Genetic Algorithm Based Approach for Autonomous Mobile Robot Path Planning

An improved PSO algorithm for smooth path planning of mobile robots using continuous high-degree Bezier curve

This research introduces a path planning method based on the geometric A-star algorithm. The whole approach is applied to an Automated Guided Vehicle (AGV) in order to avoid the problems of many nodes, long-distance and large turning angle, and these problems usually exist in the sawtooth and cross paths produced by the traditional A-star algorithm. First, a grid method models a port environment. Second, the nodes in the close-list are filtered by the functions $P\left ({{x,y} }\right)$ and $W\left ({{x,y} }\right)$ and the nodes that do not meet the requirements are removed to avoid the generation of irregular paths. Simultaneously, to enhance the stability of the AGV regarding turning paths, the polyline at the turning path is replaced by a cubic B-spline curve. The path planning experimental results applied to different scenarios and different specifications showed that compared with other seven different algorithms, the geometric A-star algorithm reduces the number of nodes by 10% ~ 40%, while the number of turns is reduced by 25%, the turning angle is reduced by 33.3%, and the total distance is reduced by 25.5%. Overall, the simulation results of the path planning confirmed the effectiveness of the geometric A-star algorithm.

/pdf/geometric-a-star-algorithm-an-improved-a-star-algorithm-for-23pxzol2mr.pdf

Geometric A-Star Algorithm: An Improved A-Star Algorithm for AGV Path Planning in a Port Environment

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.

/pdf/mobile-robot-path-planning-based-on-ant-colony-algorithm-3g8h4bdcis.pdf

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

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

Optimal path planning and execution for mobile robots using genetic algorithm and adaptive fuzzy-logic control

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Optimal path planning and execution for mobile robots using genetic algorithm and adaptive fuzzy-logic control