• 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 Industrial and Applied Mathematics
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• v.25 no.4
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• pp.149-161
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• 2021

This study proposes a methodology for mission/path planning of an unmanned aerial vehicle (UAV) using an artificial potential field with the Markov Decision Process (MDP). The planning problem is formulated as an MDP. A low-resolution solution of the MDP is obtained and used to define an artificial potential field, which provides a continuous UAV mission plan. A numerical case study is conducted to demonstrate the validity of the proposed technique.

• Journal of Aerospace System Engineering
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• v.14 no.2
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• pp.1-11
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• 2020

Detecting obstacles and generating a suitable path to avoid obstacles in real time is a prime mission requirement for UAVs. In areas, close to buildings and people, detecting obstacles in the path and estimating its own position (egomotion) in GPS degraded/denied environments are usually addressed with vision-based Simultaneous Localization and Mapping (SLAM) techniques. This presents possibilities and challenges for the feasible path generation with constraints of vehicle dynamics in the configuration space. In this paper, a near real-time feasible path is shown to be generated in the ORB-SLAM framework using a chain-based path planning approach in a force field with dynamic constraints on path length and minimum turn radius. The chain-based path plan approach generates a set of nodes which moves in a force field that permits modifications of path rapidly in real time as the reward function changes. This is different from the usual approach of generating potentials in the entire search space around UAV, instead a set of connected waypoints in a simulated chain. The popular ORB-SLAM, suited for real time approach is used for building the map of the environment and UAV position and the UAV path is then generated continuously in the shortest time to navigate to the goal position. The principal contribution are (a) Chain-based path planning approach with built in obstacle avoidance in conjunction with ORB-SLAM for the first time, (b) Generation of path with minimum overheads and (c) Implementation in near real time.

• Journal of Advanced Navigation Technology
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• v.28 no.1
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• pp.68-76
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• 2024

With the recent rapid growth of the domestic and international unmanned aerial vehicle (UAV) market and the increasing importance of UAV operations in urban centers, such as UAMs, the safety management and regulatory framework for human life and property damage caused by UAV failures has been emphasized. In this study, we conducted a comparative analysis of risk-cost models that evaluate the risk of an operating area for safe UAM flight path planning, and identified the main limitations of each model to derive considerations for future model development. By providing a basic model for improving the safety of UAM operations, this study is expected to make an important contribution to technical improvements and policy decisions in the field of UAM flight path planning.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.3
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• pp.15-20
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• 2024

Path planning for unmanned aerial vehicles (UAV) is crucial in avoiding collisions with obstacles in complex environments that include both static and dynamic obstacles. Path planning algorithms like RRT and A^* are effectively handle static obstacle avoidance but have limitations with increasing computational complexity in high-dimensional environments. Reinforcement learning-based algorithms can accommodate complex environments, but like traditional path planning algorithms, they struggle with training complexity and convergence in higher-dimensional environment. In this paper, we proposed a reinforcement learning model utilizing a cell decomposition algorithm. The proposed model reduces the complexity of the environment by decomposing the learning environment in detail, and improves the obstacle avoidance performance by establishing the valid action of the agent. This solves the exploration problem of reinforcement learning and improves the convergence of learning. Simulation results show that the proposed model improves learning speed and efficient path planning compared to reinforcement learning models in general environments.

• IE interfaces
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• v.25 no.3
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• pp.365-375
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• 2012

An Unmanned Aerial Vehicle (UAV) is a powered pilotless aircraft, which is controlled remotely or autonomously. UAVs are an attractive alternative for many scientific and military organizations. UAVs can perform operations that are considered to be risky or uninhabitable for humans. UAVs are currently employed in many military missions and a number of civilian applications. For accomplishing the UAV's missions, guarantee of survivability should be preceded. The main objective of this study is to suggest a mathematical programming model and a $A^*PS$_PGA (A-star with Post Smoothing_Parallel Genetic Algorithm) for Multiple UAVs's path planning to maximize survivability. A mathematical programming model is composed by using MRPP (Most Reliable Path Problem) and MTSP (Multiple Traveling Salesman Problem). After transforming MRPP into Shortest Path Problem (SPP),$A^*PS$_PGA applies a path planning for multiple UAVs.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.12
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• pp.1238-1243
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• 2008

This paper presents an HILS(Hardware in the Loop Simulations) based experimental study for the UAV's flight trajectory planning/generation algorithms and guidance/control laws. For the various mission that is loaded on each waypoint, proper trajectory planning and generation algorithms are applied to achieve best performances. Specially, the 'smoothing path' generation and the 'tangent orbit path' guidance laws are presented for the smooth path transitions and in-circle loitering mission, respectively. For the control laws that can minimize the effects of side wind, side slip angle($\beta$) feedback to the rudder scheme is implemented. Finally, being implemented on real hardwares, all the proposed algorithms are validated with integrations of hardware and software altogether via HILS.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.42 no.2
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• pp.181-190
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• 2014

The optimized flight path planning which is appropriate for UAV operation with high performance and multiplex sensors is required for efficient ISR missions. Furthermore, a mission visualization tool is necessary for the assessment of MoE(Measures of Effectiveness) prior to mission operation and the urgent tactical decision in peace time and wartime. A mission visualization and analysis tool was developed by combining STK and MATLAB, whose tool was used for UAV ISR mission analyses in this study. In this mission analysis tool, obstacle avoidance and FoM(Figure of Merit) analysis algorithms were applied to enable the optimized mission planning.

• Journal of Aerospace System Engineering
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• v.18 no.3
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• pp.1-7
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• 2024

The utilization of Unmanned Aerial Vehicles (UAVs) is expanding in various industries such as logistics, manufacturing, and transportation. However, to operate a large number of UAVs, it is imperative to first plan a secure and efficient self-configuring communication network for UAVs. In this study, we proposed a method for planning a secure and efficient UAV self-configuring communication network using Voronoi diagrams in the following three steps: 1) generating Voronoi diagrams using obstacles, 2) selecting obstacles to consider for path generation, and 3) planning the optimal path and outputting the path. The real-time feasibility of using the proposed method for planning optimal communication paths for a realistic number of UAVs was experimentally validated.

• Journal of Aerospace System Engineering
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• v.13 no.3
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• pp.15-22
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• 2019

In this paper, a global and local flight path system for autonomous flight of the UAV is proposed. The overall system is based on the ROS robot operating system. The UAV in-built computer detects obstacles through 2-D Lidar and generates real-time local path and global path based on VFH and Modified $RRT^*$-Smart, respectively. Additionally, a movement command is issued based on the generated path on the UAV flight controller. The ground station computer receives the obstacle information and generates a 2-D SLAM map, transmits the destination point to the embedded computer, and manages the state of the UAV. The autonomous UAV flight system of the is verified through a simulator and actual flight.