Path Planning for CAVs Considering Dynamic Obstacle Avoidance Based on Improved Driving Risk Field and A* Algorithm

2020 ◽  
Author(s):  
Ye Tian ◽  
Huaxin Pei ◽  
Yi Zhang
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
A Algorithm ◽  
Driving Risk ◽  
Dynamic Obstacle Avoidance ◽  
Dynamic Obstacle

scholarly journals An Improved VFF Approach for Robot Path Planning in Unknown and Dynamic Environments

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Jianjun Ni ◽  
Wenbo Wu ◽  
Jinrong Shen ◽  
Xinnan Fan
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Dynamic Environments ◽  
Minimum Problem ◽  
Robot Path Planning ◽  
Virtual Force ◽  
Dynamic Obstacle Avoidance ◽  
Dynamic Obstacle ◽  
Local Minimum Problem ◽  
Robot Path

Robot path planning in unknown and dynamic environments is one of the hot topics in the field of robot control. The virtual force field (VFF) is an efficient path planning method for robot. However, there are some shortcomings of the traditional VFF based methods, such as the local minimum problem and the higher computational complexity, in dealing with the dynamic obstacle avoidance. In this paper, an improved VFF approach is proposed for the real-time robot path planning, where the environment is unknown and changing. An area ratio parameter is introduced into the proposed VFF based approach, where the size of the robot and obstacles are considered. Furthermore, a fuzzy control module is added, to deal with the problem of obstacle avoidance in dynamic environments, by adjusting the rotation angle of the robot. Finally, some simulation experiments are carried out to validate and demonstrate the efficiency of the proposed approach.

scholarly journals A New Hybrid Method in Global Dynamic Path Planning of Mobile Robot

2018 ◽  
Vol 13 (6) ◽  
pp. 1032-1046 ◽  
Author(s):  
Xiaoru Song ◽  
Song Gao ◽  
C.B. Chen ◽  
Kai Cao ◽  
Jiaoru Huang
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Real Time ◽  
Obstacle Avoidance ◽  
A Algorithm ◽  
Fusion Algorithm ◽  
Global Dynamic ◽  
Dynamic Path Planning ◽  
Dynamic Path ◽  
Dynamic Obstacle

Path planning and real-time obstacle avoidance is the key technologies of mobile robot intelligence. But the efficiency of the global path planning is not very high. It is not easy to avoid obstacles in real time. Aiming at these shortcomings it is proposed that a global dynamic path planning method based on improved A* algorithm and dynamic window method. At first the improved A* algorithm is put forward based on the traditional A* algorithm in the paper. Its optimized heuristic search function is designed. They can be eliminated that the redundant path points and unnecessary turning points. Simulation experiment 1 results show that the planned path length is reduced greatly. And the path transition points are less, too. And then it is focused on the global dynamic path planning of fusion improved A* Algorithm and Dynamic Window Method. The evaluation function is constructed taking into account the global optimal path. The real time dynamic path is planning. On the basis of ensuring the optimal global optimization of the planning path, it is improved that the smoothness of the planning path and the local real-time obstacle avoidance ability. The simulation experiments results show that the fusion algorithm is not only the shorter length, but also the smoother path compared the traditional path planning algorithms with the fusion algorithm in the paper. It is more fit to the dynamics of the robot control. And when a dynamic obstacle is added, the new path can be gained. The barrier can be bypass and the robot is to reach the target point. It can be guaranteed the global optimality of the path. Finally the Turtlebot mobile robot was used to experiment. The experimental results show that the global optimality of the proposed path can be guaranteed by the fusion algorithm. And the planned global path is smoother. When the random dynamic obstacle occurs in the experiment, the robot can be real-time dynamic obstacle avoidance. It can re-plan the path. It can bypass the random obstacle to reach the original target point. The outputting control parameters are more conducive to the robot’s automatic control. The fusion method is used for global dynamic path planning of mobile robots in this paper. In summary the experimental results show that the method is good efficiency and real-time performance. It has great reference value for the dynamic path planning application of mobile robot.

Online Heuristically Planning for Relative Optimal Paths Using a Stochastic Algorithm for USVs

2019 ◽  
Vol 73 (2) ◽  
pp. 485-508
Author(s):  
Naifeng Wen ◽  
Rubo Zhang ◽  
Guanqun Liu ◽  
Junwei Wu
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Optimal Path ◽  
Stochastic Algorithm ◽  
Proportional Navigation ◽  
Reference Information ◽  
International Regulations ◽  
Dynamic Obstacle Avoidance ◽  
Dynamic Obstacle ◽  
Local Path

This paper attempts to solve a challenge in online relative optimal path planning of unmanned surface vehicles (USVs) caused by current and wave disturbance in the practical marine environment. The asymptotically optimal rapidly extending random tree (RRT*) method for local path optimisation is improved. Based on that, an online path planning (OPP) scheme is proposed according to the USV's kinematic and dynamic model. The execution efficiency of RRT* is improved by reduction of the sampling space that is used for randomly learning environmental knowledge. A heuristic sampling scheme is proposed based on the proportional navigation guidance (PNG) method that is used to enable the OPP procedure to utilise the reference information of the global path. Meanwhile, PNG is used to guide RRT* in generating feasible paths with a small amount of gentle turns. The dynamic obstacle avoidance problem is also investigated based on the International Regulations for Preventing Collisions at Sea. Case studies demonstrate that the proposed method efficiently plans paths that are relatively easier to execute and lower in fuel expenditure than traditional schemes. The dynamic obstacle avoidance ability of the proposed scheme is also attested.

Dynamic obstacle avoidance path planning

2014 ◽  
Author(s):  
Shun-Feng Su ◽  
Ming-Chang Chen ◽  
Chung-Ying Li ◽  
Wei-Yen Wang ◽  
Wen-June Wang
Keyword(s):  
Path Planning ◽  
Obstacle Avoidance ◽  
Dynamic Obstacle Avoidance ◽  
Dynamic Obstacle
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