scholarly journals Intelligent Optimization Algorithm-Based Path Planning for a Mobile Robot

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Qisong Song ◽  
Shaobo Li ◽  
Jing Yang ◽  
Qiang Bai ◽  
Jianjun Hu ◽  
...  
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Obstacle Avoidance ◽  
Path Length ◽  
Decision Model ◽  
Ant Colony ◽  
Global Path Planning ◽  
Local Path Planning ◽  
Local Path ◽  
Safe Path

The purpose of mobile robot path planning is to produce the optimal safe path. However, mobile robots have poor real-time obstacle avoidance in local path planning and longer paths in global path planning. In order to improve the accuracy of real-time obstacle avoidance prediction of local path planning, shorten the path length of global path planning, reduce the path planning time, and then obtain a better safe path, we propose a real-time obstacle avoidance decision model based on machine learning (ML) algorithms, an improved smooth rapidly exploring random tree (S-RRT) algorithm, and an improved hybrid genetic algorithm-ant colony optimization (HGA-ACO). Firstly, in local path planning, the machine learning algorithms are used to train the datasets, the real-time obstacle avoidance decision model is established, and cross validation is performed. Secondly, in global path planning, the greedy algorithm idea and B-spline curve are introduced into the RRT algorithm, redundant nodes are removed, and the reverse iteration is performed to generate a smooth path. Then, in path planning, the fitness function and genetic operation method of genetic algorithm are optimized, the pheromone update strategy and deadlock elimination strategy of ant colony algorithm are optimized, and the genetic-ant colony fusion strategy is used to fuse the two algorithms. Finally, the optimized path planning algorithm is used for simulation experiment. Comparative simulation experiments show that the random forest has the highest real-time obstacle avoidance prediction accuracy in local path planning, and the S-RRT algorithm can effectively shorten the total path length generated by the RRT algorithm in global path planning. The HGA-ACO algorithm can reduce the iteration number reasonably, reduce the search time effectively, and obtain the optimal solution in path planning.

scholarly journals Efficient Path Planning Method of USV for Intelligent Target Search

2019 ◽  
Vol 3 (2) ◽  
Author(s):  
Jing Zhang ◽  
Fan Zhang ◽  
Zengyuan Liu ◽  
Yunsong Li
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Obstacle Avoidance ◽  
Prior Information ◽  
Planning Method ◽  
Target Search ◽  
Global Path Planning ◽  
Local Path ◽  
Real Time Information ◽  
Time Information

AbstractIn order to accomplish a target search task safely and efficiently and make full use of prior information and real-time information, a path planning method of unmanned surface vehicle (USV) for intelligent target search is proposed. The overall strategy is divided into three parts: global path planning based on prior information, local path planning based on real-time information, and improved A* obstacle avoidance algorithm. Before the start of the task, the global path planning is carried out based on prior information such as the initial position of USV, the predicted position of the target and range of search area. After the start of the task, if USV finds suspicious targets, in order to further approach these suspicious targets, it will enter different local path planning modes according to the characteristics of these targets. During task execution, if obstacles are encountered, an improved A* obstacle avoidance algorithm is adopted. The simulation results show that the proposed method can improve the efficiency of target recognition and reduce the turning cost of USV when encountering obstacles. So, for USV intelligent target search, the proposed path planning method can save resources and improve search efficiency.

scholarly journals 2D Lidar-Based SLAM and Path Planning for Indoor Rescue Using Mobile Robots

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Xuexi Zhang ◽  
Jiajun Lai ◽  
Dongliang Xu ◽  
Huaijun Li ◽  
Minyue Fu
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Real Time ◽  
Obstacle Avoidance ◽  
Basic System ◽  
Rescue Robot ◽  
Planning Algorithm ◽  
Local Path ◽  
Slam Algorithm ◽  
Path Planning Algorithm

As the basic system of the rescue robot, the SLAM system largely determines whether the rescue robot can complete the rescue mission. Although the current 2D Lidar-based SLAM algorithm, including its application in indoor rescue environment, has achieved much success, the evaluation of SLAM algorithms combined with path planning for indoor rescue has rarely been studied. This paper studies mapping and path planning for mobile robots in an indoor rescue environment. Combined with path planning algorithm, this paper analyzes the applicability of three SLAM algorithms (GMapping algorithm, Hector-SLAM algorithm, and Cartographer algorithm) in indoor rescue environment. Real-time path planning is studied to test the mapping results. To balance path optimality and obstacle avoidance, A ∗ algorithm is used for global path planning, and DWA algorithm is adopted for local path planning. Experimental results validate the SLAM and path planning algorithms in simulated, emulated, and competition rescue environments, respectively. Finally, the results of this paper may facilitate researchers quickly and clearly selecting appropriate algorithms to build SLAM systems according to their own demands.

scholarly journals Optimal Motion Planning in GPS-Denied Environments Using Nonlinear Model Predictive Horizon

Sensors ◽  
2021 ◽  
Vol 21 (16) ◽  
pp. 5547
Author(s):  
Younes Al Younes ◽  
Martin Barczyk
Keyword(s):  
Path Planning ◽  
Motion Planning ◽  
Real Time ◽  
Nonlinear Model ◽  
Feedback Linearization ◽  
Autonomous Vehicle ◽  
Flight Test ◽  
Local Path Planning ◽  
Planning Approach ◽  
Local Path

Navigating robotic systems autonomously through unknown, dynamic and GPS-denied environments is a challenging task. One requirement of this is a path planner which provides safe trajectories in real-world conditions such as nonlinear vehicle dynamics, real-time computation requirements, complex 3D environments, and moving obstacles. This paper presents a methodological motion planning approach which integrates a novel local path planning approach with a graph-based planner to enable an autonomous vehicle (here a drone) to navigate through GPS-denied subterranean environments. The local path planning approach is based on a recently proposed method by the authors called Nonlinear Model Predictive Horizon (NMPH). The NMPH formulation employs a copy of the plant dynamics model (here a nonlinear system model of the drone) plus a feedback linearization control law to generate feasible, optimal, smooth and collision-free paths while respecting the dynamics of the vehicle, supporting dynamic obstacles and operating in real time. This design is augmented with computationally efficient algorithms for global path planning and dynamic obstacle mapping and avoidance. The overall design is tested in several simulations and a preliminary real flight test in unexplored GPS-denied environments to demonstrate its capabilities and evaluate its performance.

scholarly journals Real-time Local Path Planning for Intelligent Vehicle combining Tentacle Algorithm and B-spline Curve

2021 ◽  
Vol 54 (10) ◽  
pp. 51-58
Author(s):  
Zhuoren Li ◽  
Lu Xiong ◽  
Dequan Zeng ◽  
Zhiqiang Fu ◽  
Bo Leng ◽  
...  
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Intelligent Vehicle ◽  
B Spline ◽  
Spline Curve ◽  
Local Path Planning ◽  
Local Path

A real-time local path planning method in complex scenes

2021 ◽  
Author(s):  
Cai Wenlin ◽  
Zihui Zhu ◽  
Jianhua Li ◽  
Jiaqi Liu ◽  
Chunxi Wang
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Planning Method ◽  
Local Path Planning ◽  
Complex Scenes ◽  
Local Path

Robotic Fish Path Planning Based on an Improved A* Algorithm

2013 ◽  
Vol 336-338 ◽  
pp. 968-972 ◽  
Author(s):  
Huan Wang ◽  
Yu Lian Jiang
Keyword(s):  
Path Planning ◽  
Complex Environment ◽  
A Algorithm ◽  
Global Path Planning ◽  
Local Path Planning ◽  
Search Data ◽  
Infinite Loop ◽  
Transition Points ◽  
Local Path ◽  
Divide And Rule

Applying the global path planning to traditional A* algorithm in a complex environment and a lot of obstacles will result in an infinite loop because there are too many search data. To resolve this problem, this paper provides a new divide-and-rule path planning method which is based on improved A* algorithm. It uses several transition points to divide the entire grid map areas into several sub-regions. We set different speeds in each sub-region for local path planning. Thus the complex global path planning is turned into some simple local path planning. It reduces the search data of A* algorithm and avoids falling into the infinite loop. By this method, this paper designs the path planning of heading the ball, and smoothes the orbit. The simulation results show that the improved A* algorithm is better and more effective than the traditional one.

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