A Path-Planning Algorithm Using Vector Potential Functions in Triangular Regions

Pamosoaji, Anugrah K.; Hong, Keum–Shik

doi:10.1109/tsmca.2012.2221457

Cited by 47 publications

(34 citation statements)

References 39 publications

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“…Path planning is one of the fundamental issues in robotics research to improve the autonomy of mobile robots in complex environments. The problem of path planning, whose aim is to find a collision-free path from an initial position to a goal position, has been extensively studied in the literature [1][2][3][4][5][6][7][8][9]. Many traditional approaches for mobile robot path planning are based on geometry.…”

Section: Introductionmentioning

confidence: 99%

A hierarchical path planning approach based on A ⁎ and least-squares policy iteration for mobile robots

Zuo

Guo

et al. 2015

Neurocomputing

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Section: Introductionmentioning

confidence: 99%

A hierarchical path planning approach based on A ⁎ and least-squares policy iteration for mobile robots

Zuo

Guo

et al. 2015

Neurocomputing

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“…Trajectory planning by the system is needed to navigate the vehicle. The optimal trajectory based on the global map has been widely studied in the field of robot navigation [30][31][32]. The coordinate points of all stalks are mapped in accordance with the world coordinate.…”

Section: Detection Of the Crop Row Linementioning

confidence: 99%

RBF-Based Monocular Vision Navigation for Small Vehicles in Narrow Space below Maize Canopy

et al. 2016

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Abstract:Maize is one of the major food crops in China. Traditionally, field operations are done by manual labor, where the farmers are threatened by the harsh environment and pesticides. On the other hand, it is difficult for large machinery to maneuver in the field due to limited space, particularly in the middle and late growth stage of maize. Unmanned, compact agricultural machines, therefore, are ideal for such field work. This paper describes a method of monocular visual recognition to navigate small vehicles between narrow crop rows. Edge detection and noise elimination were used for image segmentation to extract the stalks in the image. The stalk coordinates define passable boundaries, and a simplified radial basis function (RBF)-based algorithm was adapted for path planning to improve the fault tolerance of stalk coordinate extraction. The average image processing time, including network latency, is 220 ms. The average time consumption for path planning is 30 ms. The fast processing ensures a top speed of 2 m/s for our prototype vehicle. When operating at the normal speed (0.7 m/s), the rate of collision with stalks is under 6.4%. Additional simulations and field tests further proved the feasibility and fault tolerance of our method.

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“…e path planning process seeks to obtain an optimum collision-free path from a starting point to a target point around obstacles according to the particular criteria such as distance traveled, travel time, and incurred cost [1][2][3]. As such, path planning is a subject of intense interest in the mobile robot research community [4][5][6][7][8][9][10][11][12]. Path planning is generally divided into local path planning conducted in an unknown environment and global path planning conducted in a known environment.…”

Section: Introductionmentioning

confidence: 99%

Mobile Robot Path Planning Based on a Generalized Wavefront Algorithm

Zhang

2020

Mathematical Problems in Engineering

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is study develops a generalized wavefront algorithm for conducting mobile robot path planning. e algorithm combines multiple target point sets, multilevel grid costs, logarithmic expansion around obstacles, and subsequent path optimization. e planning performances obtained with the proposed algorithm, the A * algorithm, and the rapidly exploring random tree (RRT) algorithm optimized using a Bézier curve are compared using simulations with different grid map environments comprising different numbers of obstacles with varying shapes. e results demonstrate that the generalized wavefront algorithm generates smooth and safe paths around obstacles that meet the required kinematic conditions associated with the actual maneuverability of mobile robots and significantly reduces the planned path length compared with the results obtained with the A * algorithm and the optimized RRT algorithm with a computation time acceptable for real-time applications. erefore, the generated path is not only smooth and effective but also conforms to actual robot maneuverability in practical applications.

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A Path-Planning Algorithm Using Vector Potential Functions in Triangular Regions

Cited by 47 publications

References 39 publications

A hierarchical path planning approach based on A ⁎ and least-squares policy iteration for mobile robots

A hierarchical path planning approach based on A ⁎ and least-squares policy iteration for mobile robots

RBF-Based Monocular Vision Navigation for Small Vehicles in Narrow Space below Maize Canopy

Mobile Robot Path Planning Based on a Generalized Wavefront Algorithm

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