Mobile manipulators are used in a variety of fields because of their flexibility and maneuverability.The path planning capability of the mobile manipulator is one of the important indicators to evaluate the performanc...Mobile manipulators are used in a variety of fields because of their flexibility and maneuverability.The path planning capability of the mobile manipulator is one of the important indicators to evaluate the performance of the manipulator,but it is greatly challenged in the face of maps with narrow channel.To address the problem,an improved hierarchical motion planner(IHMP)is proposed,which consists of a two-dimensional(2D)path planner for the mobile base,and a three-dimensional(3D)trajectory planner for the on-board manipulator.Firstly,a hybrid sampling strategy is proposed,which can reduce invalid nodes of the generated probabilistic roadmap.Bridge test is used to locate the narrow channel areas,and a Gaussian sampler is deployed in these areas and the boundaries.Meanwhile,a random sampler is deployed in the rest areas.Trajectory planner for on-board manipulator is to generate a collision-free and safe trajectory in the narrow channel with collaboration of the 2D path planner.The experimental results show that IHMP is effective for mobile manipulator motion planning in complex static environments,especially in narrow channel.展开更多
There are many challenges for robot navigation in densely populated dynamic environments.This paper presents a survey of the path planning methods for robot navigation in dense environments.Particularly,the path plann...There are many challenges for robot navigation in densely populated dynamic environments.This paper presents a survey of the path planning methods for robot navigation in dense environments.Particularly,the path planning in the navigation framework of mobile robots is composed of global path planning and local path planning,with regard to the planning scope and the executability.Within this framework,the recent progress of the path planning methods is presented in the paper,while examining their strengths and weaknesses.Notably,the recent developed Velocity Obstacle method and its variants that serve as the local planner are analyzed comprehensively.Moreover,as a model-free method that is widely used in current robot applications,the reinforcement learning-based path planning algorithms are detailed in this paper.展开更多
文摘Mobile manipulators are used in a variety of fields because of their flexibility and maneuverability.The path planning capability of the mobile manipulator is one of the important indicators to evaluate the performance of the manipulator,but it is greatly challenged in the face of maps with narrow channel.To address the problem,an improved hierarchical motion planner(IHMP)is proposed,which consists of a two-dimensional(2D)path planner for the mobile base,and a three-dimensional(3D)trajectory planner for the on-board manipulator.Firstly,a hybrid sampling strategy is proposed,which can reduce invalid nodes of the generated probabilistic roadmap.Bridge test is used to locate the narrow channel areas,and a Gaussian sampler is deployed in these areas and the boundaries.Meanwhile,a random sampler is deployed in the rest areas.Trajectory planner for on-board manipulator is to generate a collision-free and safe trajectory in the narrow channel with collaboration of the 2D path planner.The experimental results show that IHMP is effective for mobile manipulator motion planning in complex static environments,especially in narrow channel.
文摘There are many challenges for robot navigation in densely populated dynamic environments.This paper presents a survey of the path planning methods for robot navigation in dense environments.Particularly,the path planning in the navigation framework of mobile robots is composed of global path planning and local path planning,with regard to the planning scope and the executability.Within this framework,the recent progress of the path planning methods is presented in the paper,while examining their strengths and weaknesses.Notably,the recent developed Velocity Obstacle method and its variants that serve as the local planner are analyzed comprehensively.Moreover,as a model-free method that is widely used in current robot applications,the reinforcement learning-based path planning algorithms are detailed in this paper.
