scholarly journals A New Real-Time Path Planning Method Based on the Belief Space

2013 ◽  
Vol 2013 ◽  
pp. 1-14
Author(s):  
Yu-xin Zhao ◽  
Xin-an Wu ◽  
Yan Ma
Keyword(s):  
Path Planning ◽  
Real Time ◽  
Basic Belief ◽  
Complex Environment ◽  
New Approach ◽  
Time Path ◽  
Local Path Planning ◽  
Simulation Results ◽  
Local Path ◽  
Belief Model

A new approach of real-time path planning based on belief space is proposed, which solves the problems of modeling the real-time detecting environment and optimizing in local path planning with the fusing factors. Initially, a double-safe-edges free space is defined for describing the sensor detecting characters, so as to transform the complex environment into some free areas, which can help the robots to reach any positions effectively and safely. Then, based on the uncertainty functions and the transferable belief model (TBM), the basic belief assignment (BBA) spaces of each factor are presented and fused in the path optimizing process. So an innovative approach for getting the optimized path has been realized with the fusing the BBA and the decision making by the probability distributing. Simulation results indicate that the new method is beneficial in terms of real-time local path planning.

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

Stereo Camera Based Real-Time Local Path-Planning for Mobile Robots

2014 ◽  
pp. 345-354
Author(s):  
Huanqing Yang ◽  
Jianhua Zhang ◽  
Shenyong Chen
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Real Time ◽  
Stereo Camera ◽  
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

Real-time local path planning for mobile robots

2013 ◽  
Author(s):  
Yingchong Ma ◽  
Gang Zheng ◽  
Wilfrid Perruquetti
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Real Time ◽  
Local Path Planning ◽  
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.

A novel method of unmanned surface vehicle autonomous cruise

2016 ◽  
Vol 43 (1) ◽  
pp. 121-130 ◽  
Author(s):  
Shaorong Xie ◽  
Peng Wu ◽  
Hengli Liu ◽  
Peng Yan ◽  
Xiaomao Li ◽  
...  
Keyword(s):  
Path Planning ◽  
Obstacle Detection ◽  
Planning Method ◽  
Dijkstra Algorithm ◽  
Complex Environment ◽  
Unmanned Surface Vehicle ◽  
Content Type ◽  
Global Path Planning ◽  
Local Path Planning ◽  
Local Path

Purpose – This paper aims to propose a new method for combining global path planning with local path planning, to provide an efficient solution for unmanned surface vehicle (USV) path planning despite the changeable environment. Path planning is the key issue of USV navigation. A lot of research works were done on the global and local path planning. However, little attention was given to combining global path planning with local path planning. Design/methodology/approach – A search of shortcut Dijkstra algorithm was used to control the USV in the global path planning. When the USV encounters unknown obstacles, it switches to our modified artificial potential field (APF) algorithm for local path planning. The combinatorial method improves the approach of USV path planning in complex environment. Findings – The method in this paper offers a solution to the issue of path planning in changeable or unchangeable environment, and was confirmed by simulations and experiments. The USV follows the global path based on the search of shortcut Dijkstra algorithm. Both USV achieves obstacle avoidances in the local region based on the modified APF algorithm after obstacle detection. Both the simulation and experimental results demonstrate that the combinatorial path planning method is more efficient in the complex environment. Originality/value – This paper proposes a new path planning method for USV in changeable environment. The proposed method is capable of efficient navigation in changeable and unchangeable environment.

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.

Local path planning in a complex environment for self-driving car

2014 ◽  
Author(s):  
Unghui Lee ◽  
Sangyol Yoon ◽  
HyunChul Shim ◽  
Pascal Vasseur ◽  
Cedric Demonceaux
Keyword(s):  
Path Planning ◽  
Complex Environment ◽  
Local Path Planning ◽  
Local Path
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