Sphere sets from point clouds for efficient collision detection in robot motion planning

Author(s):  
Alex Visser ◽  
Zengxi Pan ◽  
Stephen van Duin
Author(s):  
Angel Pasqual del Pobil ◽  
Miguel A. Serna

Abstract A simple and practical model with applications in 3D motion planning is presented. The new model is based on a double spherical hierarchy of detail to represent solid bodies. First, each element making up the robot and the obstacles is approximated by means of a set of exterior spheres which are automatically defined. Second, another set composed of interior spheres is generated. These representations define a hierarchy, since they can be redefined as many times as necessary: starting with two spheres per element, the approximation may be improved until it contains hundreds of spheres. Moreover, they converge to a zero-error representation. The proposed spherical model leads to a simple treatment for the problem of dynamic collision detection, and it is further applied to collision-free path planning for robot manipulators in 3D.


2021 ◽  
pp. 340-348
Author(s):  
Hao Wu ◽  
Ming Lu ◽  
XinJie Zhou ◽  
Philip F. Yuan

AbstractIn practical robotic construction work, such as laying bricks and painting walls, obstructing objects are encountered and motion planning needs to be done to prevent collisions. This paper first introduces the background and results of existing work on motion planning and describes two of the most mainstream methods, the potential field method, and the sampling-based method. How to use the probabilistic route approach for motion planning on a 6-axis robot is presented. An example of a real bricklaying job is presented to show how to obtain point clouds and increase the speed of computation by customizing collision and ignore calculations. Several methods of smoothing paths are presented and the paths are re-detected to ensure the validity of the paths. Finally, the flow of the whole work is presented and some possible directions for future work are suggested. The significance of this paper is to confirm that a relatively fast motion planning can be achieved by an improved algorithmic process in grasshopper.


2021 ◽  
Vol 6 (2) ◽  
pp. 2256-2263
Author(s):  
Hai Zhu ◽  
Francisco Martinez Claramunt ◽  
Bruno Brito ◽  
Javier Alonso-Mora

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