scholarly journals An Approach to Air-to-Surface Mission Planner on 3D Environments for an Unmanned Combat Aerial Vehicle

Drones ◽  
2022 ◽  
Vol 6 (1) ◽  
pp. 20
Author(s):  
Ji-Won Woo ◽  
Yoo-Seung Choi ◽  
Jun-Young An ◽  
Chang-Joo Kim
Keyword(s):  
Flight Path ◽  
Target Area ◽  
Dense Area ◽  
Air Defense ◽  
3D Terrain ◽  
Planning Algorithm ◽  
Aerial Vehicle ◽  
3D Environments ◽  
Path Planner ◽  
Do So

Recently, interest in mission autonomy related to Unmanned Combat Aerial Vehicles(UCAVs) for performing highly dangerous Air-to-Surface Missions(ASMs) has been increasing. Regarding autonomous mission planners, studies currently being conducted in this field have been mainly focused on creating a path from a macroscopic 2D environment to a dense target area or proposing a route for intercepting a target. For further improvement, this paper treats a mission planning algorithm on an ASM which can plan the path to the target dense area in consideration of threats spread in a 3D terrain environment while planning the shortest path to intercept multiple targets. To do so, ASMs are considered three sequential mission elements: ingress, intercept, and egress. The ingress and egress elements require a terrain flight path to penetrate deep into the enemy territory. Thus, the proposed terrain flight path planner generates a nap-of-the-earth path to avoid detection by enemy radar while avoiding enemy air defense threats. In the intercept element, the shortest intercept path planner based on the Dubins path concept combined with nonlinear programming is developed to minimize exposure time for survivability. Finally, the integrated ASM planner is applied to several mission scenarios and validated by simulations using a rotorcraft model.

scholarly journals Rapid Penetration Path Planning Method for Stealth UAV in Complex Environment with BB Threats

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Zhe Zhang ◽  
Jian Wu ◽  
Jiyang Dai ◽  
Cheng He
Keyword(s):  
Path Planning ◽  
Flight Path ◽  
Complex Environment ◽  
Air Defense ◽  
Penetration Ability ◽  
Planning Algorithm ◽  
Aerial Vehicle ◽  
Dual Base ◽  
Search Approach ◽  
Path Planning Algorithm

This paper presents the flight penetration path planning algorithm in a complex environment with Bogie or Bandit (BB) threats for stealth unmanned aerial vehicle (UAV). The emergence of rigorous air defense radar net necessitates efficient flight path planning and replanning for stealth UAV concerning survivability and penetration ability. We propose the improved A-Star algorithm based on the multiple step search approach to deal with this uprising problem. The objective is to achieve rapid penetration path planning for stealth UAV in a complex environment. Firstly, the combination of single-base radar, dual-base radar, and BB threats is adopted to different threat scenarios which are closer to the real combat environment. Besides, the multistep search strategy, the prediction technique, and path planning algorithm are developed for stealth UAV to deal with BB threats and achieve the penetration path replanning in complex scenarios. Moreover, the attitude angle information is integrated into the flight path which can meet real flight requirements for stealth UAV. The theoretical analysis and numerical results prove the validity of our method.

scholarly journals A New Coverage Flight Path Planning Algorithm Based on Footprint Sweep Fitting for Unmanned Aerial Vehicle Navigation in Urban Environments

2019 ◽  
Vol 9 (7) ◽  
pp. 1470 ◽  
Author(s):  
Abdul Majeed ◽  
Sungchang Lee
Keyword(s):  
Path Planning ◽  
Unmanned Aerial Vehicle ◽  
Travelling Salesman Problem ◽  
Urban Environments ◽  
Flight Path ◽  
Computational Time ◽  
Minimum Length ◽  
Planning Algorithm ◽  
Aerial Vehicle ◽  
Path Planning Algorithm

This paper presents a new coverage flight path planning algorithm that finds collision-free, minimum length and flyable paths for unmanned aerial vehicle (UAV) navigation in three-dimensional (3D) urban environments with fixed obstacles for coverage missions. The proposed algorithm significantly reduces computational time, number of turns, and path overlapping while finding a path that passes over all reachable points of an area or volume of interest by using sensor footprints’ sweeps fitting and a sparse waypoint graph in the pathfinding process. We devise a novel footprints’ sweep fitting method considering UAV sensor footprint as coverage unit in the free spaces to achieve maximal coverage with fewer and longer footprints’ sweeps. After footprints’ sweeps fitting, the proposed algorithm determines the visiting sequence of footprints’ sweeps by formulating it as travelling salesman problem (TSP), and ant colony optimization (ACO) algorithm is employed to solve the TSP. Furthermore, we generate a sparse waypoint graph by connecting footprints’ sweeps’ endpoints to obtain a complete coverage flight path. The simulation results obtained from various scenarios fortify the effectiveness of the proposed algorithm and verify the aforementioned claims.

A Heuristic Mission Planning Algorithm for Heterogeneous Tasks with Heterogeneous UAVs

2015 ◽  
Vol 03 (03) ◽  
pp. 205-219 ◽  
Author(s):  
Jingjing Wang ◽  
Y. F. Zhang ◽  
L. Geng ◽  
J. Y. H. Fuh ◽  
S. H. Teo
Keyword(s):  
Task Scheduling ◽  
Search Algorithm ◽  
Time Windows ◽  
Task Assignment ◽  
Flight Path ◽  
Mission Planning ◽  
Planning Problem ◽  
Planning Algorithm ◽  
Aerial Vehicle ◽  
Good Plan

This paper investigates the unmanned aerial vehicle (UAV)-mission planning problem (MPP) in which one needs to quickly find a good plan/schedule to carry out various tasks of different time windows at various locations using a fleet of fixed-winged heterogeneous UAVs. Such a realistic and complex UAV-MPP is decomposed into two sub-problems: flight path planning and task scheduling. A graph construction and search algorithm is developed for the flight path generation. For the task scheduling problem, a new hybrid algorithm based on heuristic has been proposed: (1) small-to-medium sized problem — heuristics for task assignment and all permutations for sequencing, and (2) large sized problem — heuristics for both task assignment and sequencing. The proposed algorithms have been implemented and tested. Numerical experimental results show that the proposed algorithm is very efficient and can effectively solve relatively big problems.

scholarly journals Efficient Energy Flight Path Planning Algorithm Using 3-D Visibility Roadmap for Small Unmanned Aerial Vehicle

2017 ◽  
Vol 2017 ◽  
pp. 1-13 ◽  
Author(s):  
Zahoor Ahmad ◽  
Farman Ullah ◽  
Cong Tran ◽  
Sungchang Lee
Keyword(s):  
Path Planning ◽  
Energy Efficient ◽  
Flight Path ◽  
Considerable Time ◽  
3 Dimensional ◽  
Efficient Energy ◽  
Planning Algorithm ◽  
Aerial Vehicle ◽  
Low Altitude ◽  
Path Planning Algorithm

This paper presents the flight path planning algorithm in a 3-dimensional environment with fixed obstacles for small unmanned aerial vehicles (SUAVs). The emergence of SUAVs for commercial uses with low-altitude flight necessitates efficient flight path planning concerning economical energy consumption. We propose the visibility roadmap based on the visibility graph approach to deal with this uprising problem. The objective is to approximate the collision-free and energy-efficient flight path of SUAVs for flight missions in a considerable time complexity. Stepwise, we describe the construction of the proposed pathfinding algorithm in a convex static obstacle environment. The theoretical analysis and simulation results prove the effectiveness of our method.

scholarly journals Collision-free path planning for welding manipulator via hybrid algorithm of deep reinforcement learning and inverse kinematics

2021 ◽  
Author(s):  
Jie Zhong ◽  
Tao Wang ◽  
Lianglun Cheng
Keyword(s):  
Reinforcement Learning ◽  
Path Planning ◽  
Free Path ◽  
Inverse Kinematics ◽  
Multiple Dimensions ◽  
Continuous State ◽  
Planning Algorithm ◽  
Convergence Performance ◽  
Path Planner ◽  
Action Spaces

AbstractIn actual welding scenarios, an effective path planner is needed to find a collision-free path in the configuration space for the welding manipulator with obstacles around. However, as a state-of-the-art method, the sampling-based planner only satisfies the probability completeness and its computational complexity is sensitive with state dimension. In this paper, we propose a path planner for welding manipulators based on deep reinforcement learning for solving path planning problems in high-dimensional continuous state and action spaces. Compared with the sampling-based method, it is more robust and is less sensitive with state dimension. In detail, to improve the learning efficiency, we introduce the inverse kinematics module to provide prior knowledge while a gain module is also designed to avoid the local optimal policy, we integrate them into the training algorithm. To evaluate our proposed planning algorithm in multiple dimensions, we conducted multiple sets of path planning experiments for welding manipulators. The results show that our method not only improves the convergence performance but also is superior in terms of optimality and robustness of planning compared with most other planning algorithms.

scholarly journals Regional robust secure precise wireless transmission design for multi-user UAV broadcasting system

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Tong Shen ◽  
Tingting Liu ◽  
Yan Lin ◽  
Yongpeng Wu ◽  
Feng Shu ◽  
...  
Keyword(s):  
Estimation Error ◽  
Wireless Transmission ◽  
Artificial Noise ◽  
Target Area ◽  
Secrecy Rate ◽  
Estimation Errors ◽  
Transmission Design ◽  
Broadcasting System ◽  
Aerial Vehicle ◽  
Noise Ratio

Abstract In this paper, two regional robust secure precise wireless transmission (SPWT) schemes for multi-user unmanned aerial vehicle (UAV), (1)regional signal-to-leakage-and-noise ratio (SLNR) and artificial-noise-to-leakage-and-noise ratio (ANLNR) (R-SLNR-ANLNR) maximization and (2) point SLNR and ANLNR (P-SLNR-ANLNR) maximization, are proposed to tackle with the estimation errors of the target users’ location. In the SPWT system, the estimation error for SPWT cannot be ignored. However, the conventional robust methods in secure wireless communications optimize the beamforming vector in the desired positions only in statistical means and cannot guarantee the security for each symbol. The proposed regional robust schemes are designed for optimizing the secrecy performance in the whole error region around the estimated location. Specifically, with the known maximal estimation error, we define the target region and wiretap region. Then, we design an optimal beamforming vector and an artificial noise projection matrix, which achieve the confidential signal in the target area having the maximal power while only few signal power is conserved in the potential wiretap region. Instead of considering the statistical distributions of the estimated errors into optimization, we optimize the SLNR and ANLNR of the whole target area, which significantly decreases the complexity. Moreover, the proposed schemes can ensure that the desired users are located in the optimized region, which are more practical than the conventional methods. Simulation results show that our proposed regional robust SPWT design is capable of substantially improving the secrecy rate compared to the conventional non-robust method. The P-SLNR-ANLNR maximization-based method has the comparable secrecy performance with lower complexity than that of the R-SLNR-ANLNR maximization-based method.

scholarly journals Real - Time Trajectory and Velocity Planning for Autonomous Vehicles

2021 ◽  
Vol 10 (5) ◽  
pp. 439-448
Author(s):  
Hrishikesh Dey ◽  
Rithika Ranadive ◽  
Abhishek Chaudhari
Keyword(s):  
Path Planning ◽  
Velocity Profile ◽  
Real Time ◽  
Autonomous Vehicle ◽  
Optimal Path ◽  
Autonomous Driving ◽  
Navigation Algorithm ◽  
Velocity Planning ◽  
Planning Algorithm ◽  
Path Planner

Path planning algorithm integrated with a velocity profile generation-based navigation system is one of the most important aspects of an autonomous driving system. In this paper, a real-time path planning solution to obtain a feasible and collision-free trajectory is proposed for navigating an autonomous car on a virtual highway. This is achieved by designing the navigation algorithm to incorporate a path planner for finding the optimal path, and a velocity planning algorithm for ensuring a safe and comfortable motion along the obtained path. The navigation algorithm was validated on the Unity 3D Highway-Simulated Environment for practical driving while maintaining velocity and acceleration constraints. The autonomous vehicle drives at the maximum specified velocity until interrupted by vehicular traffic, whereas then, the path planner, based on the various constraints provided by the simulator using µWebSockets, decides to either decelerate the vehicle or shift to a more secure lane. Subsequently, a splinebased trajectory generation for this path results in continuous and smooth trajectories. The velocity planner employs an analytical method based on trapezoidal velocity profile to generate velocities for the vehicle traveling along the precomputed path. To provide smooth control, an s-like trapezoidal profile is considered that uses a cubic spline for generating velocities for the ramp-up and ramp-down portions of the curve. The acceleration and velocity constraints, which are derived from road limitations and physical systems, are explicitly considered. Depending upon these constraints and higher module requirements (e.g., maintaining velocity, and stopping), an appropriate segment of the velocity profile is deployed. The motion profiles for all the use-cases are generated and verified graphically.

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