scholarly journals Local and Global Path Generation for Autonomous Vehicles Using Splines

Ingeniería ◽  
2016 ◽  
Vol 21 (2) ◽  
pp. 188-200 ◽  
Author(s):  
Randerson Lemos ◽  
Olmer Garcia ◽  
Janito Vaqueiro Ferreira
Keyword(s):  
Path Planning ◽  
Autonomous Vehicles ◽  
Planning Problem ◽  
Arc Length ◽  
Raw Data ◽  
Planning Technique ◽  
Global Route ◽  
Autonomous Mobility ◽  
Highly Correlated ◽  
Global And Local

Context: Before autonomous vehicles being a reality in daily situations, outstanding issues regarding the techniques of autonomous mobility must be solved. Hence, relevant aspects of a path planning for terrestrial vehicles are shown.Method: The approached path planning technique uses splines to generate the global route. For this goal, waypoints obtained from online map services are used. With the global route parametrized in the arc-length, candidate local paths are computed and the optimal one is selected by cost functions.Results: Different routes are used to show that the number and distribution of waypoints are highly correlated to a satisfactory arc-length parameterization of the global route, which is essential to the proper behavior of the path planning technique.Conclusions: The cubic splines approach to the path planning problem successfully generates the global and local paths. Nevertheless, the use of raw data from the online map services showed to be unfeasible due the consistency of the data. Hence, a preprocessing stage of the raw data is proposed to guarantee the well behavior and robustness of the technique.

scholarly journals Adaptive Operator Quantum-Behaved Pigeon-Inspired Optimization Algorithm with Application to UAV Path Planning

Algorithms ◽  
2018 ◽  
Vol 12 (1) ◽  
pp. 3 ◽  
Author(s):  
Chunhe Hu ◽  
Yu Xia ◽  
Junguo Zhang
Keyword(s):  
Path Planning ◽  
Nonlinear Problems ◽  
Mapping Method ◽  
Initial Population ◽  
Local Optimum ◽  
Planning Problem ◽  
Dynamic Constraints ◽  
Logistic Mapping ◽  
Global And Local ◽  
Better Than

Path planning of unmanned aerial vehicles (UAVs) in threatening and adversarial areas is a constrained nonlinear optimal problem which takes a great amount of static and dynamic constraints into account. Quantum-behaved pigeon-inspired optimization (QPIO) has been widely applied to such nonlinear problems. However, conventional QPIO is suffering low global convergence speed and local optimum. In order to solve the above problems, an improved QPIO algorithm, adaptive operator QPIO, is proposed in this paper. Firstly, a new initialization process based on logistic mapping method is introduced to generate the initial population of the pigeon-swarm. After that, to improve the performance of the map and compass operation, the factor parameter will be adaptively updated in each iteration, which can balance the ability between global and local search. In the final landmark operation, the gradual decreasing pigeon population-updating strategy is introduced to prevent premature convergence and local optimum. Finally, the demonstration of the proposed algorithm on UAV path planning problem is presented, and the comparison result indicates that the performance of our algorithm is better than that of particle swarm optimization (PSO), pigeon-inspired optimization (PIO), and its variants, in terms of convergence and accuracy.

scholarly journals Global and Local Path Planning Study in a ROS-Based Research Platform for Autonomous Vehicles

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Pablo Marin-Plaza ◽  
Ahmed Hussein ◽  
David Martin ◽  
Arturo de la Escalera
Keyword(s):  
Path Planning ◽  
Autonomous Vehicles ◽  
Starting Point ◽  
Model Configuration ◽  
Software Prototyping ◽  
Research Platform ◽  
Local Path ◽  
Low Sensitivity ◽  
Global And Local ◽  
Carlos Iii

The aim of this work is to integrate and analyze the performance of a path planning method based on Time Elastic Bands (TEB) in real research platform based on Ackermann model. Moreover, it will be proved that all modules related to the navigation can coexist and work together to achieve the goal point without any collision. The study is done by analyzing the trajectory generated from global and local planners. The software prototyping tool is Robot Operating System (ROS) from Open Source Robotics Foundation and the research platform is the iCab (Intelligent Campus Automobile) from University Carlos III. This work has been validated from a test inside the campus where the iCab has performed the navigation between the starting point and the goal point without any collision. During the experiment, we proved the low sensitivity of the TEB method to variations of the vehicle model configuration and constraints.

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.

scholarly journals UAV Path Planning for Reconnaissance and Look-Ahead Coverage Support for Mobile Ground Vehicles

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4595
Author(s):  
Herath M. P. C. Jayaweera ◽  
Samer Hanoun
Keyword(s):  
Path Planning ◽  
Potential Field ◽  
Superior Performance ◽  
Artificial Potential Field ◽  
Planning Problem ◽  
Ground Vehicles ◽  
Look Ahead ◽  
Planning Technique ◽  
Coverage Quality ◽  
Low Performance

Path planning of unmanned aerial vehicles (UAVs) for reconnaissance and look-ahead coverage support for mobile ground vehicles (MGVs) is a challenging task due to many unknowns being imposed by the MGVs’ variable velocity profiles, change in heading, and structural differences between the ground and air environments. Few path planning techniques have been reported in the literature for multirotor UAVs that autonomously follow and support MGVs in reconnaissance missions. These techniques formulate the path planning problem as a tracking problem utilizing gimbal sensors to overcome the coverage and reconnaissance complexities. Despite their lack of considering additional objectives such as reconnaissance coverage and dynamic environments, they retain several drawbacks, including high computational requirements, hardware dependency, and low performance when the MGV has varying velocities. In this study, a novel 3D path planning technique for multirotor UAVs is presented, the enhanced dynamic artificial potential field (ED-APF), where path planning is formulated as both a follow and cover problem with nongimbal sensors. The proposed technique adopts a vertical sinusoidal path for the UAV that adapts relative to the MGV’s position and velocity, guided by the MGV’s heading for reconnaissance and exploration of areas and routes ahead beyond the MGV sensors’ range, thus extending the MGV’s reconnaissance capabilities. The amplitude and frequency of the sinusoidal path are determined to maximize the required look-ahead visual coverage quality in terms of pixel density and quantity pertaining to the area covered. The ED-APF was tested and validated against the general artificial potential field techniques for various simulation scenarios using Robot Operating System (ROS) and Gazebo-supported PX4-SITL. It demonstrated superior performance and showed its suitability for reconnaissance and look-ahead support to MGVs in dynamic and obstacle-populated environments.

A path planning method of anti-jamming ability improvement for autonomous vehicle navigating in off-road environments

2017 ◽  
Vol 44 (4) ◽  
pp. 406-415 ◽  
Author(s):  
Jiajia Chen ◽  
Wuhua Jiang ◽  
Pan Zhao ◽  
Jinfang Hu
Keyword(s):  
Path Planning ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Safety Margin ◽  
Planning Method ◽  
General Regression Neural Network ◽  
Support Vector ◽  
Planning Problem ◽  
Fuzzy Support Vector Machine ◽  
Content Type

Purpose Navigating in off-road environments is a huge challenge for autonomous vehicles, due to the safety requirement, the effects of noises and non-holonomic constraints of vehicle. This paper aims to describe a path planning method based on fuzzy support vector machine (FSVM) and general regression neural network (GRNN) that is able to provide a solution path for the autonomous vehicle navigating in the off-road environments. Design/methodology/approach The authors decompose the path planning problem into three steps. In the first step, A* algorithm is applied to obtain the positive and negative samples. In the second step, the authors use a learning approach based on radial basis function kernel FSVM to maximize the safety margin for driving, and the fuzzy membership is designed based on GRNN which can help to resolve the problem that the traditional path planning method is easily influenced by noises or outliers. In the third step, the Bezier interpolation algorithm is used to smooth the path. The simulations are designed to verify the parameters of the path planning algorithm. Findings The method is implemented on autonomous vehicle and verified against many outdoor scenes. Road test indicates that the proposed method can produce a flexible, smooth and safe path with good anti-jamming performance. Originality/value This paper applied a new path planning method based on GRNN-FSVM for autonomous vehicle navigating in off-road environments. GRNN-FSVM can reduce the effects of outliers and maximize the safety margin for driving, the generated path is smooth and safe, while satisfying the constraint of vehicle kinematic.

scholarly journals Path planning with Pythagorean-hodograph curves for unmanned or autonomous vehicles

2017 ◽  
Vol 232 (7) ◽  
pp. 1361-1372 ◽  
Author(s):  
Rida T Farouki ◽  
Carlotta Giannelli ◽  
Duccio Mugnaini ◽  
Alessandra Sestini
Keyword(s):  
Path Planning ◽  
Autonomous Vehicles ◽  
Underwater Vehicles ◽  
Constant Speed ◽  
Arc Length ◽  
Exact Methods ◽  
Bounded Curvature ◽  
Curvature Bound ◽  
Pythagorean Hodograph

Pythagorean-hodograph (PH) curves offer distinct advantages in planning curvilinear paths for unmanned or autonomous air, ground, or underwater vehicles. Although several authors have discussed their use in these contexts, prior studies contain misconceptions about the properties of PH curves or invoke heuristic approximate constructions when exact methods are available. To address these issues, the present study provides a basic introduction to the key properties of PH curves, and describes some exact constructions of particular interest in path planning. These include (a) maintenance of minimum safe separations within vehicle swarms; (b) construction of paths of different shape but identical arc length, ensuring simultaneous arrival of vehicles travelling at a constant speed; (c) determination of the curvature extrema of PH paths, and their modification to satisfy a given curvature bound; and (d) construction of curvature-continuous paths of bounded curvature through fields of polygonal obstacles.

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