Zhang, Q.; Li, R.; Sun, J.; Wei, L.; Huang, J.; Tan, Y. Dynamic 3D Point-Cloud-Driven Autonomous Hierarchical Path Planning for Quadruped Robots. Biomimetics2024, 9, 259.
Zhang, Q.; Li, R.; Sun, J.; Wei, L.; Huang, J.; Tan, Y. Dynamic 3D Point-Cloud-Driven Autonomous Hierarchical Path Planning for Quadruped Robots. Biomimetics 2024, 9, 259.
Zhang, Q.; Li, R.; Sun, J.; Wei, L.; Huang, J.; Tan, Y. Dynamic 3D Point-Cloud-Driven Autonomous Hierarchical Path Planning for Quadruped Robots. Biomimetics2024, 9, 259.
Zhang, Q.; Li, R.; Sun, J.; Wei, L.; Huang, J.; Tan, Y. Dynamic 3D Point-Cloud-Driven Autonomous Hierarchical Path Planning for Quadruped Robots. Biomimetics 2024, 9, 259.
Abstract
Aiming at effectively generating safe and reliable motion paths for quadruped robots, a hierar-chical path planning approach driven by dynamic 3D point cloud is proposed in this article. The developed path planning model is essentially constituted of two layers: global path planning layer and local path planning layer. At the global path planning layer, a new method is proposed for calculating terrain potential field based on point cloud height segmentation. Variable step size is employed to improve the path smoothness. At the local path planning layer, a real-time prediction method for potential collision area and a strategy for temporary target point selection is developed. Quadruped robot experiments were carried out in an outdoor complex environment. The experi-mental results verified that, for global path planning, the smoothness of the path is improved and the complexity of the passing ground is reduced. The effective step size is increased by a maxi-mum of 13.4 times, and the number of iterations is decreased by up to 1/6, compared with the traditional fixed step size planning algorithm. For local path planning, the path length is short-ened by 20%, more efficient dynamic obstacle avoidance and more stable velocity planning are achieved by using the improved dynamic window approach (DWA).
Keywords
Quadruped robots; 3D point cloud; complex terrain; dynamic obstacles; particle swarm optimization (PSO); artificial potential field (APF); dynamic window approach (DWA)
Subject
Computer Science and Mathematics, Robotics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
