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

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.

Download Full-text

A novel method of unmanned surface vehicle autonomous cruise

Industrial Robot the international journal of robotics research and application ◽

10.1108/ir-05-2015-0097 ◽

2016 ◽

Vol 43 (1) ◽

pp. 121-130 ◽

Cited By ~ 7

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.

Download Full-text

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

Abstract and Applied Analysis ◽

10.1155/2013/260578 ◽

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.

Download Full-text

A Comparison of Local Path Planning Techniques of Autonomous Surface Vehicles for Monitoring Applications: The Ypacarai Lake Case-study

Sensors ◽

10.3390/s20051488 ◽

2020 ◽

Vol 20 (5) ◽

pp. 1488

Author(s):

Federico Peralta ◽

Mario Arzamendia ◽

Derlis Gregor ◽

Daniel G. Reina ◽

Sergio Toral

Keyword(s):

Path Planning ◽

Random Trees ◽

Fast Marching Method ◽

Fast Marching ◽

Planning Techniques ◽

Advantages And Disadvantages ◽

Autonomous Surface Vehicle ◽

Local Path Planning ◽

Custom Made ◽

Local Path

Local path planning is important in the development of autonomous vehicles since it allows a vehicle to adapt their movements to dynamic environments, for instance, when obstacles are detected. This work presents an evaluation of the performance of different local path planning techniques for an Autonomous Surface Vehicle, using a custom-made simulator based on the open-source Robotarium framework. The conducted simulations allow to verify, compare and visualize the solutions of the different techniques. The selected techniques for evaluation include A*, Potential Fields (PF), Rapidly-Exploring Random Trees* (RRT*) and variations of the Fast Marching Method (FMM), along with a proposed new method called Updating the Fast Marching Square method (uFMS). The evaluation proposed in this work includes ways to summarize time and safety measures for local path planning techniques. The results in a Lake environment present the advantages and disadvantages of using each technique. The proposed uFMS and A* have been shown to achieve interesting performance in terms of processing time, distance travelled and security levels. Furthermore, the proposed uFMS algorithm is capable of generating smoother routes.

Download Full-text