A Path Planning Method for Indoor Robots Based on Partial a Global A-Star Algorithm

Wang, Kang‐Le; Dang, Shu-wen; He, Fajiang; Peng, Cheng

doi:10.2991/fmsmt-17.2017.83

Cited by 3 publications

(3 citation statements)

References 3 publications

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“…For the static obstacle environment, the improved algorithm in this paper can avoid obstacles and plan a smooth path. For the complex scenes, the results of experiments (3) and (4) show that the global path planning algorithm based on IBA can plan a reasonable path. The path can avoid obstacles and is relatively smooth, which verifies the feasibility and effectiveness of the improved algorithm in this paper.…”

Section: Right Angle Bend and Narrow Passage Environmentmentioning

confidence: 99%

“…Local path planning 3 refers to planning a collision-free optimal path to a dynamic obstacle or an obstacle that does not appear in the prior map within a limited time. At present, the research on traditional global path planning algorithms with known static environment information is relatively mature, such as A* heuristic search algorithm 4 Visibility Graph, 5 artificial potential field method, 6 etc. A* algorithm and Dijkstra algorithm have the problems of large amount of calculation and long time; artificial potential field rule, when there are obstacles near the target, the robot cannot reach the target, and there is a problem of local optimal solution.…”

Section: Introductionmentioning

confidence: 99%

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A novel path planning method of mobile robots based on an improved bat algorithm

Wang

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part C:

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In order to solve the global path planning problem of mobile robots, an improved bat algorithm based on inertial weight and Levy flight is proposed in this paper. The linear inertial weights are used to prevent the algorithm from converging prematurely and the Levy flight is introduced in the global search stage to change the flight direction of the bat individuals. Furthermore, in the local search stage, the random exploration mechanism in Cauchy Distribution is utilized to enhance the local mining ability of the algorithm and search for the local optimal values. Then, some simulations are provided to verify the superiority of the improved bat algorithm to other optimization algorithms. Finally, the improved bat algorithm is applied in the global path planning, and the environment model and fitness function construction are reasonably established. The results indicate the feasibility and effectiveness of proposed algorithm in solving path planning problems.

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Section: Right Angle Bend and Narrow Passage Environmentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A novel path planning method of mobile robots based on an improved bat algorithm

Wang

et al. 2020

Proceedings of the Institution of Mechanical Engineers, Part C:

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“…A-star (or A * ) search algorithm [4] is one of the most widely used heuristic path-planning algorithms on grid maps. It generates the global optimal paths dynamically and can theoretically guarantee the convergence of the global optimal solution [5]. Such characteristic makes it suitable for dynamically changed maps such as real-time path planning in robotics or RTS games.…”

Section: Introductionmentioning

confidence: 99%

FPGA Implementation of A∗ Algorithm for Real-Time Path Planning

Zhou

Jin

Wang

2020

International Journal of Reconfigurable Computing

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The traditional A∗ algorithm is time-consuming due to a large number of iteration operations to calculate the evaluation function and sort the OPEN list. To achieve real-time path-planning performance, a hardware accelerator’s architecture called A∗ accelerator has been designed and implemented in field programmable gate array (FPGA). The specially designed 8-port cache and OPEN list array are introduced to tackle the calculation bottleneck. The system-on-a-chip (SOC) design is implemented in Xilinx Kintex-7 FPGA to evaluate A∗ accelerator. Experiments show that the hardware accelerator achieves 37–75 times performance enhancement relative to software implementation. It is suitable for real-time path-planning applications.

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PRM Path Smoothening by Circular Arc Fillet Method for Mobile Robot Navigation

KILIÇARSLAN OUACH,

EREN,

ÖZCAN

2023

UMAG

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The problem of motion planning and navigation for mobile robots in complex environments has been a central issue in robotics. Navigating these environments requires sophisticated algorithms that handle obstacles and provide smooth, efficient paths. The Probabilistic Roadmap (PRM) method is a widespread technique in robotics for constructing paths for mobile robot navigation. In this study, we propose a novel path-smoothing method using arc fillets for path planning, building on PRM's foundation in the presence of obstacles. Our method operates in two primary stages to improve path efficiency and quality. The first stage generates the shortest path between the initial and goal states in an obstacle-rich environment using PRM, constructing a straight-line, collision-free route. The second stage smooths corners caused by nodes with arc fillets, ensuring smooth turns and minimizing abrupt changes in direction, resulting in more natural and efficient robot motion. We conducted simulations and tests using various PRM features to evaluate the proposed method. The results indicate that the built route offers a smooth turning motion and quicker, more compact movement while evading obstacles. This study contributes to mobile robot navigation by offering a practical approach to improving pathway quality in challenging environments.

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A Path Planning Method for Indoor Robots Based on Partial a Global A-Star Algorithm

Cited by 3 publications

References 3 publications

A novel path planning method of mobile robots based on an improved bat algorithm

A novel path planning method of mobile robots based on an improved bat algorithm

FPGA Implementation of A∗ Algorithm for Real-Time Path Planning

PRM Path Smoothening by Circular Arc Fillet Method for Mobile Robot Navigation

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