scholarly journals An improved A-Star based path planning algorithm for autonomous land vehicles

2020 ◽  
Vol 17 (5) ◽  
pp. 172988142096226
Author(s):  
Shang Erke ◽  
Dai Bin ◽  
Nie Yiming ◽  
Zhu Qi ◽  
Xiao Liang ◽  
...  
Keyword(s):  
Path Planning ◽  
Computing Time ◽  
Avoidance Performance ◽  
Evaluation Standard ◽  
Land Vehicles ◽  
Key Points ◽  
Variable Step ◽  
Planning Algorithm ◽  
Global Planning ◽  
Path Planning Algorithm

This article presents a novel path planning algorithm for autonomous land vehicles. There are four main contributions: Firstly, an evaluation standard is introduced to measure the performance of different algorithms and to select appropriate parameters for the proposed algorithm. Secondly, a guideline generated by human or global planning is employed to develop the heuristic function to overcome the shortcoming of traditional A-Star algorithms. Thirdly, for improving the obstacle avoidance performance, key points around the obstacle are employed, which would guide the planning path to avoid the obstacle much earlier than the traditional one. Fourth, a novel variable-step based A-Star algorithm is also introduced to reduce the computing time of the proposed algorithm. Compared with the state-of-the-art techniques, experimental results show that the performance of the proposed algorithm is robust and stable.

scholarly journals A Variable-Step RRT* Path Planning Algorithm for Quadrotors in Below-Canopy

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 62980-62989
Author(s):  
Ben Liu ◽  
Wenzhao Feng ◽  
Tingting Li ◽  
Chunhe Hu ◽  
Junguo Zhang
Keyword(s):  
Path Planning ◽  
Variable Step ◽  
Planning Algorithm ◽  
Path Planning Algorithm

scholarly journals Recursive Rewarding Modified Adaptive Cell Decomposition (RR-MACD): A Dynamic Path Planning Algorithm for UAVs

Electronics ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 306 ◽  
Author(s):  
Franklin Samaniego ◽  
Javier Sanchis ◽  
Sergio García-Nieto ◽  
Raúl Simarro
Keyword(s):  
Path Planning ◽  
Computing Time ◽  
Cell Decomposition ◽  
Computational Time ◽  
Dynamic Path Planning ◽  
Relevant Task ◽  
Planning Algorithm ◽  
Free Spaces ◽  
Memory Resources ◽  
Path Planning Algorithm

A relevant task in unmanned aerial vehicles (UAV) flight is path planning in 3 D environments. This task must be completed using the least possible computing time. The aim of this article is to combine methodologies to optimise the task in time and offer a complete 3 D trajectory. The flight environment will be considered as a 3 D adaptive discrete mesh, where grids are created with minimal refinement in the search for collision-free spaces. The proposed path planning algorithm for UAV saves computational time and memory resources compared with classical techniques. With the construction of the discrete meshing, a cost response methodology is applied as a discrete deterministic finite automaton (DDFA). A set of optimal partial responses, calculated recursively, indicates the collision-free spaces in the final path for the UAV flight.

scholarly journals A Global Path Planning Algorithm Based on Bidirectional SVGA

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Taizhi Lv ◽  
Chunxia Zhao ◽  
Jiancheng Bao
Keyword(s):  
Path Planning ◽  
Multicore Processors ◽  
Computing Time ◽  
Optimal Path ◽  
Visibility Graph ◽  
Path Optimization ◽  
Time And Space ◽  
Global Path Planning ◽  
Planning Algorithm ◽  
Path Planning Algorithm

For path planning algorithms based on visibility graph, constructing a visibility graph is very time-consuming. To reduce the computing time of visibility graph construction, this paper proposes a novel global path planning algorithm, bidirectional SVGA (simultaneous visibility graph construction and path optimization by A⁎). This algorithm does not construct a visibility graph before the path optimization. However it constructs a visibility graph and searches for an optimal path at the same time. At each step, a node with the lowest estimation cost is selected to be expanded. According to the status of this node, different through lines are drawn. If this line is free-collision, it is added to the visibility graph. If not, some vertices of obstacles which are passed through by this line are added to the OPEN list for expansion. In the SVGA process, only a few visible edges which are in relation to the optimal path are drawn and the most visible edges are ignored. For taking advantage of multicore processors, this algorithm performs SVGA in parallel from both directions. By SVGA and parallel performance, this algorithm reduces the computing time and space. Simulation experiment results in different environments show that the proposed algorithm improves the time and space efficiency of path planning.

scholarly journals A Fast Global Flight Path Planning Algorithm Based on Space Circumscription and Sparse Visibility Graph for Unmanned Aerial Vehicle

Electronics ◽  
2018 ◽  
Vol 7 (12) ◽  
pp. 375 ◽  
Author(s):  
Abdul Majeed ◽  
Sungchang Lee
Keyword(s):  
Path Planning ◽  
Computing Time ◽  
Three Dimensional ◽  
Search Space ◽  
Flight Path ◽  
Visibility Graph ◽  
Planning Algorithm ◽  
Path Lengths ◽  
Path Planning Algorithm ◽  
Obstacle Geometry

This paper proposes a new flight path planning algorithm that finds collision-free, optimal/near-optimal and flyable paths for unmanned aerial vehicles (UAVs) in three-dimensional (3D) environments with fixed obstacles. The proposed algorithm significantly reduces pathfinding computing time without significantly degrading path lengths by using space circumscription and a sparse visibility graph in the pathfinding process. We devise a novel method by exploiting the information about obstacle geometry to circumscribe the search space in the form of a half cylinder from which a working path for UAV can be computed without sacrificing the guarantees on near-optimality and speed. Furthermore, we generate a sparse visibility graph from the circumscribed space and find the initial path, which is subsequently optimized. The proposed algorithm effectively resolves the efficiency and optimality trade-off by searching the path only from the high priority circumscribed space of a map. The simulation results obtained from various maps, and comparison with the existing methods show the effectiveness of the proposed algorithm and verify the aforementioned claims.

scholarly journals A Multi-Objective Coverage Path Planning Algorithm for UAVs to Cover Spatially Distributed Regions in Urban Environments

Aerospace ◽  
2021 ◽  
Vol 8 (11) ◽  
pp. 343
Author(s):  
Abdul Majeed ◽  
Seong Oun Hwang
Keyword(s):  
Path Planning ◽  
Low Cost ◽  
Computing Time ◽  
Urban Environments ◽  
Minimum Length ◽  
Multi Objective ◽  
Area Of Interest ◽  
Spatially Distributed ◽  
Planning Algorithm ◽  
Path Planning Algorithm

This paper presents a multi-objective coverage flight path planning algorithm that finds minimum length, collision-free, and flyable paths for unmanned aerial vehicles (UAV) in three-dimensional (3D) urban environments inhabiting multiple obstacles for covering spatially distributed regions. In many practical applications, UAVs are often required to fully cover multiple spatially distributed regions located in the 3D urban environments while avoiding obstacles. This problem is relatively complex since it requires the optimization of both inter (e.g., traveling from one region/city to another) and intra-regional (e.g., within a region/city) paths. To solve this complex problem, we find the traversal order of each area of interest (AOI) in the form of a coarse tour (i.e., graph) with the help of an ant colony optimization (ACO) algorithm by formulating it as a traveling salesman problem (TSP) from the center of each AOI, which is subsequently optimized. The intra-regional path finding problem is solved with the integration of fitting sensors’ footprints sweeps (SFS) and sparse waypoint graphs (SWG) in the AOI. To find a path that covers all accessible points of an AOI, we fit fewer, longest, and smooth SFSs in such a way that most parts of an AOI can be covered with fewer sweeps. Furthermore, the low-cost traversal order of each SFS is computed, and SWG is constructed by connecting the SFSs while respecting the global and local constraints. It finds a global solution (i.e., inter + intra-regional path) without sacrificing the guarantees on computing time, number of turning maneuvers, perfect coverage, path overlapping, and path length. The results obtained from various representative scenarios show that proposed algorithm is able to compute low-cost coverage paths for UAV navigation in urban environments.

scholarly journals UAV Path Planning Based on Improved A ∗ and DWA Algorithms

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Xiong Bai ◽  
Haikun Jiang ◽  
Junjie Cui ◽  
Kuan Lu ◽  
Pengyun Chen ◽  
...  
Keyword(s):  
Path Planning ◽  
Optimal Path ◽  
Planning Time ◽  
Motion Constraints ◽  
Key Points ◽  
Planning Algorithm ◽  
Global Optimal ◽  
Local Path ◽  
Path Planning Algorithm ◽  
Adaptively Adjusting

This work proposes a path planning algorithm based on A ∗ and DWA to achieve global path optimization while satisfying security and speed requirements for unmanned aerial vehicles (UAV). The algorithm first preprocesses the map for irregular obstacles encountered by a UAV in flight, including grid preprocessing for arc-shaped obstacles and convex preprocessing for concave obstacles. Further, the standard A ∗ algorithm is improved based on UAV’s flight environment information and motion constraints. Further, the DWA algorithm’s limitations regarding local optimization and long planning time are mitigated by adaptively adjusting the evaluation function according to the UAV’s safety threshold, obstacles, and environment information. As a result, the global optimal path evaluation subfunction is constructed. Finally, the key points of the global path are selected as the subtarget points of the local path planning. Under the premise of the optimal path, the UAV real-time path’s efficiency and safety are effectively improved. The experimental results demonstrate that the path planning based on improved A ∗ and DWA algorithms shortens the path length, reduces the planning time, improves the UAV path smoothness, and enhances the safety of UAV path obstacle avoidance.

scholarly journals Multi-Destination Path Planning Method Research of Mobile Robots Based on Goal of Passing through the Fewest Obstacles

2021 ◽  
Vol 11 (16) ◽  
pp. 7378
Author(s):  
Hongchao Zhuang ◽  
Kailun Dong ◽  
Yuming Qi ◽  
Ning Wang ◽  
Lei Dong
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Mobile Robots ◽  
Working Conditions ◽  
Planning Method ◽  
Grid Map ◽  
Global Path Planning ◽  
Planning Algorithm ◽  
Global Planning ◽  
Path Planning Algorithm

In order to effectively solve the inefficient path planning problem of mobile robots traveling in multiple destinations, a multi-destination global path planning algorithm is proposed based on the optimal obstacle value. A grid map is built to simulate the real working environment of mobile robots. Based on the rules of the live chess game in Go, the grid map is optimized and reconstructed. This grid of environment and the obstacle values of grid environment between each two destination points are obtained. Using the simulated annealing strategy, the optimization of multi-destination arrival sequence for the mobile robot is implemented by combining with the obstacle value between two destination points. The optimal mobile node of path planning is gained. According to the Q-learning algorithm, the parameters of the reward function are optimized to obtain the q value of the path. The optimal path of multiple destinations is acquired when mobile robots can pass through the fewest obstacles. The multi-destination path planning simulation of the mobile robot is implemented by MATLAB software (Natick, MA, USA, R2016b) under multiple working conditions. The Pareto numerical graph is obtained. According to comparing multi-destination global planning with single-destination path planning under the multiple working conditions, the length of path in multi-destination global planning is reduced by 22% compared with the average length of the single-destination path planning algorithm. The results show that the multi-destination global path planning method of the mobile robot based on the optimal obstacle value is reasonable and effective. Multi-destination path planning method proposed in this article is conducive to improve the terrain adaptability of mobile robots.

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