Obstacle Avoidance Path Planning of Manipulator Based on Improved RRT Algorithm

Wen, Yang; Wen, Hao; Zhang, Zhisheng

doi:10.1109/icccr49711.2021.9349398

Cited by 8 publications

(1 citation statement)

References 6 publications

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“…FC-RRT* uses heuristic information to guide the expansion of the tree node, which extends RRT* to deal with the safety requirements and flight constraints of UAVs in a complex 3D environment [14]. RRT and its improved algorithms are widely used in path planning of manipulators, UAVs, unmanned vehicles [13][14][15][16][17][18][19][20][21]. In the mentioned algorithms, RRT and RRT* are improved by guiding the growth direction of random tree, improving utilization efficiency of samples, reducing the sampling space, etc.…”

Section: Introductionmentioning

confidence: 99%

A Path Planning Strategy of Wearable Manipulators with Target Pointing End Effectors

Zou

Guan

et al. 2022

Electronics

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End effectors like firearms, cameras and fire water guns can be classified as pointing end effectors. When installed on wearable manipulators, a new function can be given to the wearer. Different from gripper end effectors (GEEs), target pointing end effectors (TPEEs) have different working tasks, and the requirements for path planning are also different. There is very limited research on wearable manipulators with TPEEs. Meanwhile, manipulator with GEE path planning tends to be mature, but with a relatively low efficiency concerning its algorithm in solving high-dimensional problems. In this paper, a degree of freedom (DOF) allocation scheme and a path planning strategy (unlike manipulator with gripper end effector) were proposed for manipulators with a target pointing end effector in order to reduce the difficulty of path planning. Besides, this paper describes a new algorithm-dimension rapid-exploration random tree (dimension-RRT) to divide the manipulator DOFs into groups and unify DOFs groups by adding a fake time. The dimension-RRT was compared with the rapid-exploration random tree star algorithm (RRT*) in the same simulation environment; when there are 500 random points, the dimension-RRT time consumption is 0.556 of RRT* and the path length is 0.5 of RRT *. To quickly obtain a path that can avoid the human body, dynamic movement primitives (DMPs) were used to simulate typical spatial motion path and obstacle avoidance path efficiently.

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