Dynamic obstacle avoidance path planning for UAV

Jia, Tao; Shaohuan, Han; Wang, Ping; Zhang, Wenyuan; Chang, Yawu

doi:10.1109/icus50048.2020.9274865

Cited by 6 publications

(2 citation statements)

References 11 publications

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“…The second-order Bessel curve is shown in Figure 2, and 1 P , 2 P ,and 3 P are three non-collinear points on the plane. The arbitrary point 2 ( ) B t on the resulting curve is shown in equation (10) ( 1 0 )…”

Section: Bessel Curve-based Path Smoothing Strategymentioning

confidence: 99%

“…To address the numerous shortcomings of RRT in path planning, RRT* [7] and Informed-RRT* [8] are improvements to RRT. Chen J [9] proposed an improved RRT-Connect path planning algorithm, IRRT-Connect, for mobile robots to address the low search efficiency of the sampling-based fast extended random tree RRT-Connect path planning algorithm, and Jia T [10] combined RRT* and DWA to solve the dynamic obstacle avoidance problem in partially unknown environments. Haifang W [11] proposed a locally optimal path planning based on goal offset expansion and Cantmull -Rom spline interpolation of two-way fast exploration random tree star Bi-RRT* path planning algorithm.…”

Section: Introductionmentioning

confidence: 99%

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Research on informed-RRT* algorithm for multi-strategy optimization in mobile robot path planning

Feng,

Xia,

Fan

et al. 2024

Fourth International Conference on Sensors and Information Technology (ICSI 2024)

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Path planning is a key technology to realize the autonomous navigation of mobile robots. The Informed-RRT* algorithm is the current path planning algorithm that solves the high sampling efficiency in complex environments, but it also suffers from long planning times and redundant turns in complex environments. For this reason, a multi-strategy optimization Informed-RRT* algorithm is proposed, the first is to introduce the WOA algorithm during the operation of re-selecting the parent node so that the new node can find the selection of the optimal parent node in the search radius, to improve the efficiency of the search, and the second is to select a suitable Bessel curve to interpolate and optimize the generated paths, to make the generated feasible paths smoother and with fewer redundant turns. In the algorithm validation, the McNamee wheeled robot is modeled using the joint simulation platform of ROS and GAZEBO, and the improved Informed RRT* algorithm is encapsulated into the ROS-based path planning algorithm, and the experimental results show that the proposed algorithm outperforms the existing algorithms in terms of the average planning time, the planning length, and the success rate of the planning, and it provides a new feasible solution for the path planning in the complex environment.

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Section: Bessel Curve-based Path Smoothing Strategymentioning

confidence: 99%