A study of fuzzy control with ant colony algorithm used in mobile robot for shortest path planning and obstacle avoidance

Yen, Chih-Ta; Cheng, Ming-Feng

doi:10.1007/s00542-016-3192-9

Cited by 66 publications

(30 citation statements)

References 14 publications

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“…In Zeng et al (2016), it unlimited step length of finding optimal path so that the improved ACO could find a shorter path and its convergence was better. In addition, many scholars have combined the ant colony algorithm with other (intelligent) algorithms (He et al, 2016b; Liu et al, 2016; Yen and Cheng, 2016; He and Zhang, 2017) to improve the convergence rate and the smooth of path. In Liu et al (2016), the geometric method was used to optimize path.…”

Section: Introductionmentioning

confidence: 99%

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Mobile Robot Path Planning Based on Ant Colony Algorithm With A* Heuristic Method

et al. 2019

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This paper proposes an improved ant colony algorithm to achieve efficient searching capabilities of path planning in complicated maps for mobile robot. The improved ant colony algorithm uses the characteristics of A * algorithm and MAX-MIN Ant system. Firstly, the grid environment model is constructed. The evaluation function of A * algorithm and the bending suppression operator are introduced to improve the heuristic information of the Ant colony algorithm, which can accelerate the convergence speed and increase the smoothness of the global path. Secondly, the retraction mechanism is introduced to solve the deadlock problem. Then the MAX-MIN ant system is transformed into local diffusion pheromone and only the best solution from iteration trials can be added to pheromone update. And, strengths of the pheromone trails are effectively limited for avoiding premature convergence of search. This gives an effective improvement and high performance to ACO in complex tunnel, trough and baffle maps and gives a better result as compare to traditional versions of ACO. The simulation results show that the improved ant colony algorithm is more effective and faster.

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Section: Introductionmentioning

confidence: 99%

“…Also in Liu et al (2016), the force factor in the artificial potential field method is transformed into local diffusion pheromone to improve the ability of the ant colony algorithm to find the obstacle. In Yen and Cheng (2016), the fuzzy ant colony optimization method was proposed to minimize the iterative learning error.…”

Section: Introductionmentioning

confidence: 99%

Mobile Robot Path Planning Based on Ant Colony Algorithm With A* Heuristic Method

et al. 2019

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“…In Table 6, additional information from previous research related to PSO is shown. Ant colony optimization (ACO) is usually studied under varying network environments, such as grid network and Voronoi diagram [107][108][109][110][111][112][113][114][115][116][117][118][119]. Few papers, likewise, researched GA with ACO and coordinate system [120].…”

Section: Refmentioning

confidence: 99%

A Performance Review of Collision-Free Path Planning Algorithms

Shin

Chae

2020

Electronics

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Path planning for mobile agents is one of the areas that has drawn the attention of researchers’, as evidenced in the large number of papers related to the collision-free path planning (CFPP) algorithm. The purpose of this paper is to review the findings of those CFPP papers and the methodologies used to generate possible solutions for CFPP for mobile agents. This survey shows that the previous CFPP papers can be divided based on four characteristics. The performance of each method primarily used to solve CFPP in previous research is evaluated and compared. Several methods are implemented and tested in same computing environment to compare the performance of generating solution in specified spatial environment with different obstacles or size. The strengths and weakness of each methodology for CFPP are shown through this survey. Ideally, this paper will provide reference for new future research.

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“…Geometric method has been introduced to optimize the global route and also local diffusion of pheromones has obtained from a force factor defined in artificial potential field to enhance the ability of obstacle detection (Liu et al, 2017 ). In Yen and Cheng ( 2018 ), fuzzy logic is combined with ACO to reduce repetitive learning errors. In Long et al ( 2019 ), A* Heuristic characteristics improved ACO optimization performance in various complexity maps.…”

Section: Related Workmentioning

confidence: 99%

Path Planning of Mobile Robot With Improved Ant Colony Algorithm and MDP to Produce Smooth Trajectory in Grid-Based Environment

et al. 2020

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This approach has been derived mainly to improve quality and efficiency of global path planning for a mobile robot with unknown static obstacle avoidance features in grid-based environment. The quality of the global path in terms of smoothness, path consistency and safety can affect the autonomous behavior of a robot. In this paper, the efficiency of Ant Colony Optimization (ACO) algorithm has improved with additional assistance of A * Multi-Directional algorithm. In the first part, A * Multi-directional algorithm starts to search in map and stores the best nodes area between start and destination with optimal heuristic value and that area of nodes has been chosen for path search by ACO to avoid blind search at initial iterations. The path obtained in grid-based environment consist of points in Cartesian coordinates connected through line segments with sharp bends. Therefore, Markov Decision Process (MDP) trajectory evaluation model is introduced with a novel reward policy to filter and reduce the sharpness in global path generated in grid environment. With arc-length parameterization, a curvilinear smooth route has been generated among filtered waypoints and produces consistency and smoothness in the global path. To achieve a comfort drive and safety for robot, lateral and longitudinal control has been utilized to form a set of optimal trajectories along the reference route, as well as, minimizing total cost. The total cost includes curvature, lateral and longitudinal coordinates constraints. Additionally, for collision detection, at every step the set of optimal local trajectories have been checked for any unexpected obstacle. The results have been verified through simulations in MATLAB compared with previous global path planning algorithms to differentiate the efficiency and quality of derived approach in different constraint environments.

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A study of fuzzy control with ant colony algorithm used in mobile robot for shortest path planning and obstacle avoidance

Cited by 66 publications

References 14 publications

Mobile Robot Path Planning Based on Ant Colony Algorithm With A* Heuristic Method

Mobile Robot Path Planning Based on Ant Colony Algorithm With A* Heuristic Method

A Performance Review of Collision-Free Path Planning Algorithms

Path Planning of Mobile Robot With Improved Ant Colony Algorithm and MDP to Produce Smooth Trajectory in Grid-Based Environment

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