scholarly journals Automatic Parking Path Planning Based on Ant Colony Optimization and the Grid Method

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Guo Liang Han
Keyword(s):  
Path Planning ◽  
Ant Colony Optimization ◽  
Shortest Path ◽  
Grid Method ◽  
Path Tracking ◽  
Planning Method ◽  
Ant Colony ◽  
Planning Problem ◽  
Automatic Parking ◽  
Vehicle Path

This paper analyzes the path planning problem in the automatic parking process, and studies a path planning method for automatic parking. The grid method and the ant colony optimization are combined to find the shortest path from the parking start point to the end point. The grid method is used to model the parking environment to simulate the actual parking space of automatic parking; then this paper makes some improvements to the basic ant colony optimization, finds the destination by setting the ants’ movement rules in the grid, and finds the shortest path after N iterations; since the optimal path found is a polyline, it will increase the difficulty of controlling vehicle path tracking and affect the accuracy of vehicle path tracking. The bezier curve is used to generate a smooth path suitable for vehicle walking. Finally, through matlab simulation, the obstacles in the environment are simulated, and the parking trajectory is obtained. The results show that the path planning method proposed in this paper is feasible.

scholarly journals Hybrid chaos-based particle swarm optimization-ant colony optimization algorithm with asynchronous pheromone updating strategy for path planning of landfill inspection robots

2019 ◽  
Vol 16 (4) ◽  
pp. 172988141985908 ◽  
Author(s):  
Peng Chen ◽  
Qing Li ◽  
Chao Zhang ◽  
Jiarui Cui ◽  
Hao Zhou
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Ant Colony Optimization ◽  
Particle Swarm ◽  
Ant Colony ◽  
Planning Problem ◽  
Swarm Optimization ◽  
Gas Poisoning ◽  
Inspection Robots ◽  
Elitist Strategy

Robots are coming to help us in different harsh environments such as deep sea or coal mine. Waste landfill is the place like these with casualty risk, gas poisoning, and explosion hazards. It is reasonable to use robots to fulfill tasks like burying operation, transportation, and inspection. In these assignments, one important issue is to obtain appropriate paths for robots especially in some complex applications. In this context, a novel hybrid swarm intelligence algorithm, ant colony optimization enhanced by chaos-based particle swarm optimization, is proposed in this article to deal with the path planning problem for landfill inspection robots in Asahikawa, Japan. In chaos-based particle swarm optimization, Chebyshev chaotic sequence is used to generate the random factors for particle swarm optimization updating formula so as to effectively adjust particle swarm optimization parameters. This improved model is applied to optimize and determine the hyper parameters for ant colony optimization. In addition, an improved pheromone updating strategy which combines the global asynchronous feature and “Elitist Strategy” is employed in ant colony optimization in order to use global information more appropriately. Therefore, the iteration number of ant colony optimization invoked by chaos-based particle swarm optimization can be reduced reasonably so as to decrease the search time effectively. Comparative simulation experiments show that the chaos-based particle swarm optimization-ant colony optimization has a rapid search speed and can obtain solutions with similar qualities.

A Study of Path Planning Algorithm in VANETs

2014 ◽  
Vol 602-605 ◽  
pp. 3503-3507
Author(s):  
Ling Zhang
Keyword(s):  
Path Planning ◽  
Ant Colony Optimization ◽  
Intelligent Transportation System ◽  
Intelligent Transportation ◽  
Transportation System ◽  
Ant Colony ◽  
Guidance System ◽  
Planning Algorithm ◽  
Vehicle Path ◽  
Path Planning Algorithm

Based on GPS and RFID technology, a vehicle path planning guidance system is analyzed in this paper. Specially, the ant colony optimization in the vehicle path planning application of Internet in vehicles environment is proposed, and an improved strategy is put forward to provide an efficient path planning algorithm for the construction of intelligent transportation system. As an alternative of wireless radio and guidance display screen and other primary induction means, the ant colony optimization in this work could supply some significant exploring and thinking for currently construction of the intelligent transportation system.

scholarly journals Grid-Based Mobile Robot Path Planning Using Aging-Based Ant Colony Optimization Algorithm in Static and Dynamic Environments

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1880 ◽  
Author(s):  
Fatin Hassan Ajeil ◽  
Ibraheem Kasim Ibraheem ◽  
Ahmad Taher Azar ◽  
Amjad J. Humaidi
Keyword(s):  
Path Planning ◽  
Mobile Robot ◽  
Ant Colony Optimization ◽  
Optimal Path ◽  
Dynamic Environments ◽  
Ant Colony ◽  
Superior Performance ◽  
Planning Problem ◽  
Planning Algorithms ◽  
Grid Based

Planning an optimal path for a mobile robot is a complicated problem as it allows the mobile robots to navigate autonomously by following the safest and shortest path between starting and goal points. The present work deals with the design of intelligent path planning algorithms for a mobile robot in static and dynamic environments based on swarm intelligence optimization. A modification based on the age of the ant is introduced to standard ant colony optimization, called aging-based ant colony optimization (ABACO). The ABACO was implemented in association with grid-based modeling for the static and dynamic environments to solve the path planning problem. The simulations are run in the MATLAB environment to test the validity of the proposed algorithms. Simulations showed that the proposed path planning algorithms result in superior performance by finding the shortest and the most free-collision path under various static and dynamic scenarios. Furthermore, the superiority of the proposed algorithms was proved through comparisons with other traditional path planning algorithms with different static environments.

scholarly journals Development of Path Planning Algorithm Using Probabilistic Roadmap Based on Ant Colony Optimization

2020 ◽  
Vol 38 (3A) ◽  
pp. 343-351
Author(s):  
Mohammed I. Abdulakareem ◽  
Firas A. Raheem
Keyword(s):  
Path Planning ◽  
Ant Colony Optimization ◽  
Ant Colony ◽  
Planning Problem ◽  
B Spline ◽  
Spline Curve ◽  
Probabilistic Roadmap ◽  
Planning Algorithm ◽  
Feasible Path ◽  
Path Planning Algorithm

n this paper, a unique combination among probabilistic roadmap, ant colony optimization, and third order B-spline curve has been proposed to solve path-planning problem in complex and very complex environments. This proposed method can be divided into three stages. First stage is to construct a random map depending on the environment complexity using probabilistic roadmap algorithm. This could be done by sampling N nodes randomly in complex and very complex static environments, then connecting these nodes together according to some criteria or conditions. The constructed roadmap contains huge number of possible random paths that may connect the start and the goal points together. Second stage includes finding path within the pre-constructed roadmap. Ant colony optimization is selected to find or to search the best path between start and goal points. Finally, the third stage uses B-spline curve to smooth and reduce total length of the found path in the previous stage where path’s length has been reduced by 1% in first environment and by 15% in second environment. The results of the proposed approach ensure feasible path between start and goal points in complex and very complex environment. In addition, the path is guaranteed to be shortest, smooth, continues and safe.

scholarly journals Vehicle Motion Planning for the Visually Challenged People Using Ant Colony Optimization

YMER Digital ◽  
2021 ◽  
Vol 20 (12) ◽  
pp. 583-588
Author(s):  
Dr. S Prasanna ◽  
◽  
Dr. T Indumathi ◽  
Keyword(s):  
Path Planning ◽  
Ant Colony Optimization ◽  
Obstacle Detection ◽  
Ant Colony ◽  
Sensor Technology ◽  
Target Point ◽  
Planning Problem ◽  
Smart Watch ◽  
Starting Point ◽  
Visually Challenged

This paper presents a novel proposal to solve the problem of obstacle detection and path planning for visually challenged people based on simple Ant Colony Optimization Meta-heuristic (SACO-MH). The mission of the path planning problem is to enable the vehicle to move from the starting point to the target point while satisfying certain constraints. Constraint conditions are: not a collision with known or unknown obstacles, away from the obstacle as far as possible, determines the shortest path, shortest time and so on. Obstacle detection is made with the help of sensor technology and it is intimated to the user with the help of a smart watch. A voice based navigation system guides the user

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