scholarly journals Review on path planning algorithm for unmanned aerial vehicles

2021 ◽  
Vol 24 (2) ◽  
pp. 1017
Author(s):  
Nurul Saliha Amani Ibrahim ◽  
Faiz Asraf Saparudin
Keyword(s):  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Optimal Path ◽  
Computation Time ◽  
Planning Problem ◽  
Aerial Vehicles ◽  
Planning Algorithm ◽  
Path Planning Algorithm

The path planning problem has been a crucial topic to be solved in autonomous vehicles. Path planning consists operations to find the route that passes through all of the points of interest in a given area. Several algorithms have been proposed and outlined in the various literature for the path planning of autonomous vehicle especially for unmanned aerial vehicles (UAV). The algorithms are not guaranteed to give full performance in each path planning cases but each one of them has their own specification which makes them suitable in sophisticated situation. This review paper evaluates several possible different path planning approaches of UAVs in terms optimal path, probabilistic completeness and computation time along with their application in specific problems.

Optimal Collision-Free Path Planning for an Autonomous Multi-Wheeled Combat Vehicle

2017 ◽  
Author(s):  
Amr Mohamed ◽  
Moustafa El-Gindy ◽  
Jing Ren ◽  
Haoxiang Lang
Keyword(s):  
Path Planning ◽  
Potential Field ◽  
Vehicle Dynamics ◽  
Autonomous Vehicle ◽  
Optimal Path ◽  
Vehicle Model ◽  
Optimal Path Planning ◽  
Combat Vehicle ◽  
Planning Algorithm ◽  
Path Planning Algorithm

This paper presents an optimal collision-free path planning algorithm of an autonomous multi-wheeled combat vehicle using optimal control theory and artificial potential field function (APF). The optimal path of the autonomous vehicle between a given starting and goal points is generated by an optimal path planning algorithm. The cost function of the path planning is solved together with vehicle dynamics equations to satisfy the vehicle dynamics constraints and the boundary conditions. For this purpose, a simplified four-axle bicycle model of the actual vehicle considering the vehicle body lateral and yaw dynamics while neglecting roll dynamics is used. The obstacle avoidance technique is mathematically modeled based on the proposed sigmoid function as the artificial potential field method. This potential function is assigned to each obstacle as a repulsive potential field. The inclusion of these potential fields results in a new APF which controls the steering angle of the autonomous vehicle to reach the goal point. A full nonlinear multi-wheeled combat vehicle model in TruckSim software is used for validation. This is done by importing the generated optimal path data from the introduced optimal path planning MATLAB algorithm and comparing lateral acceleration, yaw rate and curvature at different speeds (9 km/h, 28 km/h) for both simplified and TruckSim vehicle model. The simulation results show that the obtained optimal path for the autonomous multi-wheeled combat vehicle satisfies all vehicle dynamics constraints and successfully validated with TruckSim vehicle model.

Cooperative Search and Path Planning of Multi-Unmanned Aerial Vehicles in Uncertain Environment Based on Bayes Theory

2013 ◽  
Vol 336-338 ◽  
pp. 843-846 ◽  
Author(s):  
Xia Chen ◽  
Jing Zhang ◽  
Zhen Yu Lu
Keyword(s):  
Path Planning ◽  
Physical Properties ◽  
Unmanned Aerial Vehicles ◽  
Search Theory ◽  
A Algorithm ◽  
Uncertain Environment ◽  
Aerial Vehicles ◽  
Bayes Theory ◽  
Planning Algorithm ◽  
Path Planning Algorithm

In order to solve the question of cooperative searching target in uncertain environment, this paper comes up with a algorithm. Firstly it analysis the uncertainty about measure of UAV sensors and environment, we built the information model of uncertain environment. Then, considering about UAV physical properties and optimal search theory, it designs the award function, gives the path planning algorithm of cooperative searching based on the Bayes theory. The algorithm ensures that the UAV formation could search unknown environment as far as possible, evade the known environment and avoid no-fly zone completely. Finally, the simulation proves the rationality and effectiveness of algorithm.

scholarly journals Ellipsoidal Path Planning for Unmanned Aerial Vehicles

2021 ◽  
Vol 11 (17) ◽  
pp. 7997
Author(s):  
Carlos Villaseñor ◽  
Alberto A. Gallegos ◽  
Gehova Lopez-Gonzalez ◽  
Javier Gomez-Avila ◽  
Jesus Hernandez-Barragan ◽  
...  
Keyword(s):  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Clustering Algorithms ◽  
Computational Cost ◽  
Mapping Algorithm ◽  
Planning Strategy ◽  
Aerial Vehicles ◽  
Planning Algorithm ◽  
Trained Neural Network ◽  
Path Planning Algorithm

The research in path planning for unmanned aerial vehicles (UAV) is an active topic nowadays. The path planning strategy highly depends on the map abstraction available. In a previous work, we presented an ellipsoidal mapping algorithm (EMA) that was designed using covariance ellipsoids and clustering algorithms. The EMA computes compact in-memory maps, but still with enough information to accurately represent the environment and to be useful for robot navigation algorithms. In this work, we develop a novel path planning algorithm based on a bio-inspired algorithm for navigation in the ellipsoidal map. Our approach overcomes the problem that there is no closed formula to calculate the distance between two ellipsoidal surfaces, so it was approximated using a trained neural network. The presented path planning algorithm takes advantage of ellipsoid entities to represent obstacles and compute paths for small UAVs regardless of the concavity of these obstacles, in a very geometrically explicit way. Furthermore, our method can also be used to plan routes in dynamical environments without adding any computational cost.

scholarly journals An Efficient Path Planning Algorithm for Networked Robots using Modified Optimization Algorithm

2019 ◽  
Vol 8 (12) ◽  
pp. 2738-2743
Keyword(s):  
Path Planning ◽  
Optimization Algorithm ◽  
Optimal Path ◽  
Object Identification ◽  
Planning Problem ◽  
Networked Robots ◽  
Design Choice ◽  
Planning Algorithm ◽  
3D Cnn ◽  
Path Planning Algorithm

Path planning has played a significant role in major numerous decision-making techniques through an automatic system involved in numerous military applications. In the last century, pathfinding and generation were carried out by multiple intelligent approaches. It is very difficult in pathfinding to reduce energy. Besides suggesting the shortest path, it has been found that optimal path planning. This paper introduces an efficient path planning algorithm for networked robots using modified optimization algorithms in combination with the η3 -splines. A new method has employed a cuckoo optimization algorithm to handle the mobile robot path planning problem. At first, η3 - splines are combined so an irregular set of points can be included alongside the kinematic parameters chosen to relate with the development and the control of mobile robots. The proposed algorithm comprises of adaptive random fluctuations (ARFs), which help to deal with the very much manageable neighborhood convergence. This algorithm carries out the process of accurate object identification along with analyzing the influence of different design choice by developing a 3D CNN architecture to determine its performance. Besides offering classification in real-time applications, the proposed algorithm outperforms the performance of state of the art in different benchmarks

scholarly journals Low-Complexity Path Planning Algorithm for Unmanned Aerial Vehicles in Complicated Scenarios

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 57049-57055 ◽  
Author(s):  
Zhiqiang Xiao ◽  
Bingcheng Zhu ◽  
Yongjin Wang ◽  
Pu Miao
Keyword(s):  
Path Planning ◽  
Unmanned Aerial Vehicles ◽  
Low Complexity ◽  
Aerial Vehicles ◽  
Planning Algorithm ◽  
Path Planning Algorithm

scholarly journals COMPUTATIONALLY EFFICIENT PATH PLANNING ALGORITHM FOR AUTONOMOUS VEHICLE

2020 ◽  
Vol 83 (1) ◽  
pp. 133-143
Author(s):  
Sanjoy Kumar Debnath ◽  
Rosli Omar ◽  
Nor Badariyah Abdul Latip ◽  
Susama Bagchi ◽  
Elia Nadira Sabudin ◽  
...  
Keyword(s):  
Path Planning ◽  
Performance Assessment ◽  
Free Path ◽  
Path Length ◽  
Autonomous Vehicle ◽  
Computation Time ◽  
Visibility Graph ◽  
Computationally Efficient ◽  
Planning Algorithm ◽  
Path Planning Algorithm

This paper analyses an experimental path planning performance between the Iterative Equilateral Space Oriented Visibility Graph (IESOVG) and conventional Visibility Graph (VG) algorithms in terms of computation time and path length for an autonomous vehicle. IESOVG is a path planning algorithm that was proposed to overcome the limitations of VG which is slow in obstacle-rich environment. The performance assessment was done in several identical scenarios through simulation. The results showed that the proposed IESOVG algorithm was much faster in comparison to VG. In terms of path length, IESOVG was found to have almost similar performance with VG.  It was also found that IESOVG was complete as it could find a collision-free path in all scenarios.

A Geometric Path-Planning Algorithm in Cluttered Planar Environments Using Convex Hulls

2018 ◽  
Author(s):  
Nafiseh Masoudi ◽  
Georges Fadel
Keyword(s):  
Path Planning ◽  
Optimal Path ◽  
Visibility Graph ◽  
Convex Hulls ◽  
Planning Problem ◽  
Wire Routing ◽  
Visibility Algorithm ◽  
Planning Algorithm ◽  
Global Optimal ◽  
Path Planning Algorithm

The problem of finding a collision free path in an environment occupied by obstacles, known as path planning, has many applications in design of complex systems such as wire routing in automobile assemblies or motion planning for robots. Developing the visibility graph of the workspace is among the first techniques to address the path-planning problem. The visibility algorithm is efficient in finding the global optimal path. However, it is computationally expensive as it explores the entire workspace of the problem to create all non-intersecting segments of the graph. In this paper, we propose an algorithm based on the notion of convex hulls to generate the partial visibility graph from a given start point to a goal point in a 2D workspace cluttered with a number of disjoint polygonal convex or concave obstacles. The algorithm facilitates the attainment of the shortest path in a planar workspace while reducing the size of the visibility graph to explore.

scholarly journals Autonomous Vehicle Path Planning using Q-Learning

2021 ◽  
Vol 2128 (1) ◽  
pp. 012018
Author(s):  
Mohammed M S Ibrahim ◽  
Mostafa Rostom Atia ◽  
MW Fakhr
Keyword(s):  
Path Planning ◽  
Autonomous Vehicle ◽  
Optimal Path ◽  
Lookup Table ◽  
Planning Problem ◽  
Training Time ◽  
Q Learning ◽  
Starting Position ◽  
Planning Algorithm ◽  
Path Planning Problem

Abstract Path planning is vital in autonomous vehicle technology, from robots to self-driving cars and driverless trucks, it is impossible to navigate without a proper path planning algorithm, various algorithms exist Q-learning being one of them. Q-learning is used extensively in discrete applications as it is effective in finding solutions to these problems. This research investigates the possibility of using Q-learning for solving the local path planning problem with obstacle avoidance. Q-learning is split into two phases, the first being the training phase, and the second being the application phase. During training, Q-learning requires exponentially increasing training time based on the system’s state space. However, when Q-learning is applied it becomes as simple as a lookup table which allows it to run on even the simplest microcontrollers. Two simulations are conducted with varying environments. One to showcase the ability to learn the optimal path, the other to showcase the ability for learning navigation in variable environments. The first simulation was run on a static environment with one obstacle, with enough training episodes, Q-learning could solve the path planning problem with minimal movement steps. The second simulation focuses on a randomized environment, obstacles and the agent’s starting position are randomly chosen at the start of every episode. During testing, Q-learning was able to find a path to the target when a path did exist, as It was possible in certain configurations for the vehicle to be stuck in between obstacles with no feasible path or solution.

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