Robotic Fish Path Planning Based on an Improved A* Algorithm

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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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.

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Local Path Planning of Autonomous Vehicles Based on A* Algorithm with Equal-Step Sampling

2018 37th Chinese Control Conference (CCC) ◽

10.23919/chicc.2018.8482915 ◽

2018 ◽

Author(s):

WANG Yijing ◽

LIU Zhengxuan ◽

ZUO Zhiqiang ◽

Ll Zheng

Keyword(s):

Path Planning ◽

Autonomous Vehicles ◽

A Algorithm ◽

Local Path Planning ◽

Local Path

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Intelligent Optimization Algorithm-Based Path Planning for a Mobile Robot

Computational Intelligence and Neuroscience ◽

10.1155/2021/8025730 ◽

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.

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

The 4th Annual IEEE International Conference on Cyber Technology in Automation, Control and Intelligent ◽

10.1109/cyber.2014.6917505 ◽

2014 ◽

Cited By ~ 18

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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Laser simulator

International Journal of Advanced Robotic Systems ◽

10.1177/1729881418804726 ◽

2018 ◽

Vol 15 (5) ◽

pp. 172988141880472 ◽

Cited By ~ 2

Author(s):

Mohammed AH Ali ◽

Musa Mailah

Keyword(s):

Path Planning ◽

A Algorithm ◽

Fast Marching ◽

The Real ◽

Robot Trajectory ◽

Local Path Planning ◽

Local Environments ◽

Local Path ◽

Search Graph ◽

Global And Local

A novel technique called laser simulator approach for visibility search graph-based path planning has been developed in this article to determine the optimum collision-free path in unknown environment. With such approach, it is possible to apply constraints on the mobile robot trajectory while navigating in complex terrains such as in factories and road environments, as the first work of its kind. The main advantage of this approach is the ability to be used for both global/local path planning in the presence of constraints and obstacles in unknown environments. The principle of the laser simulator approach with all possibilities and cases that could emerge during path planning is explained to determine the path from initial to destination positions in a two-dimensional map. In addition, a comparative study on the laser simulator approach, A* algorithm, Voronoi diagram with fast marching and PointBug algorithms was performed to show the benefits and drawbacks of the proposed approach. A case study on the utilization of the laser simulator in both global and local path planning has been applied in a road roundabout setting which is regarded as a complex environment for robot path planning. In global path planning, the path is generated within a grid map of the roundabout environment to select the path according to the respective road rules. It is also used to recognize the real roundabout from a sequence of images during local path planning in the real-world system. Results show that the performance of the proposed laser simulator approach in both global and local environments is achieved with low computational and path costs, in which the optimum path from the selected start position to the goal point is tracked accordingly in the presence of the obstacles.

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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.

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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.

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