Mobile robot path planning using Ant Colony Optimization

Rashid, Razif; Perumal, N.; Elamvazuthi, Irraivan; Tageldeen, Momen Kamal; Khan, M.K.A. Ahamed; Parasuraman, S.

doi:10.1109/roma.2016.7847836

Cited by 42 publications

(25 citation statements)

References 8 publications

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“…The first group includes several previous studies that have exploited examples of natural swarm behaviours. The works in [8] and [9] utilized the standard Ant Colony optimization (ACO) to solve path planning problems for complex environments. An improved version of ACO (IACO) has been proposed in [10] to obtain faster convergence speed and to avoid trapping into local minimum.…”

Section: Related Workmentioning

confidence: 99%

Multi-objective path planning of an autonomous mobile robot using hybrid PSO-MFB optimization algorithm

Ajeil

Ibraheem

Sahib

et al. 2020

Applied Soft Computing

163

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The main aim of this paper is to solve a path planning problem for an autonomous mobile robot in static and dynamic environments. The problem is solved by determining the collision-free path that satisfies the chosen criteria for shortest distance and path smoothness. The proposed path planning algorithm mimics the real world by adding the actual size of the mobile robot to that of the obstacles and formulating the problem as a moving point in the free-space. The proposed algorithm consists of three modules. The first module forms an optimized path by conducting a hybridized Particle Swarm Optimization-Modified Frequency Bat (PSO-MFB) algorithm that minimizes distance and follows path smoothness criteria. The second module detects any infeasible points generated by the proposed PSO-MFB Algorithm by a novel Local Search (LS) algorithm integrated with the PSO-MFB algorithm to be converted into feasible solutions. The third module features obstacle detection and avoidance (ODA), which is triggered when the mobile robot detects obstacles within its sensing region, allowing it to avoid collision with obstacles. The simulation results indicate that this method generates an optimal feasible path even in complex dynamic environments and thus overcomes the shortcomings of conventional approaches such as grid methods. Moreover, compared to recent path planning techniques, simulation results show that the proposed PSO-MFB algorithm is highly competitive in terms of path optimality.

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Section: Related Workmentioning

confidence: 99%

Multi-objective path planning of an autonomous mobile robot using hybrid PSO-MFB optimization algorithm

Ajeil

Ibraheem

Sahib

et al. 2020

Applied Soft Computing

163

View full text Add to dashboard Cite

show abstract

“…The traditional ant colony algorithm evaluation function is directly measured by the length of the path [38]- [40]. However, after a lot of experiments, it finds that the path smoothness of the ant colony algorithm is poor, and there are many inflection points and redundant nodes [41], [42].…”

Section: G Path Evaluation Function Reconstructionmentioning

confidence: 99%

Smooth Path Planning of Ackerman Chassis Robot based on Improved ant Colony Algorithm

2020

International Journal of Circuits, Systems and Signal Processing

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In the domain of robotics and autonomous driving, the automatic path planning of vehicle collision-free motion is an essential task on the navigation level. It is found that the traditional path planning algorithm and the ployline path cannot fully meet the driving requirements of Ackerman chassis robot. In order to solve the autonomous navigation problem of Ackerman chassis mobile robot in structured environment, this paper presents a new improved algorithm. The method of configuration space can introduce the robot's own structural size parameters into the algorithm. Through convex polygon detection method, the local U-shaped area in the map is transformed into a closed area. The essence of these two strategies is to preprocess the map. The initial pheromone distribution is no longer globally uniform, but is distributed according to the terrain. The volatilization factor of pheromone is changed from static constant to dynamic one, which is combined with Poisson distribution law. This strategy makes the improved pheromone distribution law not only avoid the randomness and blindness in the initial stage of the algorithm, but also ensure the ant colony's exploration behavior and guiding role in the middle stage of the algorithm. Path smoothing is also a challenging task. This algorithm optimizes the path step by step by improving the evaluation function, removing redundant nodes and 2-turning algorithm. Thus, a collision free smooth path suitable for Ackerman robot is obtained. This paper combines a variety of algorithm improvement strategies, not only improving the performance of ant colony algorithm path exploration, but also planning a smooth curve path suitable rather than polyline for Ackerman mobile robot tracking. The algorithm is coded and simulated by MATLAB, and the feasibility and effectiveness of the algorithm are verified. This will provide an important basis for the subsequent algorithm migration and lay the foundation for the path tracking control of the Ackermann chassis robot.

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“…Step 2. According to the robot's eight directions of motion [17], we may mark the eight directions of the current grid, see Figure 4, and distinguish the narrow aisle according to formulas (3), (4), (5), and (6). Then we may calculate the distance in each direction.…”

Section: The Improved Ant Colony Algorithmmentioning

confidence: 99%

An Improved Ant Colony Algorithm of Robot Path Planning for Obstacle Avoidance

Wang

Zhao

et al. 2019

Journal of Robotics

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The obstacle avoidance in path planning, a hot topic in mobile robot control, has been extensively investigated. The existing ant colony algorithms, however, remain as drawbacks including failing to cope with narrow aisles in working areas, large amount of calculation, etc. To address above technical issues, an improved ant colony algorithm is proposed for path planning. In this paper, a new weighted adjacency matrix is presented to determine the walking direction and the narrow aisles therefore are avoided by redesigning the walking rules. Also, the best ant and the worst ant are introduced for the adjustment of pheromone to facilitate the searching process. The proposed algorithm guarantees that robots are able to find a satisfying path in the presence of narrow aisles. The simulation results show the effectiveness of the proposed algorithm.

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Mobile robot path planning using Ant Colony Optimization

Cited by 42 publications

References 8 publications

Multi-objective path planning of an autonomous mobile robot using hybrid PSO-MFB optimization algorithm

Multi-objective path planning of an autonomous mobile robot using hybrid PSO-MFB optimization algorithm

Smooth Path Planning of Ackerman Chassis Robot based on Improved ant Colony Algorithm

An Improved Ant Colony Algorithm of Robot Path Planning for Obstacle Avoidance

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