scholarly journals Fast-RRT: A RRT-Based Optimal Path Finding Method

2021 ◽  
Vol 11 (24) ◽  
pp. 11777
Author(s):  
Zhenping Wu ◽  
Zhijun Meng ◽  
Wenlong Zhao ◽  
Zhe Wu
Keyword(s):  
Optimal Path ◽  
Search Time ◽  
Sampling Strategy ◽  
Poor Performance ◽  
Narrow Channel ◽  
Random Trees ◽  
Expansion Strategy ◽  
Algorithm Stability ◽  
Search Speed ◽  
Feasible Path

As a sampling-based pathfinding algorithm, Rapidly Exploring Random Trees (RRT) has been widely used in motion planning problems due to the ability to find a feasible path quickly. However, the RRT algorithm still has several shortcomings, such as the large variance in the search time, poor performance in narrow channel scenarios, and being far from the optimal path. In this paper, we propose a new RRT-based path find algorithm, Fast-RRT, to find a near-optimal path quickly. The Fast-RRT algorithm consists of two modules, including Improved RRT and Fast-Optimal. The former is aims to quickly and stably find an initial path, and the latter is to merge multiple initial paths to obtain a near-optimal path. Compared with the RRT algorithm, Fast-RRT shows the following improvements: (1) A Fast-Sampling strategy that only samples in the unreached space of the random tree was introduced to improve the search speed and algorithm stability; (2) A Random Steering strategy expansion strategy was proposed to solve the problem of poor performance in narrow channel scenarios; (3) By fusion and adjustment of paths, a near-optimal path can be faster found by Fast-RRT, 20 times faster than the RRT* algorithm. Owing to these merits, our proposed Fast-RRT outperforms RRT and RRT* in both speed and stability during experiments.

scholarly journals Development of an Improved Rapidly Exploring Random Trees Algorithm for Static Obstacle Avoidance in Autonomous Vehicles

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2244
Author(s):  
S. M. Yang ◽  
Y. A. Lin
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Optimal Path ◽  
Random Trees ◽  
Average Deviation ◽  
Lane Changes ◽  
Path Smoothing ◽  
Rapidly Exploring Random Trees

Safe path planning for obstacle avoidance in autonomous vehicles has been developed. Based on the Rapidly Exploring Random Trees (RRT) algorithm, an improved algorithm integrating path pruning, smoothing, and optimization with geometric collision detection is shown to improve planning efficiency. Path pruning, a prerequisite to path smoothing, is performed to remove the redundant points generated by the random trees for a new path, without colliding with the obstacles. Path smoothing is performed to modify the path so that it becomes continuously differentiable with curvature implementable by the vehicle. Optimization is performed to select a “near”-optimal path of the shortest distance among the feasible paths for motion efficiency. In the experimental verification, both a pure pursuit steering controller and a proportional–integral speed controller are applied to keep an autonomous vehicle tracking the planned path predicted by the improved RRT algorithm. It is shown that the vehicle can successfully track the path efficiently and reach the destination safely, with an average tracking control deviation of 5.2% of the vehicle width. The path planning is also applied to lane changes, and the average deviation from the lane during and after lane changes remains within 8.3% of the vehicle width.

scholarly journals The Square Root UKF-SLAM Algorithm Based on the Smallest Proportion of Skewness in Single Line Sampling

2018 ◽  
Vol 160 ◽  
pp. 06005
Author(s):  
MengYuan Chen ◽  
GuoWei Qin ◽  
Tong Xu
Keyword(s):  
Computational Complexity ◽  
Covariance Matrix ◽  
Sampling Strategy ◽  
Single Line ◽  
Estimation Accuracy ◽  
Square Root ◽  
Line Sampling ◽  
Algorithm Stability ◽  
Slam Algorithm ◽  
Symmetric Sampling

In view of the distortion in the filter gain matrix calculation as well as the high computational complexity and the nonlocal effect of symmetric sampling that exists in the UKF-SLAM algorithm, the square root UKF-SLAM algorithm based on the smallest proportion of skewness in single line sampling was proposed. According to the mended algorithm, the square root of covariance matrix is brought into iteration operation instead of covariance matrix, moreover, the smallest proportion of skewness in single line sampling is utilized for the optimization of sampling strategy. The results of simulation show that the algorithm can effectively improve the position accuracy in robot as well as the estimation accuracy of feature map. Furthermore, the computational complexity is reduced and the algorithm stability is improved.

Study on Fast Codeword Search Based on Improved Ant Colony Algorithm

2014 ◽  
Vol 556-562 ◽  
pp. 3768-3773
Author(s):  
Da Yong Zou ◽  
Wei Wu
Keyword(s):  
Vector Quantization ◽  
Ant Colony Algorithm ◽  
Behavior Pattern ◽  
Optimal Path ◽  
Search Time ◽  
Search Space ◽  
Ant Colony ◽  
Computation Complexity ◽  
Setting Parameters ◽  
Quick Search

Vector quantization technology is an efficient and competitive method for data compression, but it is not easy to be implemented because of the comparatively high computation complexity it requires during the coding and decoding process. This paper presents a method of Dual Population Ant Colony Algorithm Codeword Quick Search (DPACAS), exploiting the mechanism of ant trace the optimal path through the pheromones remained, and the behavior pattern of making objects together by picking up and putting down them. It uses Parallel Ant Colony algorithm to sufficiently accelerate the convergence of the ant colony. When the scale of the codebook becomes larger, by setting parameters reasonably and exchanging the pheromones between two species, it broadens the search space, reduces the search time and improves the algorithmic global convergence effectively.

scholarly journals Optimized RRT-A* Path Planning Method for Mobile Robots in Partially Known Environment

2019 ◽  
Vol 48 (2) ◽  
pp. 179-194 ◽  
Author(s):  
Ben Beklisi Kwame Ayawli ◽  
Xue Mei ◽  
Moquan Shen ◽  
Albert Yaw Appiah ◽  
Frimpong Kyeremeh
Keyword(s):  
Mobile Robots ◽  
Spline Interpolation ◽  
Optimal Path ◽  
Cubic Spline ◽  
Random Trees ◽  
Autonomous Mobile Robots ◽  
Complex Environments ◽  
Step Size ◽  
Ground Vehicle ◽  
Speed Up

This paper presents optimized rapidly exploring random trees A* (ORRT-A*) method to improve the performance of RRT-A* method to compute safe and optimal path with low time complexity for autonomous mobile robots in partially known complex environments. ORRT-A* method combines morphological dilation, goal-biased RRT, A* and cubic spline algorithms. Goal-biased RRT is modified by introducing additional step-size to speed up the generation of the tree towards the goal after which A* is applied to obtain the shortest path. Morphological dilation technique is used to provide safety for the robots while cubic spline interpolation is used to smoothen the path for easy navigation. Results indicate that ORRT-A* method demonstrates improved path quality compared to goal-biased RRT and RRT-A* methods. ORRT-A* is therefore a promising method in achieving autonomous ground vehicle navigation in unknown environments

scholarly journals NPQ-RRT ∗ : An Improved RRT ∗ Approach to Hybrid Path Planning

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Zihan Yu ◽  
Linying Xiang
Keyword(s):  
Path Planning ◽  
Optimal Path ◽  
Research Direction ◽  
Random Trees ◽  
Target Point ◽  
Evaluation Function ◽  
Local Planning ◽  
Planning Algorithm ◽  
Window Approach ◽  
Path Planning Algorithm

In recent years, the path planning of robot has been a hot research direction, and multirobot formation has practical application prospect in our life. This article proposes a hybrid path planning algorithm applied to robot formation. The improved Rapidly Exploring Random Trees algorithm PQ-RRT ∗ with new distance evaluation function is used as a global planning algorithm to generate the initial global path. The determined parent nodes and child nodes are used as the starting points and target points of the local planning algorithm, respectively. The dynamic window approach is used as the local planning algorithm to avoid dynamic obstacles. At the same time, the algorithm restricts the movement of robots inside the formation to avoid internal collisions. The local optimal path is selected by the evaluation function containing the possibility of formation collision. Therefore, multiple mobile robots can quickly and safely reach the global target point in a complex environment with dynamic and static obstacles through the hybrid path planning algorithm. Numerical simulations are given to verify the effectiveness and superiority of the proposed hybrid path planning algorithm.

scholarly journals Improved ant colony algorithm for path planning of fixed wing unmanned aerial vehicle

2022 ◽  
Vol 355 ◽  
pp. 03002
Author(s):  
Hongchao Zhao ◽  
Jianzhong Zhao
Keyword(s):  
Path Planning ◽  
Unmanned Aerial Vehicle ◽  
Ant Colony Algorithm ◽  
Search Time ◽  
Optimal Solution ◽  
Ant Colony ◽  
Local Optimal Solution ◽  
Search Speed ◽  
Aerial Vehicle ◽  
Improved Ant Colony Algorithm

Aiming at the problems of long search time and local optimal solution of ant colony algorithm (ACA) in the path planning of unmanned aerial vehicle (UAV), an improved ant colony algorithm (IACA) was proposed from the aspects of simplicity and effectiveness. The flight performance constraints of fixed wing UAVs were treated as conditions of judging whether the candidate expanded nodes are feasible, thus the feasible nodes’ number was reduced and the search efficiency was effectively raised. In order to overcome the problem of local optimal solution, the pheromone update rule is improved by combining local pheromone update and global pheromone update. The heuristic function was improved by integrating the distance heuristic factor with the safety heuristic factor, and it enhanced the UAV flight safety performance. The transfer probability was improved to increase the IACA search speed. Simulation results show that the proposed IACA possesses stronger global search ability and higher practicability than the former IACA.

scholarly journals Improved A-Star Algorithm for Long-Distance Off-Road Path Planning Using Terrain Data Map

2021 ◽  
Vol 10 (11) ◽  
pp. 785
Author(s):  
Zhonghua Hong ◽  
Pengfei Sun ◽  
Xiaohua Tong ◽  
Haiyan Pan ◽  
Ruyan Zhou ◽  
...  
Keyword(s):  
Data Structure ◽  
Path Planning ◽  
Optimal Path ◽  
Long Distance ◽  
Processing Efficiency ◽  
Maximum Acceleration ◽  
Processing Times ◽  
Digital Elevation ◽  
Feasible Path ◽  
Elevation Model

To overcome the limitation of poor processing times for long-distance off-road path planning, an improved A-Star algorithm based on terrain data is proposed in this study. The improved A-Star algorithm for long-distance off-road path planning tasks was developed to identify a feasible path between the start and destination based on a terrain data map generated using a digital elevation model. This study optimised the algorithm in two aspects: data structure, retrieval strategy. First, a hybrid data structure of the minimum heap and 2D array greatly reduces the time complexity of the algorithm. Second, an optimised search strategy was designed that does not check whether the destination is reached in the initial stage of searching for the global optimal path, thus improving execution efficiency. To evaluate the efficiency of the proposed algorithm, three different off-road path planning tasks were examined for short-, medium-, and long-distance path planning tasks. Each group of tasks corresponded to three different off-road vehicles, and nine groups of experiments were conducted. The experimental results show that the processing efficiency of the proposed algorithm is significantly better than that of the conventional A-Star algorithm. Compared with the conventional A-Star algorithm, the path planning efficiency of the improved A-Star algorithm was accelerated by at least 4.6 times, and the maximum acceleration reached was 550 times for long-distance off-road path planning. The simulation results show that the efficiency of long-distance off-road path planning was greatly improved by using the improved algorithm.

scholarly journals RRT-A*-BT approach for optimal collision-free path planning for mobile robots

2019 ◽  
Vol 4 (2) ◽  
pp. 39-50
Author(s):  
Abdelfetah Hentout ◽  
Abderraouf Maoudj ◽  
Djelloul Yahiaoui ◽  
Mustapha Aouache
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Free Path ◽  
Vision System ◽  
Optimal Path ◽  
Hybrid Approach ◽  
Computation Time ◽  
Random Trees ◽  
Cluttered Environments ◽  
Hermite Interpolating Polynomial

This paper deals with the problem of optimal collision-free path planning for mobile robots evolving inside indoor cluttered environments. Addressing this challenge, a hybrid approach is proposed combining Rapidly-exploring Random Trees (RRT), A-Star (A*) and Back-Tracking (BT) algorithms (RRT-A*-BT). Thus, a vision system is used for a nearly-exact modeling of the environment through image processing. Moreover, each iteration of the basic RRT approach is guided by A* algorithm while trying to take the shortest path linking the robot current position to target . In case of a blockage, BT algorithm is used to get out the robot from this situation. Finally, Piecewise Cubic Hermite Interpolating Polynomial (PCHIP) is used to smooth the generated optimal path. RRT-A*-BT approach is validated through different scenarios; obtained results are compared with previous works on same environments with same conditions. The results prove that RRT-A*-BT is better and faster than other algorithms of the literature, such as Genetic Algorithms and Conventional RRT, in terms of (i) computation time,(ii) path length and (iii) transfer time..

scholarly journals Path Planning Followed by Kinodynamic Smoothing for Multirotor Aerial Vehicles (MAVs)

2021 ◽  
Vol 17 (4) ◽  
pp. 491-505
Author(s):  
G. Kulathunga ◽  
◽  
D. Devitt ◽  
R. Fedorenko ◽  
A. Klimchik ◽  
...  
Keyword(s):  
Path Planning ◽  
Motion Planning ◽  
Free Path ◽  
Optimal Path ◽  
Optimal Solution ◽  
Search Space ◽  
Planning Algorithm ◽  
Feasible Path ◽  
Straight Lines ◽  
Path Planning Algorithm

Any obstacle-free path planning algorithm, in general, gives a sequence of waypoints that connect start and goal positions by a sequence of straight lines, which does not ensure the smoothness and the dynamic feasibility to maneuver the MAV. Kinodynamic-based motion planning is one of the ways to impose dynamic feasibility in planning. However, kinodynamic motion planning is not an optimal solution due to high computational demands for real-time applications. Thus, we explore path planning followed by kinodynamic smoothing while ensuring the dynamic feasibility of MAV. The main difference in the proposed technique is not to use kinodynamic planning when finding a feasible path, but rather to apply kinodynamic smoothing along the obtained feasible path. We have chosen a geometric-based path planning algorithm “RRT*” as the path finding algorithm. In the proposed technique, we modified the original RRT* introducing an adaptive search space and a steering function that helps to increase the consistency of the planner. Moreover, we propose a multiple RRT* that generates a set of desired paths. The optimal path from the generated paths is selected based on a cost function. Afterwards, we apply kinodynamic smoothing that will result in a dynamically feasible as well as obstacle-free path. Thereafter, a b-spline-based trajectory is generated to maneuver the vehicle autonomously in unknown environments. Finally, we have tested the proposed technique in various simulated environments. According to the experiment results, we were able to speed up the path planning task by 1.3 times when using the proposed multiple RRT* over the original RRT*.

scholarly journals Grade 12 Students’ Leaving Essay Questions Unsolved in the Exam: A Phenomenological Study

2021 ◽  
Vol 1 (1) ◽  
pp. 140-153
Author(s):  
Banshidhar Joshi
Keyword(s):  
Phenomenological Study ◽  
Sampling Strategy ◽  
Poor Performance ◽  
School Level ◽  
English Teacher ◽  
Final Exam ◽  
Multiple Realities ◽  
Qualitative Research Design ◽  
Depth Interview ◽  
Teaching Composition

This paper focuses on the issue of grade 12 students’ leaving essay type questions unsolved in the examinations. The main objective of this paper is to explore the causes of leaving essay type questions unsolved and to explore the effective ways of solving the issue. As my study is guided by interpretive paradigm, it demands qualitative research design to explore the multiple realities through the methods of questionnaire and in-depth interview. One of the exam centers of Bajhang district was selected as research site. In this study, 10 students, from different schools attending their final exam at a school, were selected as sample by using purposive sampling strategy. They were assigned questionnaire to fill up and one secondary level English teacher was interviewed on the issues raised in the study. Teachers’ negligence in teaching composition, lack of sufficient time for teaching, lack of practice in classroom, and students’ poor performance in writing from the very beginning are found as the main causes. The findings show that overall scenario of teaching composition in school level is not encouraging.

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