pure fiveaxis machining environment without modification.

• Journal of the Semiconductor & Display Technology
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• v.17 no.1
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• pp.88-92
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• 2018

Currently drone industry has become one of the fast growing markets and the technology for unmanned aerial vehicles are expected to continue to develop at a rapid rate. Especially small unmanned aerial vehicle systems have been designed and utilized for the various field with their own specific purposes. In these fields the path planning problem to find the shortest path between two oriented points is important. In this paper we introduce a path planning strategy for an autonomous flight of unmanned aerial vehicles through reinforcement learning with self-positioning technique. We perform Q-learning algorithm, a kind of reinforcement learning algorithm. At the same time, multi sensors of acceleraion sensor, gyro sensor, and magnetic are used to estimate the position. For the functional evaluation, the proposed method was simulated with virtual UAV environment and visualized the results. The flight history was based on a PX4 based drones system equipped with a smartphone.

• International Journal of Control, Automation, and Systems
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• v.6 no.3
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• pp.401-412
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• 2008

This paper discusses a design methodology of cooperative path planning for dynamical multi-agent systems with spatial and temporal constraints. The cooperative behavior of the multi-agent systems is specified in terms of the objective function in an optimization formulation. The path of achieving cooperative tasks is then generated by the optimization formulation constructed based on a differential flatness approach. Three scenarios of multi-agent tasking are proposed at the cooperative task planning framework. Given agent dynamics, both spatial and temporal constraints are considered in the path planning. The path planning algorithm first finds trajectory curves in a lower-dimensional space and then parameterizes the curves by a set of B-spline representations. The coefficients of the B-spline curves are further solved by a sequential quadratic programming solver to achieve the optimization objective and satisfy these constraints. Finally, several illustrative examples of cooperative path/task planning are presented.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.6
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• pp.506-513
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• 2000

This paper proposes a path planning algorithm for dispensers to increase the productivity in printed circuit board assembly lines. We analyze the assembly sequence of the dispenser, and formulate it as an integer programming problem. The mathematical formulation can accomodate multiple heads and different types of heads through extended cost matrix. The TSP algorithms are then applied to the formulated problem to find the near-optimal solution. Simulation results are presented to verify the usefulness of the proposed scheme.